Imaging Methods Research Articles

Balloon Angioplasty and Stenting of Superior Mesenteric Artery in Chronic Intestinal Ischemia Associated with Decompensated Chronic Heart Failure (Clinical Case Report)

INTRODUCTION: Chronic intestinal ischemic syndrome (CIIS) is a pathology associated with chronic circulatory insufficiency in the visceral branches of the abdominal aorta. Duplex ultrasound can be used as the first imaging method to identify mesenteric artery stenosis. In general clinical practice, the diagnosis of CIIS is relatively rare. Expansion of application of percutaneous endovascular angioplasty and stenting of mesenteric arteries in recent years has significantly increased the effectiveness of treating patients with atherosclerotic lesion of the visceral arteries and CIIS. AIM: To demonstrate a clinical case of use of X-ray endovascular technologies in CIIS in a patient of cardiology profile. A clinical case of a patient with CIIS and severe decompensated polymorbidity is presented. Due to severity of the condition and impossibility of full examination for this reason, the patient was not included in the waiting list for heart transplantation despite the existence of indications for it. He was admitted to hospital for urgent indications. Ultrasound data revealed stenosis of the superior mesenteric artery. Balloon angioplasty and stenting of the superior mesenteric artery were successful. After discharge, the patient’s condition was satisfactory. CONCLUSION: The case demonstrates that effective treatment of even one of many comorbid diseases in a severe polymorbidity patient can improve his general condition, reduce load on the cardiovascular system and reverse edematous-ascitic syndrome.

Deep-learning based discrimination of pathologic complete response using MRI in HER2-positive and triple-negative breast cancer

Distinguishing between pathologic complete response and residual cancer after neoadjuvant chemotherapy (NAC) is crucial for treatment decisions, but the current imaging methods face challenges. To address this, we developed deep-learning models using post-NAC dynamic contrast-enhanced MRI and clinical data. A total of 852 women with human epidermal growth factor receptor 2 (HER2)-positive or triple-negative breast cancer were randomly divided into a training set (n = 724) and a validation set (n = 128). A 3D convolutional neural network model was trained on the training set and validated independently. The main models were developed using cropped MRI images, but models using uncropped whole images were also explored. The delayed-phase model demonstrated superior performance compared to the early-phase model (area under the receiver operating characteristic curve [AUC] = 0.74 vs. 0.69, P = 0.013) and the combined model integrating multiple dynamic phases and clinical data (AUC = 0.74 vs. 0.70, P = 0.022). Deep-learning models using uncropped whole images exhibited inferior performance, with AUCs ranging from 0.45 to 0.54. Further refinement and external validation are necessary for enhanced accuracy.

Macrovascular blood flow and microvascular cerebrovascular reactivity are regionally coupled in adolescence.

Cerebrovascular imaging assessments are particularly challenging in adolescent cohorts, where not all modalities are appropriate, and rapid brain maturation alters hemodynamics at both macro- and microvascular scales. In a preliminary sample of healthy adolescents (n=12, 8-25 years), we investigated relationships between 4D flow MRI-derived blood velocity and blood flow in bilateral anterior, middle, and posterior cerebral arteries and BOLD cerebrovascular reactivity in associated vascular territories. As hypothesized, higher velocities in large arteries are associated with an earlier response to a vasodilatory stimulus (cerebrovascular reactivity delay) in the downstream territory. Higher blood flow through these arteries is associated with a larger BOLD response to a vasodilatory stimulus (cerebrovascular reactivity amplitude) in the associated territory. These trends are consistent in a case study of adult moyamoya disease. In our small adolescent cohort, macrovascular-microvascular relationships for velocity/delay and flow/CVR change with age, though underlying mechanisms are unclear. Our work emphasizes the need to better characterize this key stage of human brain development, when cerebrovascular hemodynamics are changing, and standard imaging methods offer limited insight into these processes. We provide important normative data for future comparisons in pathology, where combining macro- and microvascular assessments may better help us prevent, stratify, and treat cerebrovascular disease.

Markov chain-based temporal correlation processing algorithm in reverberant environment by sparse Bayesian learning for acoustic imaging

Identifying sound sources is an important step in noise control, different sound source localization algorithms have been proposed and studied for almost a century. Most of the studies assume an ideal measurement environment, i.e., an anechoic or half-anechoic environment, but there are limited studies discussing localizing sound sources in a reverberation environment. This paper proposes an acoustic imaging method in a reverberation environment based on the sparse Bayesian learning framework. Initially, a sound propagation model was established by accounting for the multipath effects of the sound source in a reverberant environment. Based on the temporal correlation of a Markov chain, the algorithm is further improved in the sparse Bayesian learning framework. By leveraging the temporal correlation the algorithm provides, identifying the true source in a reverberant environment becomes more accurate. Numerical simulation results have shown that the proposed algorithm achieves real sound source localization and eliminates the influence of image sources in contour maps under realistic reverberant environments.

A heterogeneous ultrasound open scanner for the real-time implementation of computationally demanding imaging methods.

Ultrasound open scanners have recently boosted the development and validation of novel imaging techniques. They are usually split into hardware- or software-oriented systems, depending on whether they process the echo data using embedded FPGAs/DSPs or a GPU on a host PC. The goal of this work was to realize a high-performance heterogeneous open scanner capable of leveraging the strengths of both hardware and software-oriented systems. The elaboration power of the 256-channel ultrasound advanced open platform (ULA-OP 256) was further enhanced by embedding a compact co-processing GPU system-on-module (SoM). By carefully avoiding latencies and overheads through low-level optimization work, an efficient PCIe communication interface was established between the GPU and the processing devices onboard the ULA-OP 256. As a proof of concept of the enhanced system, the high frame rate color flow mapping technique was implemented on the GPU SoM and tested. Compared to a previous DSP-based implementation, higher real-time frame rates were achieved together with unprecedented flexibility in setting crucial parameters such as the ensemble length (EL). For example, by setting EL=64 and a continuous-time high-pass filter, the flow was investigated with high temporal and spatial resolution in the femoral vein bifurcation (frame rate = 1.1 kHz) and carotid artery bulb (4.3 kHz), highlighting the flow disturbances due to valve aperture and secondary velocity components, respectively. The results of this work promote the development of other computational-expensive processing algorithms in real-time and may inspire the next generation of ultrasound high-performance heterogeneous scanners.

Fourier Ptychographic Coherent Anti-Stokes Raman Scattering Microscopy with Point-Scanning for Super-Resolution Imaging.

Fourier ptychography (FP) is a high resolution wide-field imaging method based on the extended aperture in the Fourier space, which is synthesized from raw images with varying illumination angles. If FP is extended to coherent nonlinear optical imaging, the resolution could be further improved due to the increase of the cutoff frequency of the synthesized coherent optical transfer function (C-OTF) with respect to the order of nonlinear optical processes. However, there is a fundamental conflict between wide-field FP and nonlinear optical imaging, whereby the nonlinear optical imaging typically requires a focused excitation laser beam with high power density. To tackle the problem, in this work, a unique point-scanning FP (PS-FP) method is presented for super-resolution nonlinear optical imaging, in which the nonlinear optical signal is obtained by using focused laser beam, while the conventional FP algorithm can still be used to retrieve the super-resolution image. PS-FP coherent anti-Stokes Raman scattering (PS-FP-CARS) imaging on a variety of samples, where a 1.8-fold expansion of the OTF is achieved experimentally for enhancing vibrational imaging. Further theoretical calculation shows that the C-OTF of PS-FP higher-order CARS (PS-FP-HO-CARS) can be expanded up to ≈4.9-fold, thereby improving the spatial resolution by ≈3-fold in comparison with conventional point-scanning CARS with under tightly focused beams. The generality of PS-FP method developed in this work can be adapted to other coherent nonlinear optical imaging modalities for super-resolution imaging in tissue and cells.

Approximate Observation Weighted ℓ2/3 SAR Imaging under Compressed Sensing.

Compressed Sensing SAR Imaging is based on an accurate observation matrix. As the observed scene enlarges, the resource consumption of the method increases exponentially. In this paper, we propose a weighted ℓ2/3-norm regularization SAR imaging method based on approximate observation. Initially, to address the issues brought by the precise observation model, we employ an approximate observation operator based on the Chirp Scaling Algorithm as a substitute. Existing approximate observation models typically utilize ℓq(q = 1, 1/2)-norm regularization for sparse constraints in imaging. However, these models are not sufficiently effective in terms of sparsity and imaging detail. Finally, to overcome the aforementioned issues, we apply ℓ2/3 regularization, which aligns with the natural image gradient distribution, and further constrain it using a weighted matrix. This method enhances the sparsity of the algorithm and balances the detail insufficiency caused by the penalty term. Experimental results demonstrate the excellent performance of the proposed method.

The Role of Nuclear Medicine in the Diagnostic Work-Up of Athletes: An Essential Guide for the Sports Cardiologist.

Athletes with heart disease are at increased risk of malignant ventricular arrhythmias and sudden cardiac death compared to their sedentary counterparts. When athletes have symptoms or abnormal findings at preparticipation screenings, a precise diagnosis by differentiating physiological features of the athlete's heart from pathological signs of cardiac disease is as important as it is challenging. While traditional imaging methods such as echocardiography, cardiac magnetic resonance, and computed tomography are commonly employed, nuclear medicine offers unique advantages, especially in scenarios requiring stress-based functional evaluation. This article reviews the use of nuclear medicine techniques in the diagnostic work-up of athletes with suspected cardiac diseases by highlighting their ability to investigate myocardial perfusion, metabolism, and innervation. The article discusses the application of single photon emission computed tomography (SPECT) and positron emission tomography (PET) using radiotracers such as [99mTc]MIBI, [99mTc]HDP, [18F]FDG, and [123I]MIBG. Several clinical scenarios are explored, including athletes with coronary atherosclerosis, congenital coronary anomalies, ventricular arrhythmias, and non-ischemic myocardial scars. Radiation concerns are addressed, highlighting that modern SPECT and PET equipment significantly reduces radiation doses, making these techniques safer for young athletes. We conclude that, despite being underutilized, nuclear medicine provides unique opportunities for accurate diagnosis and effective management of cardiac diseases in athletes.

Study on Nondestructive Detection Imaging Method of Log Knot Based on Judging the Shortest Path of Stress Wave Propagation

Study on Nondestructive Detection Imaging Method of Log Knot Based on Judging the Shortest Path of Stress Wave Propagation

Improved Sensitivity in a Modified Berkeley Red Sensor of Transmembrane Potential.

Voltage imaging is an important complement to traditional methods for probing cellular physiology, such as electrode-based patch clamp techniques. Unlike the related Ca2+ imaging, voltage imaging provides a direct visualization of bioelectricity changes. We have been exploring the use of sulfonated silicon rhodamine dyes (Berkeley Red Sensor of Transmembrane potential, BeRST) for voltage imaging. In this study, we explore the effect of converting BeRST to diEt BeRST, by replacing the dimethyl aniline of BeRST with a diethyl aniline group. The new dye, diEt BeRST, has a voltage sensitivity of 40% ΔF/F per 100 mV, a 33% increase compared to the original BeRST dye, which has a sensitivity of 30% ΔF/F per 100 mV. In neurons, the cellular brightness of diEt BeRST is about 20% as bright as that of BeRST, which may be due to the lower solubility of diEt BeRST (300 μM) compared to that of BeRST (800 μM). Despite this lower cellular brightness, diEt BeRST is able to record spontaneous and evoked action potentials from multiple neurons simultaneously and in single trials. Far-red excitation and emission profiles enable diEt BeRST to be used alongside existing fluorescent indicators of cellular physiology, like Ca2+-sensitive Oregon Green BAPTA. In hippocampal neurons, simultaneous voltage and Ca2+ imaging reveals neuronal spiking patterns and frequencies that cannot be resolved with traditional Ca2+ imaging methods. This study represents a first step toward describing the structural features that define voltage sensitivity and brightness in silicon rhodamine-based BeRST indicators.

A Deep Learning Inversion Method for Airborne Time-Domain Electromagnetic Data Using Convolutional Neural Network

Due to the high detection efficiency of the airborne time-domain electromagnetic method, it can quickly collect electromagnetic response data for large area-wide regions, but it also brings great challenges to the inversion interpretation of the data because there are numerous survey data that need to be inverted. Conventional optimal inversion and fast imaging methods still take a long time to obtain conductivity and depth information, which affect the efficiency of real-time data interpretation. In this paper, we present a deep learning inversion method that can be used to solve the fast inversion problem of airborne time-domain electromagnetic data; the method uses a one-dimensional convolutional neural network. The network structure consists of two parts containing different numbers of convolutional and pooling layers. The training sample dataset was generated via two ways of constructing geoelectric models through forward modelling. To check the effectiveness of our deep learning inversion strategy, we tested it on synthetic data and two types of survey data. The experimental results show that this inversion method is effective and that it can be applied to airborne time-domain electromagnetic data collected using different observation systems. The proposed inversion method can obtain better inversion results for both simple and complex stratigraphic structures and requires significantly less computation time compared to conventional optimal inversion methods.

Simultaneous In Vivo Imaging of Neutrophil Elastase and Oxidative Stress in Atherosclerotic Plaques Using a Unimolecular Photoacoustic Probe

AbstractAtherosclerosis, a major global health concern with high morbidity and mortality rates, involves complex interactions of chronic inflammation, oxidative stress, and proteolytic enzymes. Conventional imaging methods struggle to capture the dynamic biochemical processes in atherosclerotic plaques. Here, we introduce a novel unimolecular photoacoustic probe (UMAPP) designed with specific binding sites for neutrophil elastase (NE) and the redox pair O2⋅−/GSH, enabling real‐time monitoring of oxidative stress and activated neutrophils in plaques. UMAPP, comprising a boron‐dipyrromethene (BODIPY) core linked to a hydrophilic NE‐cleavable tetrapeptide and dual oxidative stress‐responsive catechol moieties, facilitates NE‐mediated modulation of photoinduced electron transfer impacting photoacoustic intensity at 685 nm (PA685). Furthermore, oxidation and reduction of catechol groups by O2⋅− and GSH induce reversible, ratiometric changes in the photoacoustic spectrum (PA745/PA685 ratio). Initial UMAPP applications successfully distinguished atherosclerotic and healthy mice, evaluated pneumonia‘s effect on plaque composition and verified the probe‘s effectiveness in drug‐treatment studies by detecting molecular alterations before visible histopathological changes. The integrated molecular imaging capabilities of UMAPP offer promising advancements in atherosclerosis diagnosis and management, enabling early and accurate identification of vulnerable plaques.

A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient Classification.

Breast cancer is a prominent cause of death among women worldwide. Infrared thermography, due to its cost-effectiveness and non-ionizing radiation, has emerged as a promising tool for early breast cancer diagnosis. This article presents a hybrid model approach for breast cancer detection using thermography images, designed to process and classify these images into healthy or cancerous categories, thus supporting disease diagnosis. Multiple pre-trained convolutional neural networks are employed for image feature extraction, and feature filter methods are proposed for feature selection, with diverse classifiers utilized for image classification. Evaluating the DRM-IR test set revealed that the combination of ResNet34, Chi-square ( ) filter, and SVM classifier demonstrated superior performance, achieving the highest accuracy at . Furthermore, the highest accuracy improvement obtained was when using the SVM classifier and Chi-square filter compared to regular convolutional neural networks. The results confirmed that the proposed method, with its high accuracy and lightweight model, outperforms state-of-the-art breast cancer detection from thermography image methods, making it a good choice for computer-aided diagnosis.

Dataset of human skin and fingernails images for non-invasive haemoglobin level assessment

Anaemia, a decrease in total concentration of haemoglobin (Hb) in blood, affects substantial percentage of the population worldwide. Currently, the gold standard for determining the Hb level is the invasive analysis of venous blood. Yet, more and more research groups demonstrate the possibility of non-invasive Hb assessment using white light imaging of tissue sites where Hb is the main chromophore, in particular, fingernails. Despite the promising declarations, non-invasive Hb assessment via RGB-imaging is still poorly used in practice. The main reason is the difficulty in establishing the true accuracy of the methods presented in different works since they are tested on private datasets collected under different experimental conditions. Here we present an open dataset containing RGB images of skin and fingernails for patients with a known level of Hb, thus providing a single benchmark for researchers and engineers in the field, aimed at fostering translation of non-invasive imaging methods to the bedside.

Autonomous multicolor bioluminescence imaging in bacteria, mammalian, and plant hosts

Bioluminescence imaging has become a valuable tool in biological research, offering several advantages over fluorescence-based techniques, including the absence of phototoxicity and photobleaching, along with a higher signal-to-noise ratio. Common bioluminescence imaging methods often require the addition of an external chemical substrate (luciferin), which can result in a decrease in luminescence intensity over time and limit prolonged observations. Since the bacterial bioluminescence system is genetically encoded for luciferase-luciferin production, it enables autonomous bioluminescence (auto-bioluminescence) imaging. However, its application to multiple reporters is restricted due to a limited range of color variants. Here, we report five-color auto-bioluminescence system named Nano-lanternX (NLX), which can be expressed in bacterial, mammalian, and plant hosts, thereby enabling auto-bioluminescence in various living organisms. Utilizing spectral unmixing, we achieved the successful observation of multicolor auto-bioluminescence, enabling detailed single-cell imaging across both bacterial and mammalian cells. We have also expanded the applications of the NLX system, such as multiplexed auto-bioluminescence imaging for gene expression, protein localization, and dynamics of biomolecules within living mammalian cells.

Quantitative phase imaging with two in-line holograms.

Quantitative phase imaging (QPI) has emerged as a practical technique for acquiring structural information from phase objects. Digital holography can realize phase detection, but it is limited by a spatial bandwidth product or affected by the overlap of conjugate images. The phase retrieval algorithm serves as an effective tool for QPI dealing with intensity patterns. Traditional phase retrieval algorithms heavily rely on strong support constraints or high data redundancy to accurately reconstruct the sample image. However, in single-frame phase retrieval algorithms, the precise acquisition of support constraints is notably challenging. The multiple-measurement spends much time on data acquisition and is unsuitable for dynamic sample observation. In this paper, we propose a novel, to the best of our knowledge, quantitative phase imaging method that utilizes only two in-line holograms. We have developed a phase retrieval algorithm based on ptychography, which eliminates twin-image and separates illumination background. The proposed method achieves high data utilization efficiency and can be employed for dynamic imaging.

Lung imaging methods: indications, strengths and limitations.

Imaging methods are fundamental tools to detect and diagnose lung diseases, monitor their treatment and detect possible complications. Each modality, starting from classical chest radiographs and computed tomography, as well as the ever more popular and easily available thoracic ultrasound, magnetic resonance imaging and nuclear medicine methods, and new techniques such as photon counting computed tomography, radiomics and application of artificial intelligence, has its strong and weak points, which we should be familiar with to properly choose between the methods and interpret their results. In this review, we present the indications, strengths and main limitations of methods for chest imaging.

Bayesian self-calibration and imaging in very long baseline interferometry

Context. Self-calibration methods with the CLEAN algorithm have been widely employed in very long baseline interferometry (VLBI) data processing in order to correct antenna-based amplitude and phase corruptions present in the data. However, human interaction during the conventional CLEAN self-calibration process can impose a strong effective prior, which in turn may produce artifacts within the final image and hinder the reproducibility of final results. Aims. In this work, we aim to demonstrate a combined self-calibration and imaging method for VLBI data in a Bayesian inference framework. The method corrects for amplitude and phase gains for each antenna and polarization mode by inferring the temporal correlation of the gain solutions. Methods. We use Stokes I data of M87 taken with the Very Long Baseline Array (VLBA) at43 GHz, pre-calibrated using the rPICARD CASA-based pipeline. For antenna-based gain calibration and imaging, we use the Bayesian imaging software resolve. To estimate gain and image uncertainties, we use a variational inference method. Results. We obtain a high-resolution M87 Stokes I image at 43 GHz in conjunction with antenna-based gain solutions using our Bayesian self-calibration and imaging method. The core with counter-jet structure is better resolved, and extended jet emission is better described compared to the CLEAN reconstruction. Furthermore, uncertainty estimation of the image and antenna-based gains allows us to quantify the reliability of the result. Conclusions. Our Bayesian self-calibration and imaging method is able to reconstruct robust and reproducible Stokes I images and gain solutions with uncertainty estimation by taking into account the uncertainty information in the data.

Automated multiple sclerosis progression rate computation of a patient from 2D FLAIR images with Rayleigh-Weibull-Fuzzy imaging and augmented morphing method

Multiple sclerosis (MS) is a neurological demyelinating disorder affecting brain and spinal cord by attacking the myelin sheaths of nerves. Estimation of the volumetric changes in MS lesions is a challenging and specialized task which is executed and judged by medical experts. The change in the volume of the lesions provides crucial information on MS progression or regression by comparing the magnetic resonance images (MRI) taken in successive scans. However, visual comparison of the images, even with an expert eye, would not always lead to a conclusive decision nor a consensus on progression or regression. Therefore, we present an automated expert system for estimating MS progression rate by automatic lesion segmentation and volume estimation using two-dimensional MRIs, which is also adaptable to various parameters, slice thickness and increment. A clinical dataset is specially formed for this research which contains three sets of 135 MR images of an MS patient generated within approximately 23- and 6-month periods consecutively with identical device parameters. The lesions are segmented by a novel Rayleigh-Weibull-Fuzzy (RWF) imaging method based on the Nakagami distribution and specialized fuzzy 2-means. Subsequently, the segmentation module is trained to fit the ground truths images created by experts to achieve the highest dice score possible for a total number of 56 images containing lesions, which is found as 93.76%. Afterwards, several imaginary image sequences are generated by augmented linear and nonlinear morphing for re-segmentation of imaginary lesions by RWF. Finally, we estimated the volumetric change between the first two MRI sequences to adjust the morphing module and to predict the progression rate of the lesions in time. The framework automatically selected the highest accuracy, which is 99.9% in the training session and estimated the progression rate in the testing phase with 99.69% accuracy, which are not achievable without augmented morphing methodology. For the first time in the literature, an automated framework could estimate the MS progression rate from the raw MR images, which is also the main innovation of this paper and the outputs would be beneficial for the experts working on this field.

Near-field terahertz electro-optical imaging based on a polarization image sensor

Abstract This paper presents a hyperspectral microscopy system that offers two-dimensional (2D) measurement of the spectral phase and amplitude information of terahertz (THz) radiation without the need for raster scanning. To achieve this, a new THz imaging method is introduced, wherein the distribution of the THz electric field is spatially measured using the electro-optic effect with a commercial polarization image sensor. This method enables the direct measurement of polarization components, eliminating the need for the polarization optics usually required in conventional electro-optical imaging. The performance of this imaging method is compared with a conventional 2D imaging system based on a standard visible camera. Finally, the sub-wavelength resolution capabilities of this new sensor are demonstrated by imaging a sample in the near field.