Imaging Approach Research Articles

Myosteatosis: diagnostic significance and assessment by imaging approaches.

Myosteatosis has emerged as an important concept in muscle health as it is associated with an increased risk of adverse health outcomes, a higher rate of complications, and increased mortality associated with ageing, chronic systemic and neuromuscular diseases, cancer, metabolic syndromes, degenerative events, and trauma. Myosteatosis involves ectopic infiltration of fat into skeletal muscle, and it exhibits a negative correlation with muscle mass, strength, and mobility representing a contributing factor to decreased muscle quality. While myosteatosis serves as an additional biomarker for sarcopenia, cachexia, and metabolic syndromes, it is not synonymous with sarcopenia. Myosteatosis induces proinflammatory changes that contribute to decreased muscle function, compromise mitochondrial function, and increase inflammatory response in muscles. Imaging techniques such as computed tomography (CT), particularly opportunistic abdominal CT scans, and magnetic resonance imaging (MRI) or magnetic resonance spectroscopy (MRS), have been used in both clinical practice and research. And in recent years, ultrasound has emerged as a promising bedside tool for measuring changes in muscle tissue. Various techniques, including CT-based muscle attenuation (MA) and intermuscular adipose tissue (IMAT) quantification, MRI-based proton density fat fraction (PDFF) and T1-T2 mapping, and musculoskeletal ultrasound (MSUS)-based echo intensity (EI) and shear wave elastography (SWE), are accessible in clinical practice and can be used as adjunct biomarkers of myosteatosis to assess various debilitating muscle health conditions. However, a stan¬dard definition of myosteatosis with a thorough understanding of the pathophysiological mechanisms, and a consensus in assessment methods and clinical outcomes has not yet been established. Recent developments in image acquisition and quantification have attempted to develop an appropriate muscle quality index for the assessment of myosteatosis. Additionally, emerging studies on artificial intelligence (AI) may provide further insights into quantification and automated assessment, including MRS analysis. In this review, we discuss the pathophysiological aspects of myosteatosis, all the current imaging techniques and recent advances in imaging assessment as potential biomarkers of myosteatosis, and the most common clinical conditions involved.

Working memory related functional connectivity in adult ADHD and its amenability to training: A randomized controlled trial

BackgroundWorking memory (WM) deficits are among the most prominent cognitive impairments in attention deficit hyperactivity disorder (ADHD). While functional connectivity is a prevailing approach in brain imaging of ADHD, alterations in WM-related functional brain networks and their malleability by cognitive training are not well known. We examined whole-brain functional connectivity differences between adults with and without ADHD during n-back WM tasks and rest at pretest, as well as the effects of WM training on functional and structural brain connectivity in the ADHD group. MethodsForty-two adults with ADHD and 36 neurotypical controls performed visuospatial and verbal n-back tasks during functional magnetic resonance imaging (fMRI). In addition, seven-minute resting state fMRI data and diffusion-weighted MR images were collected from all participants. The adults with ADHD continued into a 5-week randomized controlled WM training trial (experimental group training on a dual n-back task, n = 21; active control group training on Bejeweled II video game, n = 21), followed by a posttraining MRI. Brain connectivity was examined with Network-Based Statistic. ResultsAt the pretest, adults with ADHD had decreased functional connectivity compared with the neurotypical controls during both n-back tasks in networks encompassing fronto-parietal, temporal, occipital, cerebellar, and subcortical brain regions. Furthermore, WM-related connectivity in widespread networks was associated with performance accuracy in a continuous performance test. Regarding resting state connectivity, no group differences or associations with task performance were observed. WM training did not modulate functional or structural connectivity compared with the active controls. ConclusionOur results indicate large-scale abnormalities in functional brain networks underlying deficits in verbal and visuospatial WM commonly faced in ADHD. Training-induced plasticity in these networks may be limited

Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors

Reconstructing images from the Event Horizon Telescope (EHT) observations of M87*, the supermassive black hole at the center of the galaxy M87, depends on a prior to impose desired image statistics. However, given the impossibility of directly observing black holes, there is no clear choice for a prior. We present a framework for flexibly designing a range of priors, each bringing different biases to the image reconstruction. These priors can be weak (e.g., impose only basic natural-image statistics) or strong (e.g., impose assumptions of black hole structure). Our framework uses Bayesian inference with score-based priors, which are data-driven priors arising from a deep generative model that can learn complicated image distributions. Using our Bayesian imaging approach with sophisticated data-driven priors, we can assess how visual features and uncertainty of reconstructed images change depending on the prior. In addition to simulated data, we image the real EHT M87* data and discuss how recovered features are influenced by the choice of prior.

Novel features of Drosophila hematopoiesis uncovered by long-term live imaging

Stem cells are subject to continuous regulation to ensure that the correct balance between stem cell differentiation and self-renewal is maintained. The dynamic and ongoing nature of stem cell regulation, as well as the complex signaling microenvironment in which stem cells are typically found, means that studying them in their endogenous environment in real time has multiple advantages over static fixed-sample approaches. We recently described a method for long-term, ex-vivo, live imaging of the blood progenitors in the Drosophila larval hematopoietic organ, the Lymph Gland (LG). This methodology has allowed us to analyze multiple aspects of fly hematopoiesis, in real time, in a manner that could not be carried out previously. Here, we describe novel insights derived from our quantitative live imaging approach. These insights include: the identification of extensive filopodia in the progenitors and description of their morphology and dynamics; visualization and quantitative analysis of JAK/STAT signaling in progenitors by the simultaneous tracking of thousands of vesicles containing internalized Domeless receptors; quantitative analysis of the location, morphology, and dynamics of mitochondria in blood progenitors; long-term tracking of patterns of cell division and migration of mature blood cell in the LG; long-term tracking of multiple cell behaviors in the distal committed progenitors; analysis of Ca2+ signaling of blood progenitors in the secondary lobes of the LG. Together, these observations illustrate the power of imaging fly hematopoiesis in real time and identify many previously undescribed processes and behaviors in the LG that are likely to play important roles in the regulation of progenitor differentiation and self-renewal.

Acoustic imaging of geometrically shielded sound sources using tailored Green's functions.

In light of the growing market of urban air mobility, it is crucial to accurately detect the stationary or moving noise sources within the complex scattering environments caused by aircraft structures such as airframes and engines. This study combines conventional and wavelet-based beamforming techniques with an acoustic scattering prediction method to develop an acoustic imaging approach that considers scattering effects. Tailored Green's function is numerically evaluated and used to compute the steering vectors and the specific delayed time used in those beamforming methods. By examining common scenarios where a scatterer is positioned between the source plane and the array plane, it is observed that beamforming in a scattering environment differs from that in free space, leading to improved resolution alongside scattering-induced side lobes. The effectiveness of the developed method is validated through numerical simulations and experimental studies, confirming its improved ability to localize both stationary and rotating sound sources in a shielded environment. This advancement offers effective techniques for acoustic measurement and fault monitoring in the presence of structural scatterers.

A multimodal imaging approach to investigate retinal oxygen and vascular dynamics, and neural dysfunction in bietti crystalline dystrophy

BackgroundThis study aimed to explore retinal changes in Bietti crystalline dystrophy (BCD) patients, including retinal metabolism, blood flow, vascular remodeling, and pupillary light reflex (PLR) abnormalities. MethodsThis cross-sectional study included 120 eyes from BCD patients and 120 eyes from healthy controls, utilizing a multimodal imaging system (MEFIAS 3200, SYSEYE, Chongqing, China) to evaluate retinal oxygenation, blood flow, vascular structure, and PLR. Measurements included oxygen saturation, blood flow velocity, vessel diameters, and pulsatility metrics. PLR parameters were assessed under specific light stimuli. ResultsBCD patients demonstrated significantly higher retinal oxygen saturation and content, but lower oxygen utilization and metabolism compared to controls, with more pronounced declines in those over 40 years old. Vascular parameters revealed smaller external diameters and larger lumen diameters, indicating vascular remodeling. Retinal blood flow was lower, while the resistivity index was higher in BCD patients. Additionally, PLR abnormalities were noted, including reduced constriction amplitude, pupil constriction ratio, constriction duration, and maximum constriction velocity, along with prolonged latency were observed in BCD patients. ConclusionBCD patients had significant retinal and vascular changes, along with PLR impairments, especially in patients over 40. More targeted interventions should be focused in future research.

Coordinate-Independent 3-D Ultrasound Principal Stretch and Direction Imaging.

In clinical ultrasound, current 2-D strain imaging faces challenges in quantifying three orthogonal normal strain components. This requires separate image acquisitions based on the pixel-dependent cardiac coordinate system, leading to additional computations and estimation discrepancies due to probe orientation. Most systems lack shear strain information, as displaying all components is challenging to interpret. This paper presents a 3-D high-spatial-resolution, coordinate-independent strain imaging approach based on principal stretch and axis estimation. All strain components are transformed into three principal stretches along three normal principal axes, enabling direct visualization of the primary deformation. We devised an efficient 3-D speckle tracking method with tilt filtering, incorporating randomized searching in a two-pass tracking framework and rotating the phase of the 3-D correlation function for robust filtering. The proposed speckle tracking approach significantly improves estimates of displacement gradients related to the axial displacement component. Non-axial displacement gradient estimates are enhanced using a correlation-weighted least-squares method constrained by tissue incompressibility. Simulated and in vivo canine cardiac datasets were evaluated to estimate Lagrangian strains from end-diastole to end-systole. The proposed speckle tracking method improves displacement estimation by a factor of 4.3 to 10.5 over conventional 1-pass processing. Maximum principal axis/direction imaging enables better detection of local disease regions than conventional strain imaging. Coordinate-independent tracking can identify myocardial abnormalities with high accuracy. This study offers enhanced accuracy and robustness in strain imaging compared to current methods.

The Role of CT and MR Imaging in Stereotactic Body Radiotherapy of the Spine: From Patient Selection and Treatment Planning to Post-Treatment Monitoring.

Spine metastases (SMs) are common, arising in 70% of the cases of the most prevalent malignancies in males (prostate cancer) and females (breast cancer). Stereotactic body radiotherapy, or SBRT, has been incorporated into clinical treatment algorithms over the past decade. SBRT has shown promising rates of local control for oligometastatic spinal lesions with low radiation dose to adjacent critical tissues, particularly the spinal cord. Imaging is critically important in SBRT planning, guidance, and response monitoring. This paper reviews the roles of imaging in spine SBRT, including conventional and advanced imaging approaches for SM detection, treatment planning, and post-SBRT follow-up.

Possible involvement of Toll-like receptor 8-positive monocytes/macrophages in the pathogenesis of Sjögren's disease.

Sjögren's disease (SjD) is an autoimmune disease marked by lymphocytic infiltration of salivary and lacrimal glands, leading to glandular dysfunction, where CD4-positive helper T (Th) cells and their cytokines are crucial in the pathogenesis. Recent studies have demonstrated that Toll-like receptors (TLRs), particularly those recognizing immune complexes containing DNA and RNA, contribute to Th cell activation in various autoimmune diseases. This study explores the expression and function of these TLRs in SjD. DNA microarray analysis of salivary gland tissue from six SjD patients and real-time PCR (n = 32) was used to identify overexpressed TLRs. Single-cell RNA sequencing (scRNA-seq) was performed using tissue lesions and integrated with published scRNA-seq data from tissues and peripheral blood mononuclear cells to examine gene expression in macrophages and monocytes. Finally, multi-color immunofluorescence staining was conducted to confirm TLR8 expression and function in SjD lesions (n = 19). DNA microarray analysis revealed the up-regulation of TLR8, along with other TLRs and innate immune response genes in SjD. Real-time PCR showed significant up-regulation of TLR7 and TLR8. TLR8 up-regulated in both analyses. In scRNA-seq analysis, the TLR8-expressing cluster comprised macrophages and monocytes, which also produced T cell activation genes like CD86. TLR8-positive macrophages infiltrated inflammatory sites and frequently expressed CD86 in quantitative imaging approaches. These results suggest that infiltrating monocytes and macrophages may produce cytokines and chemokines through TLR8 stimulation, potentially enhancing B7 molecule expression, promoting the adaptive immune response, and contributing to SjD pathogenesis.

Georecon: a coarse-to-fine visual 3D reconstruction approach for high-resolution images with neural matching priors

ABSTRACT Visual 3D reconstruction enables rebuilding 3D scenes from captured images, serving as a fundamental data source for digital earth modeling and intelligent cities. In the foundational step, recent methods leverage learning-based descriptors for image registration and achieve tremendous advances in precision and robustness. However, these methods inevitably execute down sampling towards high-resolution images to fit the needs of neural networks, which leads to precision degradation of feature localization and matching. Thus, we propose GeoRecon: a novel coarse-to-fine visual 3D reconstruction method that optimally utilizes high-resolution images for high-quality visual 3D reconstruction. Firstly, the coarse stage conducts coarse reconstruction from downsampled images by performing neural matching with geometric priors. Secondly, we define the fine-grained stage, proposing a GPU-based algorithm for generating image-patch correspondences based on the neural matching priors to perform fine-grained image registration. Finally, based on the optimized camera poses under this coarse-to-fine paradigm, progressive dense reconstruction leveraging efficient neural radiance fields is proposed to accomplish the high-quality MVS reconstruction. Comparative experiments across various scenarios demonstrate the proposed method’s superior precision, robustness, and reconstruction quality.

Mass Spectrometry Imaging with Trapped Ion Mobility Spectrometry Enables Spatially Resolved Chondroitin, Dermatan, and Hyaluronan Glycosaminoglycan Oligosaccharide Analysis In Situ.

Previously, spatially resolved analysis of glycosaminoglycans (GAGs), by type and sulfation state, was unobtainable. Here, we describe a mass spectrometry imaging (MSI) approach which enables the detection, identification, localization, and profiling of GAG oligosaccharides directly from retinal tissue. Through in situ treatment of tissues with relevant chondroitinase enzymes, we liberate and spatially resolve chondroitin, dermatan, and hyaluronan from disaccharides through to hexasaccharides, directly from tissue sections. We demonstrate the separation of isomeric GAG oligosaccharide ions at different histologically relevant regions using trapped ion mobility spectrometry (TIMS). This paper describes the first spatially resolved analysis of multiple GAGs and their oligosaccharide sulfation state(s) directly from tissues.

PcdA promotes orthogonal division plane selection in Staphylococcus aureus.

The bacterial pathogen, Staphylococcus aureus, grows by dividing in two alternating orthogonal planes. How these cell division planes are positioned correctly is not known. Here we used chemical genetic screening to identify PcdA as a division plane placement factor. Molecular biology and imaging approaches revealed non-orthogonal division plane selection for pcdA mutant bacteria. PcdA is a structurally and functionally altered member of the McrB AAA+ NTPase family, which are often found as restriction enzyme subunits. PcdA interacts with the tubulin-like divisome component, FtsZ, and the structural protein, DivIVA; it also localizes to future cell division sites. PcdA multimerization, localization and function are NTPase activity-dependent. We propose that the DivIVA/PcdA complex recruits unpolymerized FtsZ to assemble along the proper cell division plane. Although pcdA deletion did not affect S. aureus growth in several laboratory conditions, its clustered growth pattern was disrupted, sensitivity to cell-wall-targeting antibiotics increased and virulence in mice decreased. We propose that the characteristic clustered growth pattern of S. aureus, which emerges from dividing in alternating orthogonal division planes, might protect the bacterium from host defences.

High-Resolution Collaborative Forward-Looking Imaging Using Distributed MIMO Arrays

Airborne radar forward-looking imaging holds significant promise for applications such as autonomous navigation, battlefield reconnaissance, and terrain mapping. However, traditional methods are hindered by complex system design, azimuth ambiguity, and low resolution. This paper introduces a distributed array collaborative, forward-looking imaging approach, where multiple aircraft with linear arrays fly in parallel to achieve coherent imaging. We analyze signal model characteristics and highlight the limitations of conventional algorithms. To address these issues, we propose a high-resolution imaging algorithm that combines an enhanced missing-data iterative adaptive approach with aperture interpolation technique (MIAA-AIT) for effective signal recovery in distributed arrays. Additionally, a novel reference range cell migration correction (reference RCMC) is employed for precise range–azimuth decoupling. The forward-looking algorithm effectively transforms distributed arrays into a virtual long-aperture array, enabling high-resolution, high signal-to-noise ratio imaging with a single snapshot. Simulations and real data tests demonstrate that our method not only improves resolution but also offers flexible array configurations and robust performance in practical applications.

Two-Step Iterative Medical Microwave Tomography.

In the field of medical imaging, microwave tomography (MWT) is based on the scattering and absorption characteristics of different tissues to microwaves and can reconstruct the electromagnetic property distribution of biological tissues non-invasively and without ionizing radiation. However, due to the inherently nonlinear and ill-posed characteristics of MWT calculations, actual imaging is prone to overfitting or artifacts. To address this, this paper proposes a two-step iterative imaging approach for rapid medical microwave tomography. This method establishes corresponding objective functions for microwave imaging across multiple frequencies and conducts iterative calculations on images at varying resolutions. This effectively enhances image clarity and accuracy while alleviating the issue of prolonged computational time associated with imaging complex structures at high resolution due to insufficient prior information during iterative processes. In the electromagnetic simulation section, we simulated a three-layer brain model and conducted imaging experiments. The results demonstrate that the algorithm significantly enhances imaging resolution, accurately pinpointing cerebral hemorrhages at different locations using an eight-antenna array and successfully reconstructs tomography images with a hemorrhage area radius of 1 cm. Lastly, experiments were conducted using a medical microwave tomography platform and four simplified human brain models, achieving millimeter-level accuracy in MWT.

Quantitative magnetooptical analysis using indicator films for the detection of magnetic field distributions, temperature, and electrical currents

The accurate characterization of local magnetic fields and temperature is vital for the design of electronic systems. To meet this imperative, we present a novel non-contact approach for simultaneous quantitative magnetic field imaging and temperature sensing using magnetooptics and a bismuth-doped yttrium iron garnet film with out-of-plane anisotropy. For the direct signal quantification, a Stokes polarization camera is employed in a conventional magnetooptical microscope. The magnetization in the garnet is modulated with an external magnetic field to continuously image the Faraday rotation at four distinct points along the saturating magnetization loop. The method enables sensing of the magnetooptical signal in saturation, the magnetooptical susceptibility, the temperature, and self-calibrated driftfree imaging of the out-of-plane magnetic field component. A spatial resolution of magnetic field in the micrometer range with millisecond exposure time is demonstrated. The method is verified by analyzing the stray magnetic field distribution of electrical current in a wire simultaneously to the Joule heating induced by the applied current.

Functional PET/MRI reveals active inhibition of neuronal activity during optogenetic activation of the nigrostriatal pathway.

The dopaminergic system is a central component of the brain's neurobiological framework, governing motor control and reward responses and playing an essential role in various brain disorders. Within this complex network, the nigrostriatal pathway represents a critical circuit for dopamine neurotransmission from the substantia nigra to the striatum. However, stand-alone functional magnetic resonance imaging is unable to study the intricate interplay between brain activation and its molecular underpinnings. In our study, the use of a functional [fluorine-18]2-fluor-2-deoxy-d-glucose positron emission tomography approach, simultaneously with blood oxygen level-dependent functional magnetic resonance imaging, provided an important insight that demonstrates an active suppression of the nigrostriatal activity during optogenetic stimulation. This result increases our understanding of the molecular mechanisms of brain function and provides an important perspective on how dopamine influences hemodynamic responses in the brain.

Multiple-Input Multiple-Output Microwave Tomographic Imaging for Distributed Photonic Radar Network

This paper deals with the imaging problem from data collected by means of a microwave photonics-based distributed radar network. The radar network is leveraged on a centralized architecture, which is composed of one central unit (CU) and two transmitting and receiving dual-band remote radar peripherals (RPs), it is capable of collecting monostatic and multistatic phase-coherent data. The imaging is herein formulated as a linear inverse scattering problem and solved in a regularized way through the truncated singular value decomposition inversion scheme. Specifically, two different imaging schemes based on an incoherent fusion of the tomographic images or a fully coherent data processing are herein developed and compared. Experimental tests carried out in a port scenario for imaging both a stationary and a moving target are reported to validate the imaging approach.

Automated denoising software for calcium imaging signals using deep learning

Dynamic Ca2+ signaling is crucial for cell survival and death, and Ca2+ imaging approaches are commonly used to study and measure cellular Ca2+ patterns within cells. However, the presence of image noise from instrumentation and experimentation protocols can impede the accurate extraction of Ca2+ signals. Removing noise from Ca2+ Spatio-Temporal Maps (STMaps) is essential for precisely analyzing Ca2+ datasets. Current methods for denoising STMaps can be time-consuming and subjective and rely mainly on image processing protocols. To address this, we developed CalDenoise, an automated software that employs robust image processing and deep learning models to remove noise and enhance Ca2+ signals in STMaps effectively. CalDenoise integrates four pipelines capable of efficiently removing salt-and-pepper, impulsive, and periodic noise and detecting and removing background noise. Comprising both an image-processing-based pipeline and three generative-adversarial-network-based (GAN) deep learning models, CalDenoise proficiently removes complex noise patterns. The software features adjustable parameters to enhance accuracy and is integrated into a user-friendly graphical interface for easy access and streamlined usage.CalDenoise can serve as a robust platform for denoising complex dynamic fluorescence signal images across diverse cell types, including Ca2+, voltage, ions, and pH signals.

Wigner state and process tomography on near-term quantum devices

We present an experimental scanning-based tomography approach for near-term quantum devices. The underlying method has previously been introduced in an ensemble-based NMR setting. Here we provide a tutorial-style explanation along with suitable software tools to guide experimentalists in its adaptation to near-term pure-state quantum devices. The approach is based on a Wigner-type representation of quantum states and operators. These representations provide a rich visualization of quantum operators using shapes assembled from a linear combination of spherical harmonics. These shapes (called droplets in the following) can be experimentally tomographed by measuring the expectation values of rotated axial tensor operators. We present an experimental framework for implementing the scanning-based tomography technique for circuit-based quantum computers and showcase results from IBM quantum experience. We also present a method for estimating the density and process matrices from experimentally tomographed Wigner functions (droplets). This tomography approach can be directly implemented using the Python-based software package DROPStomo.

YOLO v10-Based Brain Tumor Detection: An Innovative Approach in CT Imaging

YOLO v10-Based Brain Tumor Detection: An Innovative Approach in CT Imaging