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Biomedical application of terahertz imaging technology: a narrative review

December 2023
Quantitative Imaging in Medicine and Surgery 13(12):8768-8786

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Background and Objective Terahertz (THz) imaging has wide applications in biomedical research due to its properties, such as non-ionizing, non-invasive and distinctive spectral fingerprints. Over the past 6 years, the application of THz imaging in tumor tissue has made encouraging progress. However, due to the strong absorption of THz by water, the large size, high cost, and low sensitivity of THz devices, it is still difficult to be widely used in clinical practice. This paper provides ideas for researchers and promotes the development of THz imaging in clinical research. Methods The literature search was conducted in the Web of Science and PubMed databases using the keywords “Terahertz imaging”, “Breast”, “Brain”, “Skin” and “Cancer”. A total of 94 English language articles from 1 January, 2017 to 30 December, 2022 were reviewed. Key Content and Findings In this review, we briefly introduced the recent advances in THz near-field imaging, single-pixel imaging and real-time imaging, the applications of THz imaging for detecting breast, brain and skin tissues in the last 6 years were reviewed, and the advantages and existing challenges were identified. It is necessary to combine machine learning and metamaterials to develop real-time THz devices with small size, low cost and high sensitivity that can be widely used in clinical practice. More powerful THz detectors can be developed by combining graphene, designing structures and other methods to improve the sensitivity of the devices and obtain more accurate information. Establishing a THz database is one of the important methods to improve the repeatability and accuracy of imaging results. Conclusions THz technology is an effective method for tumor imaging. We believe that with the joint efforts of researchers and clinicians, accurate, real-time, and safe THz imaging will be widely applied in clinical practice in the future.

Available via license: CC BY-NC-ND 4.0

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Review Article

Biomedical application of terahertz imaging technology: a

narrative review

Mengyang Cong1, Wen Li2, Yang Liu2, Jing Bi2, Xiaokun Wang2, Xueqiao Yang2, Zihan Zhang2,

Xiaoxin Zhang2, Ya-Nan Zhao2, Rui Zhao2,3,4, Jianfeng Qiu2,5

1College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an, China; 2School of Radiology, Shandong First Medical

University & Shandong Academy of Medical Sciences, Tai’an, China; 3Department of Nuclear Medicine, The First Afliated Hospital of Shandong

First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China; 4Science and Technology Innovation Center, Shandong First

Medical University & Shandong Academy of Medical Sciences, Jinan, China; 5Center for Medical Engineer Technology Research, Shandong First

Medical University & Shandong Academy of Medical Sciences, Tai’an, China

Contributions: (I) Conception and design: M Cong, R Zhao, J Qiu; (II) Administrative support: J Qiu, R Zhao; (III) Provision of study materials or

patients: All authors; (IV) Collection and assembly of data: W Li, Y Liu, J Bi, X Wang, X Yang, Z Zhang, X Zhang, YN Zhao; (V) Data analysis and

interpretation: M Cong; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Jianfeng Qiu, PhD. School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Tai’an,

China; Center for Medical Engineer Technology Research, Shandong First Medical University & Shandong Academy of Medical Sciences,

Changcheng Road, Tai’an 271016, China. Email: jfqiu100@gmail.com; Rui Zhao, PhD. School of Radiology, Shandong First Medical University &

Shandong Academy of Medical Sciences, Tai’an, China; Science and Technology Innovation Center, Shandong First Medical University & Shandong

Academy of Medical Sciences, Jinan, China; Department of Nuclear Medicine, The First Afliated Hospital of Shandong First Medical University &

Shandong Provincial Qianfoshan Hospital, Jingshi Road, Jinan 250000, China. Email: zhaorui@sdfmu.edu.cn.

Background and Objective: Terahertz (THz) imaging has wide applications in biomedical research

due to its properties, such as non-ionizing, non-invasive and distinctive spectral ngerprints. Over the past

6 years, the application of THz imaging in tumor tissue has made encouraging progress. However, due to

the strong absorption of THz by water, the large size, high cost, and low sensitivity of THz devices, it is still

difcult to be widely used in clinical practice. This paper provides ideas for researchers and promotes the

development of THz imaging in clinical research.

Methods: The literature search was conducted in the Web of Science and PubMed databases using the

keywords “Terahertz imaging”, “Breast”, “Brain”, “Skin” and “Cancer”. A total of 94 English language

articles from 1 January, 2017 to 30 December, 2022 were reviewed.

Key Content and Findings: In this review, we briey introduced the recent advances in THz near-eld

imaging, single-pixel imaging and real-time imaging, the applications of THz imaging for detecting breast,

brain and skin tissues in the last 6 years were reviewed, and the advantages and existing challenges were

identied. It is necessary to combine machine learning and metamaterials to develop real-time THz devices

with small size, low cost and high sensitivity that can be widely used in clinical practice. More powerful THz

detectors can be developed by combining graphene, designing structures and other methods to improve the

sensitivity of the devices and obtain more accurate information. Establishing a THz database is one of the

important methods to improve the repeatability and accuracy of imaging results.

Conclusions: THz technology is an effective method for tumor imaging. We believe that with the joint

efforts of researchers and clinicians, accurate, real-time, and safe THz imaging will be widely applied in

clinical practice in the future.

Keywords: Terahertz (THz) image; breast; brain; skin; cancer

8786

Quantitative Imaging in Medicine and Surgery, Vol 13, No 12 December 2023 8769

Introduction

Terahertz (THz) wave refers to electromagnetic radiation with

a frequency of 0.1–10 THz (a wavelength of 30–3,000 μm),

which is between the microwave and infrared regions.

Compared to other bands, the THz wave has good

penetration properties for many non-polar and non-metallic

substances. At the same time, THz waves are strongly

absorbed by water molecules. THz radiation is non-ionizing

and non-invasive due to its very low photon energy. Many

organic molecules have strong absorption and scattering

characteristics for the THz spectrum due to low-frequency

vibration and rotational transitions of the molecules, so that

each molecule has its own unique “fingerprint spectrum”

(1-3). As a result, the THz technology has been applied

in the eld of food (4,5), agriculture (6,7), biology (8-10),

security inspection (11) and communications (12,13).

THz technologies are mainly divided into THz

spectroscopy and THz imaging technology. THz

spectrometers are mainly divided into three categories:

Fourier transform spectroscopy (FTS), photo-mixing

spectrometer and THz time-domain spectroscopy (THz-

TDS) (14). Although FTS has a poor signal-to-noise ratio

(SNR), it has a wide spectral coverage (100 GHz–5 THz).

Due to the high spectral density and high frequency

resolution, the photomixing spectrometer is a highly

accurate and simple instrument, but its measurement takes

a long time. THz-TDS is the Fourier transform of the

THz time domain signal of the sample into absorption

coefficient, refractive index (RI) and transmittance. This

technology provides a time-resolved spectral analysis, and

effectively suppresses some common sources of noise. The

basic principle of the THz imaging system is as follows:

place the sample on the XYZ platform to change its

position; then collect and process information of the THz

wave (amplitude and/or phase) at different positions of the

sample; finally, construct a point-by-point image. THz

imaging is mainly divided into THz pulse imaging (TPI)

systems and continuous wave (CW) THz imaging systems.

TPI is generated by a femtosecond pulsed laser. Although

it requires a longer scanning time, the intensity and phase

information of the THz waveform can be recorded to

obtain more details of the sample (15). The CW imaging

system is generated by a CW laser. It is of small size and

low cost, but its application range is limited due to the

narrow source spectrum.

There are currently many problems to be solved with

THz technology. The high-power and high-efciency THz

radiation source is the main challenge that researchers need

to overcome. Two researches have shown that spintronic-

based and intense laser-driven THz sources can achieve

higher output power (16,17). It is also crucial to achieve a

THz device with high sensitivity, ultra compactness, and

broadband detection. Quantum sensing technology and

metamaterials are being applied to THz detectors to solve

the above problems (18,19). The problem of high cost and

large volume of THz devices can be solved by solid-state

electronic oscillators.

This review has mainly summarised the development

of THz imaging technology in biomedical applications.

At present, biomedical imaging technology mainly

includes X-ray, computed tomography (CT), magnetic

resonance imaging (MRI) and so on. Compared with

X-ray and CT, THz technology has almost no radiation

hazard to the human body and significantly improves the

sensitivity of tumors differentiation in a non-ionizing

way (20). Compared with MRI, THz technology has a

suitable penetration depth for superficial tumors. And

small handheld devices for intraoperative imaging can be

developed using this technology (8). THz medical imaging

is based on differences of water content in tissue and

structural changes: cancer tissue contains more water due to

tissue edema and increased cellularity, resulting in different

THz absorption; pathological changes in the tissue lead to

changes in the microenvironment and cellular structure,

resulting in differences in imaging (8,21). According to the

basic mechanism of THz medical imaging, this technology

has been applied to breast, brain, skin, liver, colon cancer,

diabetic foot, bone, cervical cancer, etc. (22-29).

The content of this paper is mainly divided into the

following aspects: various THz medical imaging techniques

were introduced in section 2; in sections 3, 4 and 5, we mainly

discussed the research progress of THz imaging technology

in breast, brain and skin tissues in the past six years;

finally, some suggestions for the future development of

THz medical imaging were put forward. We present this

Submitted Apr 17, 2023. Accepted for publication Aug 31, 2023. Published online Sep 27, 2023.

doi: 10.21037/qims-23-526

View this article at: https://dx.doi.org/10.21037/qims-23-526

Cong et al. Review of biological terahertz imaging

8770

article in accordance with the Narrative Review reporting

checklist (available at https://qims.amegroups.com/article/

view/10.21037/qims-23-526/rc).

Methods

A comprehensive search was conducted in the Web of

Science and PubMed databases to review existing research

on the THz imaging technology and cancer. We searched

for the following keywords: “Terahertz imaging”, “Breast”,

“Brain”, “Skin” and “Cancer”. The search time range was

from January 1, 2017 to December 30, 2022. The search

strategy summary is shown in Table 1.

THz imaging technique

THz near-eld imaging

In the THz range, the spatial resolution of the traditional

far-eld spectrum is limited by the diffraction limit, which

is about half the wavelength, leading to imaging limitations.

To improve the spatial resolution, the near-field THz

wave can be generated by using the metal tip and small

aperture (30,31). In addition, placing the sample close to

the detector is the simplest method to achieve the near-eld

THz electric field (32). The near-field scanning imaging

system based on the conventional THz optical waveguide

antenna uses the unbiased antenna and microprobe as the

THz transmitter and detector, respectively. Through the

coupling between the probe tip and the near-field, the

probe can be placed close to the sample surface. Geng et al.

modied the traditional THz near-eld imaging system based

on a photoconductive antenna microprobe (PCAM) (33).

They used a delay line based on a voice coil motor, which

increased the imaging speed by 100 times. The probe-

sample separation range could be controlled within a

few microns to meet the requirements of THz near-field

imaging of biological samples. Li et al. developed a PCAM-

based near-field THz-TDS system (Figure 1A) (34). The

spatial resolution of the system had reached 3 μm, which was

1–2 orders of magnitude higher than the traditional THz

imaging system. The changes in cell morphology during

natural drying were successfully monitored (Figure 1B).

In addition to the technologies mentioned above, the

THz scattering type scanning near-eld optical microscope

(THz s-SNOM) based on the atomic force microscope

(AFM) using metal or metal-coated tips is also a very

powerful near-field imaging technology (37). It had been

demonstrated that this technology can provide nanometer

spatial resolution. Nevertheless, due to the weak THz

scattering properties of biomolecules, THz nanoimaging

of single biomolecules remains unsolved. Yang et al. used

a graphene substrate with high THz reectivity and atomic

flatness to minimize the topographic noise. At the same

time, the free propagation of the THz electric field was

concentrated in the vicinity of the platinum (Pt) probe.

In order to enhance the THz near-field signal and ensure

mechanical stability, the Pt probe with a shaft length of

100 μm was selected for the experiment. This study

ultimately provided the morphology and THz scattering

images of single biomolecules at the nanometer scale (38).

THz single-pixel imaging

The traditional THz-TDS imaging system requires

raster scanning pixel-by-pixel to reconstruct the THz

Table 1 The search strategy summary

Items Specification

Date of search 17/02/2022–01/10/2022

Databases and other sources searched Web of Science and PubMed

Search terms used “Terahertz imaging”, “Terahertz imaging” + “Breast”, “Terahertz imaging” + “Brain”, “Terahertz

imaging” + “Skin” and “Terahertz imaging” + “Cancer”

Timeframe 2017–2022

Inclusion and exclusion criteria Published full-text journals and conference papers in English, excluding reviews and non-

English papers. Papers that utilized terahertz imaging for biological tissue imaging were

selected, otherwise excluded

Selection process The literature selection was done independently by Cong M. Differences were resolved by

consensus

Quantitative Imaging in Medicine and Surgery, Vol 13, No 12 December 2023 8771

time waveform, which greatly increases the acquisition

time. Single-pixel imaging technology uses modulation

technology to encode the spatial information of the

THz wave, and it uses a single-point detector to collect

the reflected light or transmitted light after the spatially

encoded THz wave irradiates the target information.

Finally, the two-dimensional image of the target is

reconstructed by a decoding algorithm (39,40).

The core components of single pixel imaging are spatial

light modulators (SLMs) and reconstruction algorithms.

The compressed sensing (CS) imaging algorithm is one of

the most commonly used reconstruction algorithms (41).

This algorithm essentially consists of compressed sampling

and computational image reconstruction. The compressed

signal is transformed into a high-dimensional signal that is

projected onto a low-dimensional space. A reconstruction

algorithm is used to solve an optimization problem.

Ultimately, the original signal can be reconstructed with

high probability from these few projections. The algorithm

reconstructs high-quality THz images by measuring

fewer pixels (42). At the same time, the combination of

CS and inverse Fresnel diffraction (IFD) algorithms has

reconstructed clear THz single-pixel images (43). THz

imaging is limited by the diffraction limit, resulting in low

resolution and unable to meet the requirements of high-

precision measurement. The IFD algorithm can effectively

eliminate the diffraction effects in THz elds, thus greatly

improving the resolution of THz imaging. She et al.

Figure 1 Schematic diagram of the THz device. (A) Schematic illustration of the experimental setup (34). (B) Changes in cell morphology

during natural drying: (i) optical image; (ii-iv) THz image of cells dried for 1, 3, 5 h (34). (C) Schematic of THz single pixel imaging. A

spatial modulator is made by combining graphene, silicon and gold to improve image quality (35). (D) Schematic of the imaging setup.

Remove all OAPMs and illuminate the sample directly. Images were captured using a lens designed specically for RIGI cameras (36). TPX,

methyl pentene copolymer; THz, terahertz; CW, continuous wave; TX, transmitter; OAPMs, off-axis parabolic mirrors.

Current amplifier

Laser

Microprobe

Cell

Quartz slide

THz wave TPX lens

THz source

Bias voltage Fibre

x100 μm

High

Low

Transmission

Digital micromirror device 808 nm, CW

Lens

Target

Gold

High-resistance silicon

Graphene

Beam splitter Delay line

Camera

lens

Camera

module

Teflon + sample

Lens Protective

housing

Optical

fiber

Bias

≈ Focal distanceObject distance ≥600 mm

Adapter

plate

i ii

iii iv

A B

C D

Cong et al. Review of biological terahertz imaging

8772

proposed a new THz single-pixel imaging technology based

on the spatial Fourier spectrum (Figure 1C) (35). Graphene

was added to the traditional THz modulator to reconstruct

the THz image. CS imaging is difcult to achieve real-time

imaging. In this study, a partially sampled Adama matrix and

a regularized image reconstruction matrix were employed to

reduce the computation time, and achieved 32×32 resolution

at a speed of about 6 frames per second (44). Moreover, to

get insight into hidden objects, the single-pixel imaging

technology could be used to achieve two-dimensional

tomographic THz imaging (45). Recently, Li et al. designed

a SLM based on tunable liquid crystals (LCs) for THz

single-pixel compressive imaging. The proposed frequency

switching method based on LC-SLM and auto-calibrated

compressive sensing (ACCS) algorithm can improve the

frame rate limit, saving almost half of the imaging time (46).

The use of frequency selective SLM for single-pixel

imaging based on LC, micro electromechanical systems

(MEMS), and phase change materials has great potential. It

provides a reliable and low-cost method.

THz real-time imaging

With the development of highly sensitivity and real-time

THz detectors, real-time THz imaging has been widely

used in security and medical fields (47,48). The most

common real-time THz imaging technologies are fast

optical delay lines, photoconductive antenna arrays, and

electro-optical camera sampling (49). Traditional THz

imaging systems used raster scanning, which took a long

time to acquire image data. A broadband THz spectral

imaging system with a highly sensitive THz camera was

used for real-time imaging (50). Usually, the thermal

detector achieved real-time imaging by using the focal

plane array, but it had the disadvantage of low sensitivity. To

overcome this shortcoming, a new optomechanical element

molecular array was invented, which successfully obtained

THz images of metal and biological objects in real time (51).

Perraud et al. achieved real-time three-dimensional imaging

by focusing on the shape algorithm (52). Zolliker et al.

combined a commercial fiber-coupled photoconductive

antenna THz source with a microbolometer camera to

propose a real-time THz imaging system with strong

adaptability (Figure 1D) (36). The real-time images of

samples with micron resolution could be obtained by two-

dimensional electro-optical imaging of THz beams (53).

Meanwhile, the research has shown that dynamic intensity

contour correction is one of the effective ways to achieve

real-time THz electro-optical imaging (54).

THz biomedical imaging

Breast tissue

As one of the “invisible killers” of women, breast cancer

had about 2.26 million cases worldwide by 2020 (55). Breast

conserving surgery (BCS) is a common treatment for early-

stage breast cancer with tumors less than 20 cm in size.

Pathologists need to perform a histopathological analysis of

the excised tissue, which can take about 10 days. However,

15–20% of patients will need to undergo secondary surgery,

which not only affects the patient’s health but also increases

the cost of treatment (56). Therefore, THz imaging, as a

non-invasive and rapid imaging technology, can be applied

to the detection of breast tumors (57).

The study found that the water content of cancer tissue

is higher than that of healthy tissue, and the cancerous area

of the sample has a higher RI and absorption coefficient

due to the unlimited proliferation of cancer cells (58-60).

Therefore, THz imaging can distinguish cancerous tissue

from healthy tissue, which provides a fast and effective

method for assessing the edge of breast cancer tissue.

Bowman et al. performed THz imaging of formalin-xed,

paraffin-embedded breast cancer samples (61). The study

found that the detection accuracy of the THz system can

reach a depth of 1 mm when the tumor is not sliced. This

demonstrated the effectiveness of THz in assessing tumor

margins. Due to the strong absorption of THz waves in

water and the limited penetration depth of THz waves,

the reflection mode has been adopted in most studies.

The reflection mode is sensitive to small phase changes

in the absorption coefficient due to deviations in sliding

and tension thickness and non-ideal tension adhesion.

By comparing reflection and transmission, it was found

that the reection mode has higher resolution and clearer

boundary between different regions (62). Therefore, it

is more suitable for imaging of breast tissue. Most THz

imaging devices are too bulky, which limits the use of THz

imaging devices. Grootendorst et al. used a small and simple

handheld TPI system to scan 46 cases of freshly excised

breast cancer samples in vitro with the frequency range

of 0.1–1.8 THz. The results showed that the accuracy,

sensitivity and specificity of the TPI system for the

diagnosis of benign and malignant breast tissue were 87%,

86% and 96% for support vector machine (SVM) and 88%,

87% and 96% for Bayesian, respectively (Figure 2A) (63).

Quantitative Imaging in Medicine and Surgery, Vol 13, No 12 December 2023 8773

Using THz technology to image fresh breast tissue in the

300–600 GHz frequency range, Cassar et al. demonstrated

a clear contrast between breast cancer tissue and healthy

tissue (59). This result provided a theoretical basis for THz

near-field imaging in the sub-wavelength range. In the

same year, Mavarani et al. investigated different types and

grades of breast cancer tissue using THz reection imaging

at the same frequency (65). The ability to discriminate

between different tissues in this frequency range was

demonstrated. However, the resolution of THz imaging

in the range of 300–600 THz is low. In order to improve

the resolution of the image, it was been proposed that the

Fiber optic

couplers

Oscillating mirrorSilicon lens

Computer

Probe beam

Ti: sapphire laser

100 fs (780 nm)

100 MHz

Optical

dclay

Pump beam

Transmission

THz pulse

THz detector

Delay stage

Reflection

THz pulse

Optical

modulator Galvanometer

Sample

2D THz emitter

GaAs (110)

Invasive ductal

carcinoma Cancer cell

density

High

Low

High

Ductal

carcinoma

in situ

200 μm 200 μm

1.00

0.750

0.500

0.250

0.00

HES image THz image

4K cryostat

Schottky diode

Low nosie

amplifier

Translation

stage (2D)

Lock-in amp

PE lens

PE fiber

PE film

YIG oscillator

module

1.600 mm−1

1.550

1.500

1.450

1.400

Quartz tip

Emitter

Amplitude (Arb. units)

Detector

10 mm

0.03

0.02

0.01

−0.01

−0.02

−0.03

0 2 4 6 8 10 12 14 16 18

Time, ps

Air

Tumor

Fibrous

Adipose

2 mm

(i) (ii) (iii)

(i) (ii)

Figure 2 Imaging of breast tissue at THz. (A) (i) Schematic diagram of a THz handheld probe system; (ii) localization of samples for

terahertz imaging; (iii) pulse spectra from breast tissue, respectively, for tumors, fibrocytes and adipocytes, and air (63); (B) (i) SPoTS

microscope; (ii) comparison of HES and THz images of invasive ductal carcinoma (64); (C) (i) schematic of a system with Schottky diode

detectors; (ii) THz images of breast cancer in a mouse model (22). THz, terahertz; HES, hematoxylin-eosin-saffron; PE, polyethylene; YIG,

yttrium iron garnet; SPoTS, schematic of scanning point terahertz source.

Cong et al. Review of biological terahertz imaging

8774

near-field sensor based on the commercial 0.13 m SiGe-

heterojunction bipolar transistor (HBT) technology could

be used to improve SNR; in addition, the horizontal

resolution could reach 10 m (65,66). This technique can

be used to detect tissue with small changes in dielectric

constant. In other frequency ranges, compact silicon-based

subwavelength THz imagers could be used to accurately

image the edge of breast cancer (67). In order to apply THz

imaging to biological tissue imaging with inhomogeneous

subwavelength scale, Okada et al. developed a schematic of

scanning point terahertz source (SPoTS) microscope with

a spatial resolution of 10 μm (Figure 2B) (64). This study

proved that the system can observe the inhomogeneity

of cells in invasive ductal carcinoma (IDC) through

transmission reection imaging of parafn-embedded breast

cancer tissue, which promotes the application progress of

THz technology in biopsy. The maximum detection value

of the THz imaging system for tissue is 5 mm, which limits

the clinical application. In 2021, Chen et al. imaged breast

cancer in mouse models at 108 GHz using a THz scanning

device constructed by a cryogenic temperature operated

Schottky diode detector (Figure 2C) (22). The results

showed that the detection sensitivity of the detector reached

10–13 W/Hz. The thickness of the detected sample was

increased to 8 cm and the volume was less than 1 mm3.

In recent years, many researches have focused on the

automatic localization and classification of breast cancer

images. The direction and shape of freshly resected tissue

will change when it is examined by histopathology (gold

standard technology) after THz imaging. As a result,

the THz images and histopathological images cannot be

reconciled. Solving the problem of deformation by manual

marking caused a waste of human and material resources, so

grid modication algorithm was adopted (63,68). However,

this method only performs an automatic pixel-by-pixel

comparison between the external contours of the THz

image and the case image. Bowman et al. were the rst to

apply Bayesian Mixture Model in Markov chain Monte

Carlo (MCMC) format to THz image classication, and all

THz images had receiver operating characteristic (ROC)

area greater than 0.8. It was also the first time that THz

imaging was performed on E0771 breast cancer cells (69).

Compared with interpolation-based morphing, a mesh

morphing algorithm based on homography mapping can

capture and correct the complex deformation caused by

paraffin embedding, so that the pathological images can

be automatically and accurately transformed into the same

shape and resolution of the THz image counterpart (70).

To evaluate the mesh morphing algorithm, an unsupervised

Bayesian learning algorithm based on MCMC was used to

classify the samples. The results showed that the area under

the ROC of cancer was more than 85% in fresh tissue

and more than 77% in formalin-xed parafn-embedding

(FFPE) tissue. Therefore, this algorithm can provide

more effective and accurate evaluation of THz imaging.

Bowman et al. found that using the Sobel operator for

edge detection could well dene the tumor boundary, thus

enabling automatic processing for THz imaging (61). To

realize the automation of tissue classication, the research

applied principal component analysis (PCA), artificial

neural network (ANN) (accuracy 98.2%; sensitivity 100%;

specicity 100%) and K-nearest neighbor (KNN) (accuracy

96.4%; sensitivity 95.1%; specificity 100%) algorithm to

THz images, which achieved better tissue classification

accuracy and reduced breast cancer detection time (59,71).

In addition, the energy to Shannon entropy ratio (ESER)

index has been combined with machine learning classiers

for automatic identication of different breast tissues (72).

Compared to KNN and SVM, the accuracy, sensitivity

and accuracy of ESER were 92.85%, 89.66%, and 96.67%

for breast IDC, respectively. Meanwhile, Chavez et al.

demonstrated that the expectation maximization (EM)

algorithm with the low-dimension ordered orthogonal

projection (LOOP) method had an accuracy of 74.69%

in tissue classification (73). In recent years, researchers

have found that a supervised multinomial Bayesian

learning method is more suitable for the detection of

freshly excised breast cancer samples compared with the

existing one-dimensional MCMC and two-dimensional

EM (74). Using the above learning methods, the areas under

the ROC curves for cancer and muscle reached 92.71%

and 86.18% respectively. THz reflection imaging by RI

at the frequency of 550 THz has no significance for the

classication of low-density malignant edges. To overcome

this limitation, Cassar et al. combined morphological

expansion and RI threshold, which reported the high

sensitivity of 80% and specicity of 82% (75).

At present, the main challenges of THz imaging in breast

cancer are as follows: (I) the results need to be compared with

standard medical imaging tools to verify the effectiveness

of THz (76). (II) After THz imaging of freshly excised

breast cancer tissue, the direction and shape of the tissue

changed during histopathological staining. (III) Excessive

fluid around freshly resected tissue causes fluid diffusion

of cancer cells, leading to inaccurate classification. (IV)

In the resection of breast cancer tumor, doctors can use

Quantitative Imaging in Medicine and Surgery, Vol 13, No 12 December 2023 8775

THz imaging technology to quickly detect the edge of the

tumor, which can avoid a second operation to remove the

remaining cancer tissue. However, the dielectric properties

of muscle and cancer overlap, and their classication is not

accurate (63,69,73), as shown in [Figure 2A (iii)]. Therefore,

it is very important to improve the differentiation between

breast cancer tissue and brous tissue. A study has improved

the segmentation between tumor tissue and cancer by

combining Siamese neural network with multiclass SVM (77).

Therefore, machine learning or deep learning can be

used to improve the classification accuracy of fibers and

cancer. At the same time, metamaterials can also be used to

improve the contrast between bers and cancer tissue. (V)

The biological toxicity of THz contrast agent in vivo needs

to be urgently solved. For in vivo THz imaging, contrast

agents can be used to improved image contrast. Researchers

have investigated THz contrast agents such as metallic

nanomaterials, iron oxide nanoparticles, metallic oxide,

carbon-based nanomaterials for imaging in vivo (10,78-80).

However, the biological safety of contrast agents still needs

to be constantly explored.

Brain tissue

Brain tissue can be visualized using hematoxylin and

eosin (H&E) staining with an optical microscope, which

is considered the gold standard. Brain tissue can also

be imaged using positron emission tomography (PET)

imaging and uorescence imaging. However, PET imaging

requires a positron-emitting radionuclide to be injected

into the body, and fluorescence imaging requires a dye to

be injected before surgery. The tissue slices are imaged

directly using THz technology, which does not require

any labelling. Therefore, label-free THz technology can

reduce the burden on the body. Recently, studies have

shown that label-free THz technology can be used to study

brain tissues, such as demyelinating disease (Figure 3A)

(81,87), Alzheimer’s disease (Figure 3B) (82,88), brain injury

(Figure 3C) (83,89,90), glioma, and so on. It provides a new

technical method for early diagnosis and formulation of

minimally invasive treatment plan (91). Glioma is the most

common malignant tumor. Since tumors and normal tissues

cannot be accurately identified by white light microscopy,

THz technology is one of the techniques that have been used

to accurately dene the contour of the tumor boundary (92).

Due to the higher cell density and water content in brain

tumor regions, the RI and absorption coefcient of tumor

tissues are higher than those of normal tissues (93,94).

Overall, THz technology can be used for differentiation.

THz imaging can reveal different regions of brain tissue

compared to other imaging techniques. When this method

was used, the tumor area was not only consistent with the

pathological section of the H&E-stained image, but also

consistent with the tumor area conrmed by green uorescent

protein (GFP) fluorescence image (Figure 3D) (84).

At the same time, the contour of the tumor margin and the

differentiation of different grades of glioma can be clearly

shown (95), thus alleviating the bottleneck of incomplete

resection rate of low-grade glioma, which is underestimated

by protoporphyrin IX (PPIX) fluorescence imaging. The

study found that the tumor area in GFP fluorescence

imaging was wider than that in THz imaging in the

experiment of the living mouse model in vivo, which may

be due to the diffuse uorescence signal of tumor deep in

the tissue (Figure 3E) (84). Up to now, studies have analysed

glioma imaging in vivo or in vitro by using rat and mouse

models, and a few studies have used human glioma samples

for in vitro imaging (10,84,86,96). Wu et al. demonstrated

that under the high frequency intensity (2.52 THz), THz

technology can well discriminate normal tissue from brain

glioma tissue in vivo or in vitro (96).

THz time-domain attenuated total reection (THz-TD-

ATR) spectroscopy mainly consists of the THz-TDS system

and the ATR prism (97). In the experiment, the sample is

placed on the prism base. The incident THz beam incident

undergoes total internal reflection (TIR) at the prism-

sample interface, producing an evanescent wave. Compared

with transmission and reflection THz imaging, the THz-

ATR imaging system can not only reduce the inuence of

water on the results, but also maintain the characteristics

of high sensitivity while ensuring the integrity of sample

information. It was found that the continuous THz-ATR

imaging system can distinguish tumors in freshly resected

brain tissue from normal tissue (98). The effective imaging

area of the ATR prism is a key factor for the THz-ATR

imaging system. In 2020, Wu et al. adopted an isosceles

triangle-shaped silicon prism with the base angle of 49 deg

and realized that the effective imaging area is equivalent to

the imaging area of the prism. Meanwhile, the exit surface

of the secondary reflected beam is different from that of

single reection. Therefore, the imaging results will not be

affected by the secondary reected beam (Figure 3F) (85).

Research has shown that the angle of incidence of THz

wave on the prism bottom is 30 deg, and the system has

high resolution and stability. Glioma tissues could be well

distinguished through the system and were consistent with

Cong et al. Review of biological terahertz imaging

8776

visual and H&E staining images. By optimizing the ATR

prism, the angle of incidence to the bottom of the prism

was chosen to be 30 deg. C6-glioma regions of rat brain

tissue and U87-glioma regions of mouse brain tissue with

different sizes can be well differentiated by THz imaging

and are consistent with H&E staining images. This year,

Wu et al. veried the effect of temperature on the imaging

of fresh isolated mouse glioma tissue by using THz

Normal EAE

Amplitude (a.u.)

1.5

1.0

0.5

0.0

−0.5

−1.0

5 10 15

Time, ps

Normal

EAE

2.0

1.5

1.0

0.5

0.0

Normaliured

amplitude (A.U.)

Normal EAE

PC 2

−1

−2

−3

−3 −2 −1 0 1 2 3

PC 1

Normal-1

Normal-2

Normal-3

Normal-4

Normal-5

EAE-1

EAE-2

EAE-3

EAE-4

EAE-5

Absorption, cm−1

120

100

0.5 1 1.5 2

THz

Normal

1.44

1.50

1.79

1.85

2.1

2.145

Refraction index, n

1.7

1.6

1.5

1.4

1.3

0.5 1 1.5 2

THz

Normal

Visual

images

THz

images

GFP H & E TRI ppIX

TRIWhite light

Refractive index

2.3

2.2

2.4

2.0

1.9

1.8

1.7

1.6

1.5

0.8 1.2 1.6 2.0 2.4 2.8

Trequency (THz)

Normal

Tumor

***

0.5

0.4

0.3

0.2

0.1

0.0 0.8 1.2 1.6 2.0 2.4 2.8

Trequency (THz)

Difference

Reflection

FIRL 100

Gold-

coated

mirror

Beam

splitter

Chopper

Golay

celI

Motor satges

Objective

table

Sample

Prism Golay

cell

Off-axis

parabolic

mirror

−20 ℃−10 ℃0 ℃20 ℃35 ℃

Rave

0.6

0.5

0.4

0.3

0.2

−20 −10 0 10 20 30 40

Temperature T, ℃

Tumor tissue

Normal tissue

Water

(i) (ii)

(iii) (iv)

(i) (ii)

(iii) (iv)

(i) (ii)

(i) (ii) (iii) (iii) (iv)(i) (ii)

(i) (ii) (i) (ii)

2 mm

A B

C D

E F

G H

Figure 3 THz imaging of brain tissue. (A) (i) White-eld images of parafn-embedded brain coronal sections from normal and EAE monkeys.

“1-5” are the randomly measured ve comparable regions of interest. (ii) THz spectra of normal and EAE tissues (81). (B) THz absorption and

reection spectra of AD and normal brain tissue placed on a quartz substrate plate (82). (C) (i,ii) represent visual images of fresh and parafn-

embedded brain tissues, respectively; the three points of the terahertz spectroscopy experiment are shown in red circles. (iii,iv) correspond to

its THZ image (83). (D) Images from different imaging modalities (84); (E) imaging of brain tissue from living mice (84); (F) (i) Diagram of

experimental equipment; numbers 1–3 in the gure are the off-axis parabolic reector. THz-ATR was used to distinguish brain tumor tissues

from fresh rats: white light (ii), THz-ATR (iii), H&E (iv) the tumor regions marked by dashed lines. (85); (G) (i) effect of temperature on THZ

imaging images. Tumor and normal regions, as the marked with dotted boxes 1 and 2. (ii) Reection spectra of different tissues at different

temperatures (86); (H) (i) photo of the freshly-excised tissues; “V” and “VI” are necrosis zone and hemorrhage zone, respectively. (ii) THz

microscopy of the freshly-excised tissues (23). ***, P<0.001. Rave: the averaged reectivity. EAE, experimental autoimmune encephalomyelitis;

AD, Alzheimer’s disease; THz, terahertz; GFP, green uorescent protein; H&E, hematoxylin and eosin; TRI, terahertz reectometry imaging;

TPI, THz pulse imaging; ppIX, protoporphyrin IX; ATR, attenuated total reection.

Quantitative Imaging in Medicine and Surgery, Vol 13, No 12 December 2023 8777

ATR imaging system at the frequency of 0.4–2.53 THz

(Figure 3G) (86). The results showed that the average

reflectance of normal tissue increased with increasing

temperature, while the reectance of tumor area showed a

decreasing trend. In addition, the average RI and absorption

of normal tissue at 20 and −10 ℃ were both smaller than

those of tumor tissue. Therefore, it is necessary to select

a suitable temperature for THz imaging. The THz near-

field imaging system based on the PCAM successfully

distinguished the corpus callosum and brain regions of

mouse brain tissue (33). This research laid the foundation

for the subsequent THz near-field microscope. Solid

immersion microscopy is an imaging technique that can

overcome the Abbe diffraction limit. This method will

improve the resolution of THz imaging, making the image

clearer. Imaging of brain tissue with a high-resolution THz

solid-state immersion microscope revealed mesoscale spatial

fluctuations in THz optical properties due to structural

heterogeneity of intact tissue and tumor tissue. The observed

THz microscopic images showed heterogeneity of brain

tissue in the THz wavelength range (Figure 3H) (23). At the

same time, the intensity and phase of reected light through

the THz solid immersion microscope were explained using

the solid immersion lens reflectivity model. Thus, the RI

distribution of fresh rat glioma samples could be reconstructed

with sub-wavelength spatial resolution, demonstrating the

application potential of the new silicon microscope (99).

In order to better segment tumor tissue and normal

tissue, the following two methods can be applied: (I)

metamaterials (metasurfaces) were used to enhance the

interaction between THz waves and tissue samples, so the

contrast of images of normal and tumor tissue is improved

(Figure 4A,4B) (88,100); (II) machine learning is used to

process the THZ image. It mainly includes the following

4.sealing

3.Cover glass

2.Mounting medium

1.Brain

0.17

0.16

0.15

0.14

0.13

−5 0 5

x, mm

2.4ΔRΔR

Nanoslot

Si (−0.015)

0.18

0.17

0.16

0.15

0.14

0.13

−5 0 5

x, mm

4ΔRΔR

Nanoslot

Si (−0.01)

PDMS sample

Raster scan

Sensing chip

THz reflection

inc

−2

−4

−6

PC2

−6 −4 −2 0 2 4 6

PC1

Tumor

Normal

Border

0.8

0.6

0.4

0.2

Probability TumorNormal

5 mm

5-color image Greyed image

Original image

• Binarization

• Extracting the excircle

• Calculating rotation angle

Rotating the greyed image

Rotation rectification

Rotation

angle θ

Excircle

Binarization of the rotated image Selecting the ROI

(white area)

The selection of ROI

(iii)

(iv)

(i) (ii)

(vi)

(i) (ii)

(iii)

A B

C D

Resize the RO1

Standardization of ROI

Figure 4 Methods to improve the sensitivity of imaging. (A) (i) Experimental map of metamaterials; THz reflection images using (ii) a bare

Si substrate and (iii) a nano-slot chip; reflection values from images (ii) and (iii) along the dashed lines (iv) a and (vi) b (88). The reflectance

spectroscopy experiment is performed with the asterisk. (B) (i) Experimental procedure for metamaterial imaging; THz reection images using

(ii) a bare Si substrate and (iii) metamaterials (100). (C) Tumor boundaries obtained by PCA algorithm (93); (D) preprocessing of THz images

consisting of rotation rectication (89). PDMS, polydimethylsiloxane; THz, terahertz; ROI, region of interest; PCA, principal component analysis.

Cong et al. Review of biological terahertz imaging

8778

four steps: image preprocessing; region of interest (ROI)

segmentation; feature extraction; pattern classification. In

most cases, poor resolution and contrast, as well as noise

from devices and the environment, can reduce the quality

of THz images and obscure important details needed for

accurate segmentation. Therefore, the segmentation of

THz images is very important. ROI segmentation of THz

images is mainly achieved by image pre-processing through

denoising, ROI segmentation, ROI modication and ROI

display in the original image. Block matching 3D denoising,

fuzzy c-means clustering, morphological operation and Canny

edge detection were combined to accurately segment glioma

tissues from normal tissues and complex background (101).

Using the above method, the accuracy, sensitivity, and

specicity of ROI segmentation reached 95.6%, 84.5% and

97.7%, respectively. PCA was used for statistical analysis of

THz images to better distinguish normal tissues from tumor

tissues (Figure 4C) (93). The characteristics of THz images

were extracted by combining the spatial transmittance

distribution and the normalized gray histogram. Different

degrees of traumatic brain injury (TBI) were classified

and identified using random forest (RF). The highest

classification accuracy was 87.5% (Figure 4D) (89). For

the detection of mild TBI, RF has a sensitivity of 88.9%.

Therefore, it has a lower missed diagnosis rate.

Although normal tissue and tumor tissue can be

distinguished by THz imaging technology at present,

edematous tissue and tumor tissue cannot be distinguished (95).

This leads to unnecessary resection of the patient during

surgery. Developments in metamaterials and detector

sensitivity are continuously improving image contrast. This

makes it easier and more convenient to distinguish between

benign and malignant tumors.

Skin tissue

Skin is a exible outer layer of tissue that covers the body

and performs essential functions that have a major impact

on health. Many studies on skin tissue have been carried out

in the THz range.

In recent years, the advantages of THz radiation in

the detection and treatment of dermatauxe and scars have

become increasingly prominent. THz imaging of skin tissue

is based on the interaction of THz radiation with tissue

water, other low-polarity biomolecules, isolated cells and

various structural components of the tissue (24). Studies have

shown that the RI of hyperplastic scars is signicantly higher

than that of normal skin using THz imaging (102-104).

Fan et al. found that THz imaging has great potential for

monitoring the wound healing process in vivo by observing

the 6-month scar recovery process. It could also differentiate

between hypertrophic and normal scars (Figure 5A) (104).

The RI of hypertrophic scars is significantly higher than

that of normal skin, whereas the RI of normal scars is the

opposite. This study demonstrated the potential of THz as

an adjunctive therapy for scars. At the same time, it can also

be used to study the healing of skin scars by detecting the

water content and its spatial distribution in the skin (108).

At present, three- and four-point classication methods are

commonly used for the depth of burns, namely first-degree,

superficial second-degree, deep second-degree and third-

degree burns. However, this method is mainly diagnosed by

doctors according to clinical manifestations, and the accuracy

rate is only between 40% and 80%. In addition, the diagnostic

techniques used for burn depth mainly include fluorescence

detection technology (109), laser Doppler imaging (110,111),

polarization-sensitive optical coherence tomography (112),

near-infrared spectral imaging technology (113), and so

on. In contrast, THz can be used to assess burn wounds

in vitro and in vivo using differences in water content, which

is non-ionizing and can be imaged without touching the

patient (114). THz imaging has great potential to become a

prominent diagnostic technique for burn wound evaluation

with high sensitivity and high resolution (115).

Furthermore, skin cancer is usually divided into two main

types: non-melanoma skin cancer (NMSC) and malignant

melanoma (MM) skin cancer (116). In particular, MM is the

most threatening. Although most patients with NMSC can

be cured by surgery, the incidence of NMSC is about ten

times that of MM (117). The gold standard for removing

skin cancer is Mohs micrographic surgery, but it is very

time-consuming and costly. THz imaging technology has

been shown to have great potential in the diagnosis of skin

tissue and related cancers (118). Azizi et al. improved the

resolution of conventional sensors through photonic band

gap (PBG) and a new THz sensor for early detection of skin

cancer was developed (119). Melanoma has been reported to

be one of the most dangerous skin cancers (24), which has a

higher density, higher water content and lower fat content

than normal skin. Studies have shown that melanoma has a

higher RI and absorption coefcient than normal skin using

THz imaging (Figure 5B) (105,120,121). Therefore, THz

imaging has great potential for early non-invasive diagnosis

of MM. The artifacts refer to structures similar to the

sample contour caused by frequency-dependent diffraction

at the sample edges in THz images. From the THz

Quantitative Imaging in Medicine and Surgery, Vol 13, No 12 December 2023 8779

Quartz window

Emitte

Detector

Optic fibre

Optic fibre Moved by

x-y stage

5 mm

−2

−4

−4 −2 0 2 4

Infiltration

Cancer

Fat

Substrate

Sample

holderBeam

splitter

1 d motion control

THz

source

Off-axis reflector

Detection

system

dB scale

−17.0

−18.0

−20.0

−22.0

−23.1

17000

16800

16600

16400

16200

16000

5000050200 50400 50600 50800 51000 51200

Thickness, μm

μm

16000

15800

15600

15400

15200

15000

50000

50200

50400

50600

50800

51000

51200

Thickness, μm

μm

51400

(i) (ii) (iii)

(i) (ii)

(iii)

(i) (ii) (iii)

Figure 5 THz imaging of skin tissue. (A) (i) Experiment setup; THz images of hyperplastic (ii) and normal (iii) scars (104). The scar tissue

is marked with a black circle. (B) THz imaging of malignant melanoma tissue sections (105). (C) (i) Schematic diagram of the device; slices

of the 3D image across the thickness of a healthy skin sample (ii) and basal cell carcinoma skin sample (iii) (106). (D) (i) The cross-polarized

THz reectance image; the location of the tumor is indicated by the arrows; (ii) the cross-polarized optical image; (iii) H&E image (107).

Scale bar: 10 mm. THz, terahertz; H&E, hematoxylin and eosin.

intensity image, it was found that the THz power at the

edge of the sample was signicantly lower than that inside.

There is a signicant power loss at the edges. Both of the

above phenomena can affect the accuracy of the THz image

edge. Recently, Yang et al. combined Fresnel Kirchhoff

diffraction theory with optical aberration to reasonably

explain the reason for artifact and large power loss at the

THz image edge of melanoma (122). Furthermore, NMSC

is the most common cancer in the world, of which basal cell

carcinoma is one of the most common skin malignancies.

Studies have found that NMSC has higher water content,

resulting in lower transmittance than normal skin (123,124).

In three-dimensional THz images, normal skin was found

to have regular cell patterns, whereas basal cell carcinoma

lacked normal cell patterns (Figure 5C) (106). This can be

used as a basis for early diagnosis. In 2014, Joseph et al.

combined THz imaging with optical imaging for the first

time (Figure 5D) (107). The results showed that the image

could not only accurately display the morphological features

of NMSC, but also accurately identify its edge position.

Water has a strong absorption of THz waves, so the

effective penetration of THz is only 0.2–0.3 mm. In the

0.1–2 THz range, 90% of the THz radiation can be retained

in the ice for 1 mm, making it possible to image frozen tissue

with a thickness of 5 mm. The boundary between frozen and

non-frozen tissues shows strong reflection, proving that

Cong et al. Review of biological terahertz imaging

8780

THz skin-freezing technology has great potential in skin

diagnosis and other applications (125). In addition, THz

imaging technology can also show the distribution and

penetration of local drugs and detect hydration in the skin

(48,126,127). Nevertheless, there are still some defects in

the research of skin diseases by THz technology. The most

important problem is the lack of penetration depth. Skin

cancer can occur in different parts of the human body. In

order to achieve real-time imaging of skin cancer, it is also

necessary to accelerate the development of rapid movement

of imaging systems and complex geometric imaging for

different positions in future research (43).

Conclusions

In conclusion, THz imaging technology is developing

continuously in biomedicine. Due to the strong absorption

effect of water on THz waves, most of the current studies used

frozen or parafn embedded tissue sections and fresh tissue for

imaging, which can cause slight damage to the human body. In

future research, we should constantly overcome the problem

of water absorption and achieve in vivo monitoring as soon as

possible. At the same time, we need to develop THz systems

with better cost effectiveness and detection accuracy. Firstly,

THz technology is integrated with emerging technologies

such as artificial intelligence and cloud computing, to

promote the intelligence and networking of THz technology.

THz imaging can use machine learning to realize real-time

intelligent identification and detection of objects. Secondly,

the repeatability and accuracy of the results can be improved

by formulating a standard imaging operation system and

establishing a THz database. Finally, metamaterials are applied

to improve the interaction between the THZ wave and the

target sample as well as the sensitivity of the THZ detector,

ultimately achieving better detection accuracy.

THz imaging has been shown to be able to differentiate

between benign and malignant tissues, but the treatment

recommendations given by clinicians vary widely depending

on the stages and type of tumor. Therefore, the sensitivity

of the THz imaging system should be continuously

improved for the evaluate of the stage and type of tumor (8).

Slice thickness, storage conditions, and ice content should

be fully considered when establishing the THz database for

clinical testing. To improve the sensitivity of THz imaging,

THz metamaterials or nanoparticle contrast agents can be

used to enhance THz reection and improve the contrast

of images. In addition, materials such as graphene can also

be used to improve THz emitters and detectors for better

detection performance. At present, the THz endoscope

prototype can accurately distinguish tumor tissue, but

compact transceivers are still needed for THz technology

to be applied in the clinic. In addition, the development of

portable and low-cost THz devices is highly benecial for

their application in different clinical situations. At the same

time, in order to reduce the effect on detection accuracy

caused by the loss of water absorption, microfluidics

devices, nanouidic devices, and THz-ATR systems can be

used.

Acknowledgments

Funding: This study was financially supported by the

Taishan Scholars Program of Shandong Province (No.

TS201712065), Academic Promotion Program of Shandong

First Medical University (No. 2019QL009), Science and

Technology funding from Jinan (No. 2020GXRC018),

Natural Science Foundation of Shandong Province (No.

ZR2022QA034), and Talent Introduction Project of

Shandong First Medical University (No. YS23-0000041).

Footnote

Reporting Checklist: The authors have completed the

Narrative Review reporting checklist. Available at https://

qims.amegroups.com/article/view/10.21037/qims-23-526/rc

Conicts of Interest: All authors have completed the ICMJE

uniform disclosure form (available at https://qims.

amegroups.com/article/view/10.21037/qims-23-526/coif).

The authors have no conicts of interest to declare.

Ethical Statement: The authors are accountable for all

aspects of the work in ensuring that questions related

to the accuracy or integrity of any part of the work are

appropriately investigated and resolved.

Open Access Statement: This is an Open Access article

distributed in accordance with the Creative Commons

Attribution-NonCommercial-NoDerivs 4.0 International

License (CC BY-NC-ND 4.0), which permits the non-

commercial replication and distribution of the article with

the strict proviso that no changes or edits are made and

the original work is properly cited (including links to both

the formal publication through the relevant DOI and the

license). See: https://creativecommons.org/licenses/by-nc-

nd/4.0/.

Quantitative Imaging in Medicine and Surgery, Vol 13, No 12 December 2023 8781

References

1. Rønne C, Keiding SR. Low frequency spectroscopy of

liquid water using THz-time domain spectroscopy. J Mol

Liq 2002;101:199-218.

2. Berry E, Walker GC, Fitzgerald AJ, Zinov'ev NN,

Chamberlain M, Smye SW, Miles RE, Smith MA. Do

in vivo terahertz imaging systems comply with safety

guidelines? J Laser Appl 2003;15:192-8.

3. Burford NM, El-Shenawee MO. Review of terahertz

photoconductive antenna technology. Optical Engineering

2017;56:010901.

4. Afsah-Hejri L, Hajeb P, Ara P, Ehsani RJ. A

Comprehensive Review on Food Applications of Terahertz

Spectroscopy and Imaging. Compr Rev Food Sci Food Saf

2019;18:1563-621.

5. Feng CH, Otani C. Terahertz spectroscopy technology

as an innovative technique for food: Current state-of-

the-Art research advances. Crit Rev Food Sci Nutr

2021;61:2523-43.

6. Afsah-Hejri L, Akbari E, Toudeshki A, Homayouni T,

Alizadeh A, Ehsani R. Terahertz spectroscopy and imaging:

A review on agricultural applications. Comput Electron

Agric 2020;177:105628.

7. Ge H, Lv M, Lu X, Jiang Y, Wu G, Li G, Li L, Li Z,

Zhang Y. Applications of THz Spectral Imaging in the

Detection of Agricultural Products. Photonics 2021;8:518.

8. Yang X, Zhao X, Yang K, Liu Y, Liu Y, Fu W, Luo Y.

Biomedical Applications of Terahertz Spectroscopy and

Imaging. Trends Biotechnol 2016;34:810-24.

9. Wan M, Healy JJ, Sheridan JT. Terahertz phase imaging

and biomedical applications. Optics & Laser Technology

2020;122:105859.

10. Yan Z, Zhu LG, Meng K, Huang W, Shi Q. THz medical

imaging: from in vitro to in vivo. Trends Biotechnol

2022;40:816-30.

11. Li T, Zhang L, HE JA, Zhang SX, Gu DY. Terahertz

Time-Domain Spectroscopy for Identication of

Hazardous Substances in Mail. Spectroscopy and spectral

analysis 2019;39:3724-30.

12. Hirata A, Yaita M. Ultrafast Terahertz Wireless

Communications Technologies. IEEE Trans Terahertz Sci

Technol 2015;5:1128-32.

13. Feng W, Wei ST, Cao JC. 6G technology development

vision and terahertz communication. Acta Physica Sinica

2021;70:244303.

14. Yu L, Hao L, Meiqiong T, Jiaoqi H, Wei L, Jinying D,

Xueping C, Weiling F, Yang Z. The medical application

of terahertz technology in non-invasive detection of

cells and tissues: opportunities and challenges. RSC Adv

2019;9:9354-63.

15. Mcclatchey K, Reiten MT, Cheville RA. Time resolved

synthetic aperture terahertz impulse imaging. Appl Phys

Lett 2001;79:4485-7.

16. Wang M, Zhang Y, Guo L, Lv M, Wang P, Wang

X. Spintronics Based Terahertz Sources. Crystals

2022;12:1661.

17. Fülöp JA, Tzortzakis S, Kampfrath T. Laser-Driven

Strong-Field Terahertz Sources. Adv Optical Mater

2020;8:1900681.

18. Vitiello MS, De Natale P. Terahertz Quantum Cascade

Lasers as Enabling Quantum Technology. Adv Quantum

Technol 2022;5:2100082.

19. Tantiwanichapan K, Durmaz H. Improvement of Response

Time and Heat-Transfer Capacity of Metamaterial

Absorber for Terahertz Detector Applications. IEEE

Sensors Journal 2023;23:4700-6.

20. Jacoby M. Biomedical terahertz imaging advances in far-

infrared spectroscopy could aid cancer diagnosis. Chem

Eng News 2015;93:10-4.

21. Chen H, Chen TH, Tseng TF, Lu JT, Kuo CC, Fu SC,

Lee WJ, Tsai YF, Huang YY, Chuang EY, Hwang YJ, Sun

CK. High-sensitivity in vivo THz transmission imaging

of early human breast cancer in a subcutaneous xenograft

mouse model. Opt Express 2011;19:21552-62.

22. Chen H, Han J, Wang D, Zhang Y, Li X, Chen X. In

vivo Estimation of Breast Cancer Tissue Volume in

Subcutaneous Xenotransplantation Mouse Models by

Using a High-Sensitivity Fiber-Based Terahertz Scanning

Imaging System. Front Genet 2021;12:700086.

23. Kucheryavenko AS, Chernomyrdin NV, Gavdush AA,

Alekseeva AI, Nikitin PV, Dolganova IN, Karalkin PA,

Khalansky AS, Spektor IE, Skorobogatiy M, Tuchin

VV, Zaytsev KI. Terahertz dielectric spectroscopy and

solid immersion microscopy of ex vivo glioma model

101.8: brain tissue heterogeneity. Biomed Opt Express

2021;12:5272-89.

24. Nikitkina AI, Bikmulina PY, Gafarova ER, Kosheleva

NV, Efremov YM, Bezrukov EA, Butnaru DV, Dolganova

IN, Chernomyrdin NV, Cherkasova OP, Gavdush AA,

Timashev PS. Terahertz radiation and the skin: a review. J

Biomed Opt 2021;26:043005.

25. Rong L, Latychevskaia T, Chen C, Wang D, Yu Z, Zhou X,

Li Z, Huang H, Wang Y, Zhou Z. Terahertz in-line digital

holography of human hepatocellular carcinoma tissue. Sci

Rep 2015;5:8445.

Cong et al. Review of biological terahertz imaging

8782

26. Vafapour Z, Keshavarz A, Ghahraloud H. The potential

of terahertz sensing for cancer diagnosis. Heliyon

2020;6:e05623.

27. Hernandez-Cardoso GG, Rojas-Landeros SC, Alfaro-

Gomez M, Hernandez-Serrano AI, Salas-Gutierrez I,

Lemus-Bedolla E, Castillo-Guzman AR, Lopez-Lemus

HL, Castro-Camus E. Terahertz imaging for early

screening of diabetic foot syndrome: A proof of concept.

Sci Rep 2017;7:42124.

28. Freer S, Sui C, Hanham SM, Grover LM, Navarro-

Cía M. Hybrid reection retrieval method for terahertz

dielectric imaging of human bone. Biomed Opt Express

2021;12:4807-20.

29. Shi W, Wang Y, Hou L, Ma C, Yang L, Dong C, Wang

Z, Wang H, Guo J, Xu S, Li J. Detection of living

cervical cancer cells by transient terahertz spectroscopy. J

Biophotonics 2021;14:e202000237.

30. Federici JF, Mitrofanov O, Lee M, Hsu JW, Brener I,

Harel R, Wynn JD, Pfeiffer LN, West KW. Terahertz

near-eld imaging. Phys Med Biol 2002;47:3727-34.

31. van der Valk NCJ, Planken PCM. Electro-optic detection

of subwavelength terahertz spot sizes in the near eld of a

metal tip. Appl Phys Lett 2002;81:1558-60.

32. Okada K, Serita K, Zang Z, Murakami H, Kawayama I,

Cassar Q, Macgrogan G, Guillet JP, Mounaix P, Tonouchi

M. Scanning laser terahertz near-eld reection imaging

system. Appl Phys Express 2019;12:122005.

33. Geng G, Dai G, Li D, Zhou S, Li Z, Yang Z, Xu Y, Han

J, Chang T, Cui HL, Wang H. Imaging brain tissue slices

with terahertz near-eld microscopy. Biotechnol Prog

2019;35:e2741.

34. Li Z, Yan S, Zang Z, Geng G, Yang Z, Li J, Wang L,

Yao C, Cui HL, Chang C, Wang H. Single cell imaging

with near-eld terahertz scanning microscopy. Cell Prolif

2020;53:e12788.

35. She R, Liu W, Lu Y, Zhou Z, Li G. Fourier single-

pixel imaging in the terahertz regime. Appl Phys Lett

2019;115:021101.

36. Zolliker P, Shalaby M, Söllinger E, Mavrona E, Hack E.

Real-Time High Resolution THz Imaging with a Fiber-

Coupled Photo Conductive Antenna and an Uncooled

Microbolometer Camera. Sensors (Basel) 2021;21:3757.

37. Chen X, Hu D, Mescall R, You G, Basov DN, Dai Q, Liu

M. Modern Scattering-Type Scanning Near-Field Optical

Microscopy for Advanced Material Research. Adv Mater

2019;31:e1804774.

38. Yang Z, Tang D, Hu J, Tang M, Zhang M, Cui HL,

Wang L, Chang C, Fan C, Li J, Wang H. Near-Field

Nanoscopic Terahertz Imaging of Single Proteins. Small

2021;17:e2005814.

39. Vallés A, He J, Ohno S, Omatsu T, Miyamoto K.

Broadband high-resolution terahertz single-pixel imaging.

Opt Express 2020;28:28868-81.

40. Sun MJ, Zhang JM. Single-Pixel Imaging and Its

Application in Three-Dimensional Reconstruction: A Brief

Review. Sensors (Basel) 2019;19:732.

41. Zanotto L, Piccoli R, Dong J, Carafni D, Morandotti R,

Razzari L. Time-domain terahertz compressive imaging.

Opt Express 2020;28:3795-802.

42. Chan WL, Charan K, Takhar D, Kelly KF, Baraniuk

RG, Mittleman DM. A single-pixel terahertz imaging

system based on compressed sensing. Appl Phys Lett

2008;93:121105.

43. Shang Y, Wang X, Sun W, Han P, Ye J, Feng S, Zhang

Y. Terahertz image reconstruction based on compressed

sensing and inverse Fresnel diffraction. Opt Express

2019;27:14725-35.

44. Stantchev RI, Yu X, Blu T, Pickwell-MacPherson E. Real-

time terahertz imaging with a single-pixel detector. Nat

Commun 2020;11:2535.

45. Mohr T, Herdt A, Elsässer W. 2D tomographic terahertz

imaging using a single pixel detector. Opt Express

2018;26:3353-67.

46. Li W, Hu X, Wu J, Fan K, Chen B, Zhang C, Hu W, Cao

X, Jin B, Lu Y, Chen J, Wu P. Dual-color terahertz spatial

light modulator for single-pixel imaging. Light Sci Appl

2022;11:191.

47. Kowalski M. Real-time concealed object detection and

recognition in passive imaging at 250 GHz. Appl Opt

2019;58:3134-40.

48. Lindley-Hatcher H, Stantchev RI, Chen X, Hernandez-

Serrano AI, Hardwicke J, Pickwell-MacPherson E. Real

time THz imaging—opportunities and challenges for skin

cancer detection. Appl Phys Lett 2021;118:230501.

49. Guerboukha H, Nallappan K, Skorobogatiy M. Toward

real-time terahertz imaging. Adv Opt Photonics

2018;10:843-938.

50. Kanda N, Konishi K, Nemoto N, Midorikawa K,

Kuwata-Gonokami M. Real-time broadband terahertz

spectroscopic imaging by using a high-sensitivity terahertz

camera. Sci Rep 2017;7:42540.

51. Wen Y, Jia D, Ma W, Feng Y, Liu M, Dong L, Zhao Y,

Yu X. Photomechanical meta-molecule array for real-time

terahertz imaging. Microsyst Nanoeng 2017;3:17071.

52. Perraud JB, Guillet JP, Redon O, Hamdi M, Simoens F,

Mounaix P. Shape-from-focus for real-time terahertz 3D

Quantitative Imaging in Medicine and Surgery, Vol 13, No 12 December 2023 8783

imaging. Opt Lett 2019;44:483-6.

53. Blanchard F, Tanaka K. Improving time and space

resolution in electro-optic sampling for near-eld terahertz

imaging. Opt Lett 2016;41:4645-8.

54. Blanchard F, Arikawa T, Tanaka K. Real-Time Megapixel

Electro-Optical Imaging of THz Beams with Probe Power

Normalization. Sensors (Basel) 2022;22:4482.

55. Wilkinson L, Gathani T. Understanding breast cancer as a

global health concern. Br J Radiol 2022;95:20211033.

56. Ashworth PC, O’Kelly P, Purushotham AD, Pinder SE,

Kontos M, Pepper M, Wallace VP. An intra-operative

THz probe for use during the surgical removal of breast

tumors. 2008 33rd International Conference on Infrared,

Millimeter and Terahertz Waves. Pasadena, CA, USA:

IEEE; 2008:1-3.

57. Wang L. Terahertz Imaging for Breast Cancer Detection.

Sensors (Basel) 2021;21:6465.

58. Ashworth PC, Pickwell-MacPherson E, Provenzano E,

Pinder SE, Purushotham AD, Pepper M, Wallace VP.

Terahertz pulsed spectroscopy of freshly excised human

breast cancer. Opt Express 2009;17:12444-54.

59. Cassar Q, Al-Ibadi A, Mavarani L, Hillger P, Grzyb

J, MacGrogan G, Zimmer T, Pfeiffer UR, Guillet JP,

Mounaix P. Pilot study of freshly excised breast tissue

response in the 300-600 GHz range. Biomed Opt Express

2018;9:2930-42.

60. Vohra N, Bowman T, Bailey K, El-Shenawee M.

Terahertz Imaging and Characterization Protocol

for Freshly Excised Breast Cancer Tumors. J Vis Exp

2020;(158):10.3791/61007.

61. Bowman T, Wu Y, Gauch J, Campbell LK, El-Shenawee

M. Terahertz Imaging of Three-Dimensional Dehydrated

Breast Cancer Tumors. J Infrared Millim Terahertz Waves

2017;38:766-86.

62. Bowman T, El-Shenawee M, Campbell LK. Terahertz

transmission vs reection imaging and model-based

characterization for excised breast carcinomas. Biomed

Opt Express 2016;7:3756-83.

63. Grootendorst MR, Fitzgerald AJ, Brouwer de Koning

SG, Santaolalla A, Portieri A, Van Hemelrijck M, Young

MR, Owen J, Cariati M, Pepper M, Wallace VP, Pinder

SE, Purushotham A. Use of a handheld terahertz pulsed

imaging device to differentiate benign and malignant

breast tissue. Biomed Opt Express 2017;8:2932-45.

64. Okada K, Cassar Q, Murakami H, MacGrogan G,

Guillet JP, Mounaix P, Tonouchi M, Serita K. Label-Free

Observation of Micrometric Inhomogeneity of Human

Breast Cancer Cell Density Using Terahertz Near-Field

Microscopy. Photonics 2021;8:151.

65. Mavarani L, Hillger P, Bücher T, Grzyb J, Pfeiffer U,

Cassar Q, Al-Ibadi A, Zimmer T, Guillet J, Mounaix P,

MacGrogan G. NearSense – Advances Towards a Silicon-

Based Terahertz Near-Field Imaging Sensor for Ex Vivo

Breast Tumour Identication. Frequenz 2018;72:93-9.

66. Grzyb J, Heinemann B, Pfeiffer UR. A 0.55 THz Near-

Field Sensor With a μm -Range Lateral Resolution Fully

Integrated in 130 nm SiGe BiCMOS. IEEE J Solid-State

Circuits 2016;51:3063-77.

67. Pfeiffer UR, Hillger P, Jain R, Grzyb J, Bucher T, Cassar

Q, MacGrogan G, Guillet JP, Mounaix P, Zimmer T. Ex

Vivo Breast Tumor Identication: Advances Toward a

Silicon-Based Terahertz Near-Field Imaging Sensor. IEEE

Microwave Magazine 2019;20:32-46.

68. Henry SC, Zurk LM, Schecklman S. Terahertz spectral

imaging using correlation processing. IEEE Trans

Terahertz Sci Technol 2013;3:486-93.

69. Bowman T, Chavez T, Khan K, Wu J, Chakraborty A,

Rajaram N, Bailey K, El-Shenawee M. Pulsed terahertz

imaging of breast cancer in freshly excised murine tumors.

J Biomed Opt 2018;23:1-13.

70. Chavez T, Bowman T, Wu J, Bailey K, El-Shenawee

M. Assessment of Terahertz Imaging for Excised Breast

Cancer Tumors with Image Morphing. J Infrared Millim

Terahertz Waves 2018;39:1283-302.

71. Hassan JM, Hakeem SI. Detection and Classication of

Breast Cancer Based-On Terahertz Imaging Technique

Using Articial Neural Network & K-Nearest Neighbor

Algorithm. Int J Appl Eng Res 2017;12:10661-8.

72. Liu W, Zhang R, Ling Y, Tang H, She R, Wei G, Gong

X, Lu Y. Automatic recognition of breast invasive ductal

carcinoma based on terahertz spectroscopy with wavelet

packet transform and machine learning. Biomed Opt

Express 2020;11:971-81.

73. Chavez T, Vohra N, Wu J, Bailey K, El-Shenawee M.

Breast Cancer Detection with Low-dimension Ordered

Orthogonal Projection in Terahertz Imaging. IEEE Trans

Terahertz Sci Technol 2020;10:176-89.

74. Chavez T, Vohra N, Bailey K, El-Shenawee M, Wu J.

Supervised Bayesian learning for breast cancer detection

in terahertz imaging. Biomed Signal Process Control

2021;70:102949.

75. Cassar Q, Caravera S, MacGrogan G, Bücher T, Hillger

P, Pfeiffer U, Zimmer T, Guillet JP, Mounaix P. Terahertz

refractive index-based morphological dilation for breast

carcinoma delineation. Sci Rep 2021;11:6457.

76. Bowman T, El-Shenawee M, Bailey K. Challenges in

Cong et al. Review of biological terahertz imaging

8784

Terahertz Imaging of Freshly Excised Human Breast

Tumors. 2018 IEEE International Symposium on Antennas

and Propagation & USNC/URSI National Radio Science

Meeting. Boston, MA, USA: IEEE; 2018:13-4.

77. Liu H, Vohra N, Bailey K, El-Shenawee M, Nelson AH.

Deep Learning Classication of Breast Cancer Tissue from

Terahertz Imaging Through Wavelet Synchro-Squeezed

Transformation and Transfer Learning. J Infrared Millim

Terahertz Waves 2022;43:48-70.

78. Oh SJ, Choi J, Maeng I, Park JY, Lee K, Huh YM, Suh JS,

Haam S, Son JH. Molecular imaging with terahertz waves.

Opt Express 2011;19:4009-16.

79. Zhang R, Zhang L, Wu T, Zuo S, Wang R, Zhang C,

Zhang J, Fang J. Contrast-enhanced continuous-terahertz-

wave imaging based on superparamagnetic iron oxide

nanoparticles for biomedical applications. Opt Express

2016;24:7915-21.

80. Bowman T, Walter A, Shenderova O, Nunn N, McGuire

G, El-Shenawee M. A Phantom Study of Terahertz

Spectroscopy and Imaging of Micro- and Nano-diamonds

and Nano-onions as Contrast Agents for Breast Cancer.

Biomed Phys Eng Express 2017;3:055001.

81. Zou Y, Li J, Cui Y, Tang P, Du L, Chen T, Meng K,

Liu Q, Feng H, Zhao J, Chen M, Zhu LG. Terahertz

Spectroscopic Diagnosis of Myelin Decit Brain in Mice

and Rhesus Monkey with Chemometric Techniques. Sci

Rep 2017;7:5176.

82. Shi L, Shumyatsky P, Rodríguez-Contreras A, Alfano R.

Terahertz spectroscopy of brain tissue from a mouse model

of Alzheimer's disease. J Biomed Opt 2016;21:15014.

83. Zhao H, Wang Y, Chen L, Shi J, Ma K, Tang L, Xu D,

Yao J, Feng H, Chen T. High-sensitivity terahertz imaging

of traumatic brain injury in a rat model. J Biomed Opt

2018;23:1-7.

84. Ji YB, Oh SJ, Kang SG, Heo J, Kim SH, Choi Y, Song S,

Son HY, Kim SH, Lee JH, Haam SJ, Huh YM, Chang JH,

Joo C, Suh JS. Terahertz reectometry imaging for low

and high grade gliomas. Sci Rep 2016;6:36040.

85. Wu L, Xu D, Wang Y, Zhang Y, Wang H, Liao B, Gong

S, Chen T, Wu N, Feng H, Yao J. Horizontal-scanning

attenuated total reection terahertz imaging for biological

tissues. Neurophotonics 2020;7:025005.

86. Wu L, Wang Y, Liao B, Zhao L, Chen K, Ge M, Li H,

Chen T, Feng H, Xu D, Yao J. Temperature dependent

terahertz spectroscopy and imaging of orthotopic

brain gliomas in mouse models. Biomed Opt Express

2022;13:93-104.

87. Oh SJ, Kim SH, Ji YB, Jeong K, Park Y, Yang J, Park

DW, Noh SK, Kang SG, Huh YM, Son JH, Suh JS. Study

of freshly excised brain tissues using terahertz imaging.

Biomed Opt Express 2014;5:2837-42.

88. Lee SH, Shin S, Roh Y, Oh SJ, Lee SH, Song HS, Ryu

YS, Kim YK, Seo M. Label-free brain tissue imaging using

large-area terahertz metamaterials. Biosens Bioelectron

2020;170:112663.

89. Shi J, Wang Y, Chen T, Xu D, Zhao H, Chen L, Yan C,

Tang L, He Y, Feng H, Yao J. Automatic evaluation of

traumatic brain injury based on terahertz imaging with

machine learning. Opt Express 2018;26:6371-81.

90. Wang Y, Wang G, Xu D, Jiang B, Ge M, Wu L, Yang

C, Mu N, Wang S, Chang C, Chen T, Feng H, Yao J.

Terahertz spectroscopic diagnosis of early blast-induced

traumatic brain injury in rats. Biomed Opt Express

2020;11:4085-98.

91. Cherkasova O, Peng Y, Konnikova M, Kistenev Y, Shi

C, Vrazhnov D, Shevelev O, Zavjalov E, Kuznetsov S,

Shkurinov A. Diagnosis of Glioma Molecular Markers by

Terahertz Technologies. Photonics 2021;8:22.

92. Ostrom QT, Bauchet L, Davis FG, Deltour I, Fisher

JL, Langer CE, Pekmezci M, Schwartzbaum JA, Turner

MC, Walsh KM, Wrensch MR, Barnholtz-Sloan JS. The

epidemiology of glioma in adults: a "state of the science"

review. Neuro Oncol 2014;16:896-913.

93. Yamaguchi S, Fukushi Y, Kubota O, Itsuji T, Ouchi T,

Yamamoto S. Brain tumor imaging of rat fresh tissue using

terahertz spectroscopy. Sci Rep 2016;6:30124.

94. Gavdush AA, Chernomyrdin NV, Komandin GA,

Dolganova IN, Nikitin PV, Musina GR, Katyba GM,

Kucheryavenko AS, Reshetov IV, Potapov AA, Tuchin VV,

Zaytsev KI. Terahertz dielectric spectroscopy of human

brain gliomas and intact tissues ex vivo: double-Debye

and double-overdamped-oscillator models of dielectric

response. Biomed Opt Express 2021;12:69-83.

95. Gavdush AA, Chernomyrdin NV, Malakhov KM, Beshplav

ST, Dolganova IN, Kosyrkova AV, Nikitin PV, Musina

GR, Katyba GM, Reshetov IV, Cherkasova OP, Komandin

GA, Karasik VE, Potapov AA, Tuchin VV, Zaytsev KI.

Terahertz spectroscopy of gelatin-embedded human brain

gliomas of different grades: a road toward intraoperative

THz diagnosis. J Biomed Opt 2019;24:1-5.

96. Wu L, Xu D, Wang Y, Liao B, Jiang Z, Zhao L, Sun Z,

Wu N, Chen T, Feng H, Yao J. Study of in vivo brain

glioma in a mouse model using continuous-wave terahertz

reection imaging. Biomed Opt Express 2019;10:3953-62.

97. Huang Y, Singh R, Xie L, Ying Y. Attenuated Total

Reection for Terahertz Modulation, Sensing,

Quantitative Imaging in Medicine and Surgery, Vol 13, No 12 December 2023 8785

Spectroscopy and Imaging Applications: A Review. Appl

Sci 2020;10:4688.

98. Li J, Liu M, Gao J, Jiang Y, Wu L, Cheong YK, Ren G,

Yang Z. AVNP2 protects against cognitive impairments

induced by C6 glioma by suppressing tumour associated

inammation in rats. Brain Behav Immun 2020;87:645-59.

99. Chernomyrdin NV, Skorobogatiy MA, Gavdush AA,

Musina GR, Katyba GM, Komandin GA, Khorokhorov

AM, Spektor IE, Tuchin VV, Zaytsev KI. Quantitative

super-resolution solid immersion microscopy via refractive

index prole reconstruction. Optica 2021;8:1471-80.

100. Roh Y, Lee SH, Kwak J, Song HS, Shin S, Kim YK,

Wu JW, Ju BK, Kang B, Seo M. Terahertz imaging with

metamaterials for biological applications. Sens Actuators B

Chem 2022;352:130993.

101. Wang Y, Sun Z, Xu D, Wu L, Chang J, Tang L, Jiang

Z, Jiang B, Wang G, Chen T, Feng H, Yao J. A hybrid

method based region of interest segmentation for

continuous wave terahertz imaging. J Phys D Appl Phys

2020;53:095403.

102. Bajwa N, Au J, Jarrahy R, Sung S, Fishbein MC, Riopelle

D, Ennis DB, Aghaloo T, St John MA, Grundfest WS,

Taylor ZD. Non-invasive terahertz imaging of tissue water

content for ap viability assessment. Biomed Opt Express

2017;8:460-74.

103. Woodward RM, Cole BE, Wallace VP, Pye RJ, Arnone

DD, Lineld EH, Pepper M. Terahertz pulse imaging in

reection geometry of human skin cancer and skin tissue.

Phys Med Biol 2002;47:3853-63.

104. Fan S, Ung BSY, Parrott EPJ, Wallace VP, Pickwell-

MacPherson E. In vivo terahertz reection imaging

of human scars during and after the healing process. J

Biophotonics 2017;10:1143-51.

105. Li J, Xie Y, Sun P. Edge detection on terahertz pulse

imaging of dehydrated cutaneous malignant melanoma

embedded in parafn. Front Optoelectron 2019;12:317-23.

106. Rahman A, Rahman AK, Rao B. Early detection of skin

cancer via terahertz spectral proling and 3D imaging.

Biosens Bioelectron 2016;82:64-70.

107. Joseph CS, Patel R, Neel VA, Giles RH, Yaroslavsky

AN. Imaging of ex vivo nonmelanoma skin cancers in the

optical and terahertz spectral regions optical and terahertz

skin cancers imaging. J Biophotonics 2014;7:295-303.

108. Sun Q, Parrott EPJ, Pickwell-Macpherson E. In vivo

estimation of the water diffusivity in occluded human

skin using terahertz rejection spectroscopy. 2017 42nd

International Conference on Infrared, Millimeter, and

Terahertz Waves (IRMMW-THz). Cancun: IEEE; 2017.

109. Fourman MS, Phillips BT, Crawford L, McClain SA,

Lin F, Thode HC Jr, Dagum AB, Singer AJ, Clark RA.

Indocyanine green dye angiography accurately predicts

survival in the zone of ischemia in a burn comb model.

Burns 2014;40:940-6.

110. Hoeksema H, Baker RD, Holland AJ, Perry T, Jeffery SL,

Verbelen J, Monstrey S. A new, fast LDI for assessment

of burns: a multi-centre clinical evaluation. Burns

2014;40:1274-82.

111. Venclauskiene A, Basevicius A, Zacharevskij E, Vaicekauskas

V, Rimdeika R, Lukosevicius S. Laser Doppler imaging as a

tool in the burn wound treatment protocol. Wideochir Inne

Tech Maloinwazyjne 2014;9:24-30.

112. Kim KH, Pierce MC, Maguluri G, Park BH, Yoon SJ,

Lydon M, Sheridan R, de Boer JF. In vivo imaging of

human burn injuries with polarization-sensitive optical

coherence tomography. J Biomed Opt 2012;17:066012.

113. Seki T, Fujioka M, Fukushima H, Matsumori H, Maegawa

N, Norimoto K, Okuchi K. Regional tissue oxygen

saturation measured by near-infrared spectroscopy to

assess the depth of burn injuries. Int J Burns Trauma

2014;4:40-4.

114. Li S, Mohamedi AH, Senkowsky J, Nair A, Tang L.

Imaging in Chronic Wound Diagnostics. Adv Wound Care

(New Rochelle) 2020;9:245-63.

115. Dutta M, Bhalla AS, Guo R. THz Imaging of Skin Burn:

Seeing the Unseen-An Overview. Adv Wound Care (New

Rochelle) 2016;5:338-48.

116. Mueller CS, Reichrath J. Histology of melanoma

and nonmelanoma skin cancer. Adv Exp Med Biol

2008;624:215-26.

117. Zhu S, Sun C, Zhang L, Du X, Tan X, Peng S. Incidence

Trends and Survival Prediction of Malignant Skin Cancer:

A SEER-Based Study. Int J Gen Med 2022;15:2945-56.

118. Keshavarz A, Vafapour Z. Water-Based Terahertz

Metamaterial for Skin Cancer Detection Application.

IEEE Sensors Journal 2018;19:1519-24.

119. Azizi S, Nouri-Novin S, Seyedsharbaty MM, Zarrabi FB.

Early skin cancer detection sensor based on photonic band

gap and graphene load at terahertz regime. Opt Quant

Electron 2018;50:230.

120. Li D, Yang Z, Fu A, Chen T, Chen L, Tang M, Zhang

H, Mu N, Wang S, Liang G, Wang H. Detecting

melanoma with a terahertz spectroscopy imaging

technique. Spectrochim Acta A Mol Biomol Spectrosc

2020;234:118229.

121. Zhang R, Yang K, Abbasi Q, Abuali NA, Alomainy A.

Experimental Characterization of Articial Human Skin

Cong et al. Review of biological terahertz imaging

8786

with Melanomas for Accurate Modelling and Detection

in Healthcare Application. 2018 43rd International

Conference on Infrared, Millimeter, and Terahertz Waves

(IRMMW-THz2018). Nagoya: IEEE; 2018.

122. Yang Z, Zhang M, Li D, Chen L, Fu A, Liang Y, Wang H.

Study on an articial phenomenon observed in terahertz

biological imaging. Biomed Opt Express 2021;12:3133-41.

123. Joseph CS, Yaroslavsky AN, Neel VA, Goyette TM,

Giles RH. Continuous wave terahertz transmission

imaging of nonmelanoma skin cancers. Lasers Surg Med

2011;43:457-62.

124. Niculet E, Craescu M, Rebegea L, Bobeica C, Nastase

F, Lupasteanu G, Stan DJ, Chioncel V, Anghel L, Lungu

M, Tatu AL. Basal cell carcinoma: Comprehensive

clinical and histopathological aspects, novel imaging tools

and therapeutic approaches (Review). Exp Ther Med

2022;23:60.

125. Vilagosh Z, Lajevardipour A, Wood AW. Computational

phantom study of frozen melanoma imaging at 0.45

terahertz. Bioelectromagnetics 2019;40:118-27.

126. Kim KW, Kim KS, Kim H, Lee SH, Park JH, Han JH,

Seok SH, Park J, Choi Y, Kim YI, Han JK, Son JH.

Terahertz dynamic imaging of skin drug absorption. Opt

Express 2012;20:9476-84.

127. Wang L, Guilavogui S, Yin H, Wu Y, Zang X, Xie J, Ding

L, Chen L. Critical Factors for In Vivo Measurements of

Human Skin by Terahertz Attenuated Total Reection

Spectroscopy. Sensors (Basel) 2020;20:4256.

Cite this article as: Cong M, Li W, Liu Y, Bi J, Wang X,

Yang X, Zhang Z, Zhang X, Zhao YN, Zhao R, Qiu J.

Biomedical application of terahertz imaging technology: a

narrative review. Quant Imaging Med Surg 2023;13(12):8768-

8786. doi: 10.21037/qims-23-526

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PLASMONICS

Jacob Wekalao

This study introduces a novel terahertz (THz) biosensor based on metasurfaces, designed for high-sensitivity peptide detection. The sensor features a multi-layered structure comprising gold, silver, and graphene elements deposited on a silicon dioxide substrate. Finite element method simulations were employed to evaluate the sensor's performance across two frequency bands: 0.32–0.44 THz and 0.11–0.15 THz. Key design parameters—including graphene chemical potential, resonator dimensions, and incident angle—were optimized to maximize sensor performance. The optimized device demonstrated exceptional sensitivity of 1500 GHzRIU⁻¹, with a figure of merit of 60 RIU⁻¹ and a detection limit of 0.016 RIU. The sensor successfully detected subtle changes in peptide concentrations, exhibiting frequency shifts of up to 60 GHz. Furthermore, an XGBoost regression model was implemented to predict sensor behaviour, achieving perfect accuracy (R² = 100%) across multiple parameter spaces. This high-performance biosensor shows significant promise for biochemical analysis applications, particularly in peptide detection and characterization, offering potential advances in drug discovery and disease diagnosis.

Design of a polarization-insensitive terahertz metamaterial biosensor for glioma tissue identification

Article

Full-text available

May 2025

There is an urgent need to develop label-free, accurate detection techniques for gliomas due to the high aggressiveness and heterogeneity of glioma tissues. In this study, a polarization-insensitive terahertz metamaterial biosensor based on a quadruple rotationally symmetric superunit is proposed to address the issues of insufficient sensitivity and polarization-dependent interference associated with conventional metamaterials in terahertz spectroscopy. The structural parameters were optimized through theoretical modeling and electromagnetic simulations, leading to the design of C4-symmetric metamaterials with stable responses over a wide incidence angle range. These metamaterials effectively mitigate signal distortion caused by the random orientation of metamaterial placement during experiments. The experiments were conducted using a terahertz frequency-domain spectroscopy system (THz-FDS) to detect isolated glioma tissues. The results demonstrate that the sensor maintains polarization insensitivity across the full 0-360° range, significantly enhancing the contrast in dielectric properties between tumor and normal tissues. Furthermore, its resonance frequency shift exhibits a strong correlation with tissue thickness, increasing up to 26 µm, after which it stabilizes and no longer exhibits significant changes. This study confirms that polarization-insensitive metamaterials can overcome existing imaging limitations, reduce operator-induced human errors, and provide a novel, non-invasive detection solution for intraoperative boundary delineation and pathological diagnosis of gliomas, with strong potential for clinical translation.

Effect of terahertz radiation on cells and cellular structures

Article

Full-text available

Jan 2025

The paper presents the results of modern research on the effects of electromagnetic terahertz radiation in the frequency range 0.5–100 THz at different levels of power density and exposure time on the viability of normal and cancer cells. As an accompanying tool for monitoring the effect of radiation on biological cells and tissues, spectroscopic research methods in the terahertz frequency range are described, and attention is focused on the possibility of using the spectra of interstitial water as a marker of pathological processes. The problem of the safety of terahertz radiation for the human body from the point of view of its effect on the structures and systems of biological cells is also considered. Graphical Abstract

FPGA Readout for Frequency-Multiplexed Array of Micromechanical Resonators for Sub-Terahertz Imaging

Article

Full-text available

Nov 2024
SENSORS-BASEL

Field programmable gate arrays (FPGAs) have not only enhanced traditional sensing methods, such as pixel detection (CCD and CMOS), but also enabled the development of innovative approaches with significant potential for particle detection. This is particularly relevant in terahertz (THz) ray detection, where microbolometer-based focal plane arrays (FPAs) using microelectromechanical (MEMS) resonators are among the most promising solutions. Designing high-performance, high-pixel-density sensors is challenging without FPGAs, which are crucial for deterministic parallel processing, fast ADC/DAC control, and handling large data throughput. This paper presents a MEMS-resonator detector, fully managed via an FPGA, capable of controlling pixel excitation and tracking resonance-frequency shifts due to radiation using parallel digital lock-in amplifiers. The innovative FPGA architecture, based on a lock-in matrix, enhances the open-loop readout technique by a factor of 32. Measurements were performed on a frequency-multiplexed, 256-pixel sensor designed for imaging applications.

Accurate prediction of wood moisture content using terahertz time-domain spectroscopy combined with machine learning algorithms

Article

May 2025
IND CROP PROD

High-Sensitivity Flexible Meta-surface for Terahertz-Based Breast Cancer Differentiation: Study in Cellular and Fresh Tissue

Article

May 2025
SENSOR ACTUAT B-CHEM

Investigating the Potential for Label‐Free Detection of Cell Apoptosis through Terahertz Nano‐Metasurfaces

Article

Full-text available

Jan 2025

A label‐free method to monitor apoptosis in cells using a terahertz (THz) metasurface‐based biosensing platform is demonstrated. Apoptosis, a crucial process for eliminating damaged cells and preventing cancer, often dysregulates in cancer, leading to tumor formation. Measuring apoptosis directly in cells’ natural environment without labeling is a significant aim in cell biology. The platform leverages THz metasurfaces to detect H2O2‐induced apoptosis noninvasively, focusing on transmittance changes and frequency shifts to comprehensively analyze cellular dielectric properties. HEK293 cells treated with hydrogen peroxide (H2O2), a natural inducer of oxidation stress across diverse cell types, are used to validate the system, demonstrating precise sensitivity to apoptosis without the need for chemical labeling or invasive treatments. The results show a clear correlation between apoptosis, measured via Cell Counting Kit‐8, and terahertz transmittance spectra. This approach offers a powerful combination of label‐free, nondestructive, and further real‐time apoptosis monitoring in cellular systems, highlighting its potential for diverse applications in cell biology and therapeutic research.

Food safety application of Terahertz spectroscopy based on metamaterials：A review

Article

Nov 2024

Hygienic problems of using terahertz electromagnetic radiation (literature review)

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Sep 2024

The purpose of the work is to review and analyze domestic and foreign scientific works, systematize the scope of application of terahertz electromagnetic radiation (EMR) to determine hygienic problems in the field of health risk prevention in the development and use of modern radioelectronic devices. The literature search was conducted on the databases: eLibrary, Web of Science, and fifty. During the study of scientific literature, from over fifty works were analyzed, there 36 sources were selected 36 sources corresponded to the purpose of the study. Today, the urgent tasks are to predict the parameters of a complex electromagnetic environment in open areas and inside buildings using mobile communication standards 4, 5 and 6G, scientific justification of hygienic standards for the combined effects of the electromagnetic factor, methodological approaches to monitoring EMR levels, including the development of domestic selective EMR meters in a wide range of frequencies (radio frequency and terahertz ranges)

Spintronics Based Terahertz Sources

Article

Full-text available

Nov 2022

Terahertz (THz) sources, covering a range from about 0.1 to 10 THz, are key devices for applying terahertz technology. Spintronics-based THz sources, with the advantages of low cost, ultra-broadband, high efficiency, and tunable polarization, have attracted a great deal of attention recently. This paper reviews the emission mechanism, experimental implementation, performance optimization, manipulation, and applications of spintronic THz sources. The recent advances and existing problems in spintronic THz sources are fully present and discussed. This review is expected to be an introduction of spintronic terahertz sources for novices in this field, as well as a comprehensive reference for experienced researchers.

Modern Scattering‐Type Scanning Near‐Field Optical Microscopy for Advanced Material Research

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Full-text available

Sep 2022
ADV MATER

Dual-color terahertz spatial light modulator for single-pixel imaging

Article

Full-text available

Jun 2022

Spatial light modulators (SLM), capable of dynamically and spatially manipulating electromagnetic waves, have reshaped modern life in projection display and remote sensing. The progress of SLM will expedite next-generation communication and biomedical imaging in the terahertz (THz) range. However, most current THz SLMs are adapted from optical alternatives that still need improvement in terms of uniformity, speed, and bandwidth. Here, we designed, fabricated, and characterized an 8 × 8 THz SLM based on tunable liquid crystal metamaterial absorbers for THz single-pixel compressive imaging. We demonstrated dual-color compressive sensing (CS) imaging for dispersive objects utilizing the large frequency shift controlled by an external electric field. We developed auto-calibrated compressive sensing (ACS) algorithm to mitigate the impact of the spatially nonuniform THz incident beam and pixel modulation, which significantly improves the fidelity of reconstructed images. Furthermore, the complementary modulation at two absorption frequencies enables Hadamard masks with negative element values to be realized by frequency-switching, thereby halving the imaging time. The demonstrated imaging system paves a new route for THz single-pixel multispectral imaging with high reliability and low cost. Liquid crystal metasurface based spatial light modulator is developed for terahertz dual-color compressive imaging. Significant improvement of imaging quality and speed is demonstrated using proposed auto-calibrated algorithms and frequency-switching method.

Real-Time Megapixel Electro-Optical Imaging of THz Beams with Probe Power Normalization

Article

Full-text available

Jun 2022
SENSORS-BASEL

In this work, we present a simple method to improve the spatial uniformity of two-dimensional electro-optical imaging of terahertz (THz) beams. In this system, near-field THz images are captured by fully illuminating a sample using conventional optical microscope objectives. Unfortunately, due to the linear relationship between the optical probe power and the measured THz electric field, any spatial variation in probe intensity translates directly into a variation of the recorded THz electric field. Using a single normalized background frame information map as a calibration tool prior to recording a sequence of THz images, we show a full recovery of a two-dimensional flat field for various combinations of magnification factors. Our results suggest that the implementation of dynamic intensity profile correction is a promising avenue for real-time electro-optical imaging of THz beams.

Incidence Trends and Survival Prediction of Malignant Skin Cancer: A SEER-Based Study

Article

Full-text available

Mar 2022

Purpose This study aimed to analyze the incidence trend and further explore the risk factors influencing the survival among patients of malignant skin cancer in America. Methods Age-adjusted incidence rates, annual percentage change (APC) of different sex and ethnicity in 1973–2015 and patient records were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate analysis and multivariate Cox regression were used to analyze risk factors influencing the survival in skin cancer patients. Survival curves and nomograms were constructed to evaluate the survival prediction by R. Results The overall age-adjusted incidence of skin cancer increased in America from 1973 to 2005 (APC = 2.8%, 95% CI: 2.6–2.9%, P < 0.05), particularly in white patients, 66-year-old people, and males. The 3- and 5-year overall survival (OS) rates were 51.4% and 33.8%, respectively. Independent predictors for short OS include age over 65, white ethnicity, other marital status and no surgery (P < 0.05). Stage was not an independent factor of survival (P > 0.05). The nomogram with a C-index of 0.72 (95% CI: 0.71–0.73) matched an appropriate calibration curve. Conclusion Incidence of skin cancer in America was on the rise during 1973–2015 based on SEER database. Age, ethnicity, marital status and surgical history were related with survival of malignant skin cancer. Nomograms were effective tools for predicting the survival prognosis.

Deep Learning Classification of Breast Cancer Tissue from Terahertz Imaging Through Wavelet Synchro-Squeezed Transformation and Transfer Learning

Article

Full-text available

Feb 2022
J INFRARED MILLIM TE

Terahertz imaging and spectroscopy is an exciting technology that has the potential to provide insights in medical imaging. Prior research has leveraged statistical inference to classify tissue regions from terahertz images. To date, these approaches have shown that the segmentation problem is challenging for images of fresh tissue and for tumors that have invaded muscular regions. Artificial intelligence, particularly machine learning and deep learning, has been shown to improve performance in some medical imaging challenges. This paper builds on that literature by modifying a set of deep learning approaches to the challenge of classifying tissue regions of images captured by terahertz imaging and spectroscopy of freshly excised murine xenograft tissue. Our approach is to preprocess the images through a wavelet synchronous-squeezed transformation (WSST) to convert time-sequential terahertz data of each THz pixel to a spectrogram. Spectrograms are used as input tensors to a deep convolution neural network for pixel-wise classification. Based on the classification result of each pixel, a cancer tissue segmentation map is achieved. In experimentation, we adopt leave-one-sample-out cross-validation strategy, and evaluate our chosen networks and results using multiple metrics such as accuracy, precision, intersection, and size. The results from this experimentation demonstrate improvement in classification accuracy compared to statistical methods, an improvement to segmentation between muscle and cancerous regions in xenograft tumors, and identify areas to improve the imaging and classification methodology.

Understanding breast cancer as a global health concern

Article

Full-text available

Dec 2021

Breast cancer is now the most commonly diagnosed cancer in the world. The most recent global cancer burden figures estimate that there were 2.26 million incident breast cancer cases in 2020 and the disease is the leading cause of cancer mortality in women worldwide. The incidence is strongly correlated with human development, with a large rise in cases anticipated in regions of the world that are currently undergoing economic transformation. Survival, however, is far less favourable in less developed regions. There are a multitude of factors behind disparities in the global survival rates, including delays in diagnosis and lack of access to effective treatment. The World Health Organization’s new Global Breast Cancer Initiative was launched this year to address this urgent global health challenge. It aims to improve survival across the world through three pillars: health promotion, timely diagnosis, and comprehensive treatment and supportive care. In this article, we discuss the key challenges of breast cancer care and control in a global context.

Improvement of Response Time and Heat Transfer Capacity of Metamaterial Absorber for Terahertz Detector Applications

Article

Mar 2023

We have introduced a THz metamaterial absorber (MMA) system that consists of a metal disk antenna connected to successive dielectric and metal layers via a metal rod. The heat-transfer speed and capacity of the proposed platform with a metal rod inserted through the dielectric layer have been numerically studied for the first time in this work. The proposed THz MMA system can improve the heat-transfer capacity of 0.7 K/s compared to without a metal rod situation. The simulation proves that heat transfer can be achieved in the proposed absorber less than

10 ~\mu \text{s}

times compared to without a metal rod case. The simulation results indicate that the absorption strength of the MMA is almost independent of the rod depth. This detector system will be remarkably beneficial for imaging applications where fast heat-signal conversion is necessary.

6G technology development vision and terahertz communication

Article

Dec 2021
ACTA PHYS SIN-CH ED

The future sixth-generation (6G) wireless network has advantages of global coverage, high spectrum efficiency, low cost, high safety, and higher intelligent level. The 6G technology can create ubiquitous intelligent mobile networks for human society. Terahertz wireless communication has the characteristics of high data transmission rate, low delay, and anti-interference, which may be widely used in 6G technology. This paper mainly introduces the planning vision, development status, and key 6G technology, and analyzes the terahertz devices, channels, communication systems, and the possible development trend of 6G technology.

THz medical imaging: from in vitro to in vivo

Article

Jan 2022
TRENDS BIOTECHNOL

Terahertz (THz) radiation has attracted considerable attention in medical imaging owing to its nonionizing and spectral fingerprinting characteristics. To date, most studies have focused on in vitro and ex vivo objects with water-removing pretreatment because the water in vivo excessively absorbs the THz waves, which causes deterioration of the image quality. In this review, we discuss how THz medical imaging can be used for a living body. The development of imaging contrast agents has been particularly useful to this end. In addition, we also introduce progress in novel THz imaging methods that could be more suitable for in vivo applications. Based on our discussions, we chart a developmental roadmap to take THz medical imaging from in vitro to in vivo.