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Breast cancer diagnosis using radar-based medical MicroWave Imaging (MWI) has been studied in recent years. Realistic numerical and physical models of the breast are needed for simulation and experimental testing of MWI prototypes. We aim to provide the scientific community with an online repository of multiple accurate realistic breast tissue mode...
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... Conversely, exploiting images acquired by magnetic resonance imaging (MRI) examination has the potential to enrich the available cohort of breast models in terms of both dimensional and anatomical variability. The use of an MRI-based approach to make anthropomorphic breast phantoms has been demonstrated by others to be a valid approach for applications dedicated to microwave imaging diagnosis and biomechanical finite element models (7)(8)(9) . The creation of physical anthropomorphic breast phantoms for use in X-ray breast imaging investigations needs proper manufacturing technology, suitable materials, and validation of the results, as well as an assessment of time and costs. ...
... A different approach directly relates a parameter of the MRI signal, as e.g. the longitudinal relaxation time (T1), to the corresponding tissue and then assigns to that tissue the dielectric properties reported in public databases (Hasgall et al 2022, Andreuccetti et al 1997. Using this approach, models of breast (Pelicano et al 2021) and of axillary lymph nodes were developed. In this procedure however, as in the w-EPT method, the properties assigned to a certain tissue are not the ones of the patient but are average values taken from the literature and mostly derived from animal samples. ...
Objective. Aim of this work is to illustrate and experimentally validate a model to evaluate the dielectric properties of biological tissues on a wide frequency band using the magnetic resonance imaging (MRI) technique. Approach. The dielectric behaviour of biological tissues depends on frequency, according to the so-called relaxation mechanisms. The adopted model derives the dielectric properties of biological tissues in the frequency range 10 MHz–20 GHz considering the presence of two relaxation mechanisms whose parameters are determined from quantities derived from MRI acquisitions. In particular, the MRI derived quantities are the water content and the dielectric properties of the tissue under study at the frequency of the MR scanner. Main results. The model was first theoretically validated on muscle and fat using literature data in the frequency range 10 MHz–20 GHz. Results showed capabilities of reconstructing dielectric properties with errors within 16%. Then the model was applied to ex vivo muscle and liver tissues, comparing the MRI-derived properties with data measured by the open probe technique in the frequency range 10 MHz–3 GHz, showing promising results. Significance. The use of medical techniques based on the application of electromagnetic fields (EMFs) is significantly increasing. To provide safe and effective treatments, it is necessary to know how human tissues react to the applied EMF. Since this information is embedded in the dielectric properties of biological tissues, an accurate and precise dielectric characterization is needed. Biological tissues are heterogenous, and their characteristics depend on several factors. Consequently, it is necessary to characterize dielectric properties in vivo for each specific patient. While this aim cannot be reached with traditional measurement techniques, through the adopted model these properties can be reconstructed in vivo on a wide frequency band from non-invasive MRI acquisitions.
... Region growing [53] and Hoshen-Kopelman [54] algorithms were applied to pre-processed subtraction images from the DCE-fl3D sequence for tumor segmentation. Details regarding breast model processing are described in [55]. ...
... The 1-pole Cole-Cole parameters of the malignant tumors dielectric property curves are available in [32]. These parameters were subsequently converted into Debye parameters, as detailed in [55]. Benign breast tumors and breast tissues with low adipose content exhibit similar dielectric properties [32]. ...
The diagnosis of breast cancer through MicroWave Imaging (MWI) technology has been extensively researched over the past few decades. However, continuous improvements to systems are needed to achieve clinical viability. To this end, the numerical models employed in simulation studies need to be diversified, anatomically accurate, and also representative of the cases in clinical settings. Hence, we have created the first open-access repository of 3D anatomically accurate numerical models of the breast, derived from 3.0T Magnetic Resonance Images (MRI) of benign breast disease and breast cancer patients. The models include normal breast tissues (fat, fibroglandular, skin, and muscle tissues), and benign and cancerous breast tumors. The repository contains easily reconfigurable models which can be tumor-free or contain single or multiple tumors, allowing complex and realistic test scenarios needed for feasibility and performance assessment of MWI devices prior to experimental and clinical testing. It also includes an executable file which enables researchers to generate models incorporating the dielectric properties of breast tissues at a chosen frequency ranging from 3 to 10 GHz, thereby ensuring compatibility with a wide spectrum of research requirements and stages of development for any breast MWI prototype system. Currently, our dataset comprises MRI scans of 55 patients, but new exams will be continuously added.
... Early detection and intervention have been shown to significantly reduce the mortality rate and improve the quality of life and survival rates of breast cancer patients. Therefore, these factors are considered crucial for successful treatment outcomes [9]. ...
... In this study, a segmentation pipeline proposed by Pelicano et al. [9] was used to segment highly heterogeneous tumors from MRI exams. However, this study presented two limitations: 1) the need for manual selection of the seed point; and 2) the inability to segment multiple tumors, as the 3D region growing algorithm is only capable of identifying one tumor per seed of high intensity. ...
... Similarly, Al-Faris et al. in [2] studied the segmentation of tumors in MRI images using a modified version of the automatic seeded region growing algorithm, incorporating variations in seed point and threshold selection for improved performance compared to previous methods. More recently, Pelicano et al. [9] proposed a method to segment tumors in MRI images using a 3D version of the region growing technique. This method is similar to seeded region growing but operates in three dimensions. ...
Breast tumor is one of the most prominent indicators for diagnosis of breast cancer. Magnetic Resonance Imaging (MRI) is a relevant imaging modality tool for breast cancer screening. Moreover, an accurate 3D segmentation of breast tumors from MRI scans plays a key role in the analysis of the disease. This paper presents a pipeline to automatically segment multiple tumors in breast MRI scans, following the methodology proposed by one previous study, addressing its limitations in detecting multiple tumors and automatically selecting seed points using a 3D region growing algorithm. The pre-processing includes bias field correction, data normalization, and image filtering. The segmentation process involved several steps, including identifying high-intensity points, followed by identifying high-intensity regions using k-means clustering. Then, the centers of the regions were used as seeds for the 3D region growing algorithm, resulting in a mask with 3D structures. These masks were then analyzed in terms of their volume, compactness, and circularity. Despite the need for further adjustments in the model parameters, the successful segmentation of four tumors proved that our solution is a promising approach for automatic multi-tumor segmentation with the potential to be combined with a classification model relying on the characteristics of the segmented structures.
... Due to the dielectric characteristics of malignant and healthy breast tissue at microwave frequencies, MWI devices have been researched in recent years for early-stage breast cancer diagnostics [6][7][8]. Cancerous tissue differs from healthy tissue because the permeability of the cancer cell membrane changes, allowing more water to pass into the cell [9]. As a result, malignant cells have more water and dissolved ions inside them with respect to healthy cells of exactly the same type of tissue. ...
In this paper, a wideband antenna is proposed for ultra-wideband microwave imaging applications. The antenna is comprised of a tapered slot ground, a rectangular slotted patch and four star-shaped parasitic components. The added slotted patch is shown to be effective in improving the bandwidth and gain. The proposed antenna system provides a realized gain of 6 dBi, an efficiency of around 80% on the radiation bandwidth, and a wide impedance bandwidth (S11 < −10 dB) of 6.3 GHz (from 3.8 to 10.1 GHz). This supports a true wideband operation. Furthermore, the fidelity factor for face-to-face (FtF) direction is 91.6%, and for side by side (SbS) is 91.2%. This proves the excellent directionality and less signal distortion of the designed antenna. These high figures establish the potential use of the proposed antenna for imaging. A heterogeneous breast phantom with dielectric characteristics identical to actual breast tissue with the presence of tumors was constructed for experimental validation. An antenna array of the proposed antenna element was situated over an artificial breast to collect reflected and transmitted waves for tumor characterization. Finally, an imaging algorithm was used to process the retrieved data to recreate the image in order to detect the undesirable tumor object inside the breast phantom.
... The first step in planning the treatment usually involves positioning the body, skin marking, and taking imaging scans. The standard techniques used for breast cancer imaging include ultrasounds, mammography, magnetic resonance imaging (MRI), and positron emission tomography (PET), as well as techniques currently assessed at an experimental stage, such as microwave imaging (MI), infrared thermography (IRT), and others [1][2][3]. Early detection of neoplastic breast lesions and appropriate treatment at an early stage of the malignant disease significantly improve the chances of curing breast cancer, improve the quality of patients' life, and facilitate a quick return to normal life [4]. ...
Radio-frequency (RF) ablation is a reliable technique for the treatment of deep-seated malignant tumors, including breast carcinoma, using high ablative temperatures. The paper aims at a comparative analysis of the specific absorption rate and temperature distribution during RF ablation with regard to different female breast tumors. In the study, four tumor models equivalent to an irregular tumor were considered, i.e., an equivalent sphere and ellipsoid with the same surfaces and volumes as the irregular tumor and an equivalent sphere and ellipsoid inscribed in the irregular tumor. An RF applicator with a specific voltage, operating at 100 kHz inserted into the anatomically correct female breast, was applied as a source of electromagnetically induced heat. A conjugated Laplace equation with the modified Pennes equation was used to obtain the appropriate temperature gradient in the treated area. The levels of power dissipation in terms of the specific absorption rate (SAR) inside the naturalistically shaped tumor, together with the temperature profiles of the four simplified tumor models equivalent to the irregular one, were determined. It was suggested that the equivalent tumor models might successfully replace a real, irregularly shaped tumor, and the presented numeric methodology may play an important role in the complex therapeutic RF ablation process of irregularly shaped female breast tumors.
... The breast phantom's anatomy was recreated using anatomical representations and CT scans [12]. The anatomy of the breast can be divided into four main primary structures, as shown in Figure 1, for the creation of a phantom: lactiferous ducts, and skin, mammary glands (fibro glandular tissue) and adipose tissues (fat) [13]. In most of the existing MBI systems on the market, the breast is in a direct contact with the system's surface. ...
In this study, various breast phantom (BP) models for microwave breast imaging (MBI) are investigated and the creation and assessment of designed models are presented. Symmetrical and asymmetrical BP models have been constructed. based on 3D printed structures stuffed with various mixed material combinations that roles various breast tissue layers (skin, healthy fat tissue, glandular tissue, heterogeneous mix tissue, and tumor tissue) in terms of permittivity over the ultra-wide band frequency (3.1-10.6GHz) range. However, the main issue in making such phantoms is coming up with adequate material mixes that mimic those characteristics across the frequency band, as well as creating the phantom with realistic approach. The complex dielectric characteristics are tested after fabrication with a dielectric probe kit coupled to a VNA. Then, the measured complex dielectric properties are compared to the real human breast dielectric values. The symmetrical and asymmetrical phantoms' integrated structure allows the tumor and BPs to be dynamically combined to provide a test setup based on MBI technologies. Once the breast phantom has been produced, antenna arrays are positioned around it to collect scattering parameter data for tumor characterization. Finally, the extracted feature data was used to reconstruct the image in order to find the undesirable tumor component within the breast phantom using an imaging algorithm. INDEX TERMS Microwave imaging (MWI), breast cancer detection, dielectric characterization, symmetrical and asymmetrical breast.
... A chest X-ray (CXRs) is essential for diagnosing rib fracture, heart shape, blood vessel expansion, pulmonary vessel size, and pulmonary edema [1]. However, X-ray images are difficult to read because several anatomic structures cross and overlap in the chest [2], making it is difficult for radiologists to identify diseases hidden in a chest background where organs overlap [3,4]. ...
Chest radiography is one of the most widely used diagnostic methods in hospitals, but it is difficult to read clearly because several human organ tissues and bones overlap. Therefore, various image processing and rib segmentation methods have been proposed to focus on the desired target. However, it is challenging to segment ribs elaborately using deep learning because they cannot reflect the characteristics of each region. Identifying which region has specific characteristics vulnerable to deep learning is an essential indicator of developing segmentation methods in medical imaging. Therefore, it is necessary to compare the deep learning performance differences based on regional characteristics. This study compares the differences in deep learning performance based on the rib region to verify whether deep learning reflects the characteristics of each part and to demonstrate why this regional performance difference has occurred. We utilized 195 normal chest X-ray datasets with data augmentation for learning and 5-fold cross-validation. To compare segmentation performance, the rib image was divided vertically and horizontally based on the spine, clavicle, heart, and lower organs, which are characteristic indicators of the baseline chest X-ray. Resultingly, we found that the deep learning model showed a 6–7% difference in the segmentation performance depending on the regional characteristics of the rib. We verified that the performance differences in each region cannot be ignored. This study will enable a more precise segmentation of the ribs and the development of practical deep learning algorithms.
... The digital models were produced from data collected in a public repository (https://github.com/acpelicano/breast_models_repository accessed on 30 September 2024) of models derived from MRI images for MI research [19]. The repository provides segmented medical images in MHA format that, processed with the software 3DSlicer (https://www.slicer.org/ ...
The impact of breast cancer on public health is serious, and due to risk/benefit assessment, screening programs are usually restricted to women older than 49 years. Microwave imaging devices offer advantages such as non-ionizing radiation, low cost, and the ability to distinguish between cancerous and healthy tissues due to their electrical properties. Ensuring the safety of this technology is vital for its potential clinical application. To estimate the temperature increase in breast tissues from a microwave imaging scanner, cases of healthy, benign, and malignant breast tissues were analyzed using three digital models and adding two healthy breast models with varying densities. Virtual experiments were conducted using the Sim4Life software (version 7.2) with a system consisting of a horn antenna in transmission and a Vivaldi antenna in reception. Temperature increases were estimated based on the Specific Absorption Rate distributions computed for different configurations and frequencies. The highest temperature increase obtained in this analysis is lower than 60 μK in fibroglandular tissue or skin, depending on the frequency and breast density. The presence of a receiving antenna acting as a scatterer modifies the temperature increase, which is almost negligible. Microwave examination can be performed without harmful thermal effects due to electromagnetic field exposure.
