Image Reconstruction - Science topic

Human cerebral blood vessels are highly intricate and significantly contribute to brain function support. In the surgical process of these vessels, the neurosurgeons will basically employ magnetic resonance imaging (MRI) as an imaging media to understand the location of the disorder, the anatomical position of vessels, and a guide in the surgical p...

Objective. Positronium lifetime tomography (PLT) is an emerging modality that aims to reconstruct 3D images of positronium lifetime in humans and animals in vivo. The lifetime of ortho-positronium can be influenced by the microstructure and the concentration of bio-active molecules in tissue, providing valuable information for better understanding...

Magnetic Resonance Imaging (MRI) is a technology for non-invasive imaging of anatomical features in detail. It can help in functional analysis of organs of a specimen but it is very costly. In this work, methods for (1) virtual three-dimensional (3D) reconstruction from a single sequence of two-dimensional (2D) slices of MR images of a human spine...

Objective In cone-beam computed tomography (CBCT), voxel resolution and size directly affect image quality. This study aimed to analyze the effect of voxel resolution on the linear and volumetric measurements of tooth and pulp in three-dimensional images obtained with different voxel sizes and to test the repeatability of these measurements. Mater...

Deep learning (DL) models are effective in leveraging latent representations from MR data, emerging as state-of-the-art solutions for accelerated MRI reconstruction. However, challenges arise due to the inherent uncertainties associated with undersampling in k-space, coupled with the over- or under-parameterized and opaque nature of DL models. Addr...

With the popularity of High Dynamic Range (HDR) display technology, consumer demand for HDR images is increasing. Since HDR cameras are expensive, reconstructing High Dynamic Range (HDR) images from traditional Low Dynamic Range (LDR) images is crucial. However, existing HDR image reconstruction algorithms often fail to recover fine details and do...

With the exponential growth of digital imagery, some novel techniques for visual information reconstruction are needed since the development of high-speed and precision methods is still an open problem for several applications such as medical diagnosis, satellite imaging, and general image processing. This paper proposes a new hybrid approach for r...

Colonoscopy, recognised as the gold standard for diagnosing and treating colorectal cancer, faces limitations that may result in overlooking some colonic regions. This can lead to missed lesions and interval cancer, leading to incomplete treatment. Addressing this challenge, the authors present a novel Assistive Imaging Device for Colon Map Reconst...

The point-by point 3D scanning strategy adopted in Stimulated Emission Depletion Microscopy (STED) is time-consuming. The 3D scanning can be replaced with a 2D scanning in the non-diffracting Bessel-Bessel STED (BB-STED). In order to extract the excited emitters' axial information in BB-STED, we propose to encode axial information by using a detect...

Multiview 3-D reconstruction holds considerable promise across a wide applications in social manufacturing. Conducting in-depth research on precise and robust multiview 3-D reconstruction holds the potential to significantly empower the domain of social manufacturing. Recently, there has been a burgeoning interest in the domain of neural implicit s...

Quantitative microwave imaging (MWI) involves solving the inverse scattering problem (ISP), which is characterized by nonlinearity and ill-posedness. To address the challenges posed by ISP, iterative linearization techniques have been introduced alongside regularization procedures. Born Iterative Method (BIM) and Distorted Born Iterative Method (DB...

Banknotes may be damaged during various events, such as floods, fires, insect infestations, and mechanical or manual shredding. Disaster victims might need to perform banknote reconstruction when applying for currency exchange, or investigative agencies might need to conduct such reconstruction during evidence collection. When the number of banknot...

In ghost imaging (GI) techniques, if the illumination patterns used in the reconstruction algorithm do not match that incident on the object's surface, the reconstructed image will be blurred. Here, we propose a computational GI system based on Bessel beams (Bessel-GI). Owing to the diffraction-free property of Bessel beams, Bessel-GI can image obj...

Long-wave infrared (LWIR) spectral imaging plays a critical role in various applications such as gas monitoring, mineral exploration, and fire detection. Recent advancements in computational spectral imaging, powered by advanced algorithms, have enabled the acquisition of high-quality spectral images in real time, such as with the Uncooled Snapshot...

Purpose To develop a robust single breath‐hold approach for volumetric lung imaging at 0.55T. Method A balanced‐SSFP (bSSFP) pulse sequence with 3D stack‐of‐spiral (SoS) out‐in trajectory for volumetric lung imaging at 0.55T was implemented. With 2.7× undersampling, the pulse sequence enables imaging during a 17‐s breath‐hold. Image reconstruction...

This paper presents an energy optimization approach to applying electrical impedance tomography (EIT) for medical diagnostics, particularly in detecting lung diseases. The designed Lung Electrical Tomography System (LETS) incorporates 102 electrodes and advanced image reconstruction algorithms. Energy efficiency is achieved through the use of moder...

Interferometric closure invariants, constructed from triangular loops of mixed Fourier components, capture calibration-independent information on source morphology. While a complete set of closure invariants is directly obtainable from measured visibilities, the inverse transformation from closure invariants to the source intensity distribution is...

Objectives To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) in low-dose abdominal computed tomography (CT) imaging compared with hybrid iterative reconstruction (HIR) and conventional deep learning reconstruction (cDLR) algorithms. Methods We retrospectively analyzed abdominal CT scans performed using a low-d...

Hyperspectral image (HSI) reconstruction is a critical and indispensable step in spectral compressive imaging (CASSI) systems and directly affects our ability to capture high-quality images in dynamic environments. Recent research has increasingly focused on deep unfolding frameworks for HSI reconstruction, showing notable progress. However, these...

Most solar hard X-ray (HXR) imagers in the past and current solar missions obtain X-ray images via Fourier transform imaging technology, which requires proper imaging algorithms to reconstruct images from spatially-modulated or temporally-modulated signals. A variety of algorithms have been developed during the last 50 years for the characteristics...

Spike cameras, as an innovative neuromorphic camera that captures scenes with the 0-1 bit stream at 40 kHz, are increasingly employed for the 3D reconstruction task via Neural Radiance Fields (NeRF) or 3D Gaussian Splatting (3DGS). Previous spike-based 3D reconstruction approaches often employ a casecased pipeline: starting with high-quality image...

To investigate the application advantages of dual‐low technology (low radiation dose and low contrast agent dose) in deep learning image reconstruction (DLIR) compared to the adaptive statistical iterative reconstruction‐Veo (ASIR‐V) standard protocol when combing coronary computed tomography angiography (CCTA) and abdominal computed tomography ang...

The research on sand-dust image enhancement usually follows the developmental dynamics of image haze removal and is transitioning from traditional methods to end-to-end (e2e) learning-based algorithms. However, the more complex degradation of sandstorm images inevitably increases the potential risks in e2e algorithms, leading to unstable model perf...

Purpose To evaluate the feasibility of a high-precision single-shot fast spin–echo (SS-FSE) sequence using the deep learning-based Precise IQ Engine (PIQE) algorithm in comparison with standard SS-FSE for T2-weighted MR imaging of the abdomen, and to compare the image quality with a multi-shot (MS)-FSE sequence using the PIQE algorithm. Methods Th...

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Thro...

Neural fields or implicit neural representations (INRs) have attracted significant attention in machine learning and signal processing due to their efficient continuous representation of images and 3D volumes. In this work, we build on INRs and introduce a coordinate-based local processing framework for solving imaging inverse problems, termed LoFi...

Handwriting Brain-Computer Interfaces (BCIs) provides a promising communication avenue for individuals with paralysis. While English-based handwriting BCIs have achieved rapid typewriting with 26 lowercase letters (mostly containing one stroke each), it is difficult to extend to complex characters, especially those with multiple strokes and large c...

We investigate the construction of gradient-guided conditional diffusion models for reconstructing private images, focusing on the adversarial interplay between differential privacy noise and the denoising capabilities of diffusion models. While current gradient-based reconstruction methods struggle with high-resolution images due to computational...

Photoacoustic imaging (PAI) represents an innovative biomedical imaging modality that harnesses the advantages of optical resolution and acoustic penetration depth while ensuring enhanced safety. Despite its promising potential across a diverse array of preclinical and clinical applications, the clinical implementation of PAI faces significant chal...

Detecting the structural integrity of composite material used in an airframe includes passive non-destructive testing (NDT) and active electrical impendence tomography (EIT). EIT benefits enhance real-time response and accuracy but require pragmatic approaches to analysis of electrode errors from faulty or non-responsive readings. Non-functional co...

Three-gamma imaging is attracting attention as a futuristic diagnostic imaging method that surpasses positron emission tomography (PET). Its conceptual key is using b+-c nuclides that simultaneously emit a prompt gamma ray with the positron decay. In this review, we have categorized the utilizations of prompt gamma rays into three categories: multi...

3D reconstruction from multi-view images is considered as a longstanding problem in computer vision and graphics. In order to achieve high-fidelity geometry and appearance of 3D scenes, this paper proposes a novel geometric object learning method for multi-view reconstruction with fuzzy set theory . We establish a new neural 3D reconstruction th...

Renal glomeruli have traditionally been studied by micrometer-scale optical microscopy to interrogate overall physiology or molecular distributions and by nanoscale electron microscopy to interrogate the ultrastructure of thin sections. While these approaches are powerful, they have been limited in their ability to obtain detailed views of the glom...

Magnetic particle imaging is a tracer-based tomographic imaging technique that allows the concentration of magnetic nanoparticles to be determined with high spatio-temporal resolution. To reconstruct an image of the tracer concentration, the magnetization dynamics of the particles must be accurately modeled. A popular ensemble model is based on sol...

Electromagnetic interference (EMI) can disrupt the operation and reliability of electronic devices, thus effective shielding and detection of EMI sources are crucial to mitigate these issues. However, locating EMI sources in shielding enclosures is a challenging task due to the complex electromagnetic (EM) environment. Compared to conventional meth...

Boron Neutron Capture Therapy (BNCT) is expected to be a promising radiation therapy for cancer, and research on various issues associated with it is still in progress. One of the unsolved issues is the construction of a system for observing the effect of the treatment (local boron dose) in real time under high-background circumstances. Thus, we se...

Radio tomographic imaging (RTI) is a device-free sensing technology that can image the radio frequency (RF) attenuation of physical objects in the environment. RTI uses received signal strength (RSS) information from a wireless communication network (WCN) to perform image reconstruction. However it requires a dense WCN consisting of a large number...

Low-light image enhancement aims to improve the visual quality of images captured under poor illumination. However, enhancing low-light images often introduces image artifacts, color bias, and low SNR. In this work, we propose AnlightenDiff, an anchoring diffusion model for low light image enhancement. Diffusion models can enhance the low light ima...

Deep learning-based lane line detection has garnered substantial success in common scenarios. However, detecting lane lines under conditions of severe occlusion, where visual cues are largely absent, remains a considerable challenge. To address this issue, we propose a cutting-edge strategy that utilizes an enhanced Vision Transformer (ViT) for the...

Objectives To assess the performance of the “dark blood” (DB) technique, deep-learning reconstruction (DLR), and their combination on aortic images for large-vessel vasculitis (LVV) patients. Materials and methods Fifty patients diagnosed with LVV scheduled for aortic computed tomography angiography (CTA) were prospectively recruited in a single c...

Damage imaging algorithms are crucial for evaluating the condition of critical structures such as adhesively bonded joints. Particularly during service, baseline-free structural health monitoring is essential for robust and real-time evaluation. This paper proposes and investigates the impact of the shape of the damage intensity distribution and da...

With the recent development of smart farms, researchers are very interested in such fields. In particular, the field of disease diagnosis is the most important factor. Disease diagnosis belongs to the field of anomaly detection and aims to distinguish whether plants or fruits are normal or abnormal. The problem can be solved by binary or multi-clas...

To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents To meet the increasing demand for rapid and efficient evaluation of tunnel blasting quality, this study presents a comprehensive review of the current state of the art in tunnel blasting evaluation, organized into five key areas: Blastin...

This study aimed to investigate the potential benefit of ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) angiography in visualization of abdominal arteries in comparison to standard-reconstruction (SR) images of virtual monoenergetic images (VMI) at low kiloelectron volt (keV). We prospectively included 47 and 47 participants to un...

In a multi-view 3D reconstruction problem, the task is to infer the 3D shape of an object from various images taken from different viewpoints. Transformer-based networks have demonstrated their ability to achieve high performance in such problems, but they face challenges in identifying the optimal way to merge the different views in order to estim...

Objectives To investigate the influence of kernels and iterative reconstructions on pericoronary adipose tissue (PCAT) attenuation in coronary CT angiography (CCTA). Materials and methods Twenty otherwise healthy subjects (16 females; median age 52 years) with atypical chest pain, low risk of coronary artery disease (CAD), and without CAD in photo...

With the advent of the 6G era, the number of IoT devices has experienced explosive growth, leading to the generation of massive amounts of data at the network edge. Semantic communication, as an innovative solution to handling this data deluge, can significantly enhance communication efficiency. However, the limited storage and computational resour...

Consumer-grade Electroencephalography (EEG) devices equipped with few electrodes often suffer from low spatial resolution, hindering the accurate capture of intricate brain activity patterns. To address this issue, we propose MASER, a novel super-resolution approach for EEG recording. In MASER, we design the eMamba block for extracting EEG features...

Boron Neutron Capture Therapy (BNCT) is an emerging radiation treatment for cancer, and its challenges are being explored. Systems capable of capturing real-time observations of this treatment’s effectiveness, particularly BNCT-SPECT methods that measure gamma rays emitted instantaneously from outside the body during nuclear reactions and that reco...

Scene text editing aims to replace the source text with the target text while preserving the original background. Its practical applications span various domains, such as data generation and privacy protection, highlighting its increasing importance in recent years. In this study, we propose a novel Scene Text Editing network with Explicitly-decoup...

Motion correction aims to prevent motion artefacts which may be caused by respiration, heartbeat, or head movements for example. In a preliminary step, the measured data is divided in gates corresponding to motion states, and displacement maps from a reference state to each motion state are estimated. One common technique to perform motion correcti...

With the help of traffic lights and street cameras, optical camera communication (OCC) can be adopted in Internet of Vehicles (IoV) applications to realize communication between vehicles and roadside units. However, the encoded light emitted by these OCC transmitters (LED infrastructures on the roadside and/or LED-based headlamps embedded in cars)...

Objective. Magnetic particle imaging (MPI) is a tracer-based medical imaging modality with great potential due to its high sensitivity, high spatiotemporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem, which the use of regularization methods can address. Multi-contrast MPI reconst...

With the continuous development of near-field radar microwave imaging technology, low-rank or compressed sensing (CS) methods have shown great potential in solving high-resolution microwave imaging under sparse aperture. However, traditional low-rank or CS methods are restricted to handling random sampling data. When specific random sampling condit...

Spectral super-resolution (SSR) aims to restore a hyperspectral image (HSI) from a single RGB image, in which deep learning has shown impressive performance. However, the majority of the existing deep-learning-based SSR methods inadequately address the modeling of spatial–spectral features in HSI. That is to say, they only sufficiently capture eith...

Computed Tomography (CT) is an essential non-destructive three dimensional imaging modality used in medicine, security screening, and inspection of manufactured components. Typical CT data acquisition entails the collection of a thousand or more projections through the object under investigation through a range of angles covering one hundred eighty...

This study investigated the potential of the generative adversarial neural network (cGAN) image reconstruction in industrial electrical capacitance tomography. The image reconstruction quality was examined using image patterns typical for a two-phase flow. The training dataset was prepared by generating images of random test objects and simulating...

This paper presents a novel image reconstruction pipeline for three-gamma (3-γ) positron emission tomography (PET) aimed at improving spatial resolution and reducing noise in nuclear medicine. The proposed Direct3γ pipeline addresses the inherent challenges in 3-γ PET systems, such as detector imperfections and uncertainty in photon interaction poi...

Deep learning (DL) methods have been extensively applied to various image recovery problems, including magnetic resonance imaging (MRI) and computed tomography (CT) reconstruction. Beyond supervised models, other approaches have been recently explored including two key recent schemes: Deep Image Prior (DIP) that is an unsupervised scan-adaptive met...

In this work, we propose to leverage a deep-learning (DL) based reconstruction framework for high quality Swept-Source Optical Coherence Tomography (SS-OCT) images, by incorporating wavelength ({\lambda}) space interferometric fringes. Generally, the SS-OCT captured fringe is linear in wavelength space and if Inverse Discrete Fourier Transform (IDF...

Multispectral and multimodal unstained blood smear images are obtained and evaluated to offer computer‐assisted automated diagnostic evidence for malaria. However, these images suffer from uneven lighting, contrast variability, and local luminosity due to the acquisition system. This limitation significantly impacts the diagnostic process and its o...