Purpose: The Unadjusted Langevin Algorithm (ULA) in combination with diffusion models can generate high quality MRI reconstructions with uncertainty estimation from highly undersampled k-space data. However, sampling methods such as diffusion posterior sampling or likelihood annealing suffer from long reconstruction times and the need for parameter tuning. The purpose of this work is to develop a robust sampling algorithm with fast convergence. Theory and Methods: In the reverse diffusion process used for sampling the posterior, the exact likelihood is multiplied with the diffused prior at all noise scales. To overcome the issue of slow convergence, preconditioning is used. The method is trained on fastMRI data and tested on retrospectively undersampled brain data of a healthy volunteer. Results: For posterior sampling in Cartesian and non-Cartesian accelerated MRI the new approach outperforms annealed sampling in terms of reconstruction speed and sample quality. Conclusion: The proposed exact likelihood with preconditioning enables rapid and reliable posterior sampling across various MRI reconstruction tasks without the need for parameter tuning.

Accurate simulations of electric fields (E-fields) in brain stimulation depend on tissue conductivity representations that link macroscopic assumptions with underlying microscopic tissue structure. Mesoscale conductivity variations can produce meaningful changes in E-fields and neural activation thresholds but remain largely absent from standard macroscopic models. Recent microscopic models have suggested substantial local E-field perturbations and could, in principle, inform mesoscale conductivity. However, the quantitative validity of microscopic models is limited by fixation-related tissue distortion and incomplete extracellular-space reconstruction. We outline approaches that bridge macro- and microscales to derive consistent mesoscale conductivity distributions, providing a foundation for accurate multiscale models of E-fields and neural activation in brain stimulation.

Computed tomography perfusion (CTP) and magnetic resonance perfusion (MRP) are widely used in acute ischemic stroke assessment and other cerebrovascular conditions to generate quantitative maps of cerebral hemodynamics. While commercial perfusion analysis software exists, it is often costly, closed source, and lacks customizability. This work introduces PyPeT, an openly available Python Perfusion Tool for head CTP and MRP processing. PyPeT is capable of producing cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), time-to-peak (TTP), and time-to-maximum (Tmax) maps from raw four-dimensional perfusion data. PyPeT aims to make perfusion research as accessible and customizable as possible. This is achieved through a unified framework in which both CTP and MRP data can be processed, with a strong focus on modularity, low computational burden, and significant inline documentation. PyPeT's outputs can be validated through an extensive debug mode in which every step of the process is visualized. Additional validation was performed via visual and quantitative comparison with reference perfusion maps generated by three FDA-approved commercial perfusion tools and a research tool. These comparisons show a mean SSIM around 0.8 for all comparisons, indicating a good and stable correlation with FDA-approved tools. The code for PyPeT is openly available at our GitHub this https URL

Recent advancements in artificial intelligence (AI), particularly foundation models (FMs), have revolutionized medical image analysis, demonstrating strong zero- and few-shot performance across diverse medical imaging tasks, from segmentation to report generation. Unlike traditional task-specific AI models, FMs leverage large corpora of labeled and unlabeled multimodal datasets to learn generalized representations that can be adapted to various downstream clinical applications with minimal fine-tuning. However, despite the rapid proliferation of FM research in medical imaging, the field remains fragmented, lacking a unified synthesis that systematically maps the evolution of architectures, training paradigms, and clinical applications across modalities. To address this gap, this review article provides a comprehensive and structured analysis of FMs in medical image analysis. We systematically categorize studies into vision-only and vision-language FMs based on their architectural foundations, training strategies, and downstream clinical tasks. Additionally, a quantitative meta-analysis of the studies was conducted to characterize temporal trends in dataset utilization and application domains. We also critically discuss persistent challenges, including domain adaptation, efficient fine-tuning, computational constraints, and interpretability along with emerging solutions such as federated learning, knowledge distillation, and advanced prompting. Finally, we identify key future research directions aimed at enhancing the robustness, explainability, and clinical integration of FMs, thereby accelerating their translation into real-world medical practice.

Automating radiology report generation poses a dual challenge: building clinically reliable systems and designing rigorous evaluation protocols. We introduce a multi-agent reinforcement learning framework that serves as both a benchmark and evaluation environment for multimodal clinical reasoning in the radiology ecosystem. The proposed framework integrates large language models (LLMs) and large vision models (LVMs) within a modular architecture composed of ten specialized agents responsible for image analysis, feature extraction, report generation, review, and evaluation. This design enables fine-grained assessment at both the agent level (e.g., detection and segmentation accuracy) and the consensus level (e.g., report quality and clinical relevance). We demonstrate an implementation using chatGPT-4o on public radiology datasets, where LLMs act as evaluators alongside medical radiologist feedback. By aligning evaluation protocols with the LLM development lifecycle, including pretraining, finetuning, alignment, and deployment, the proposed benchmark establishes a path toward trustworthy deviance-based radiology report generation.

Objective: The aim of this study was to evaluate the efficacy of alendronate therapy in improving bone density distribution in skull bones and corresponding ultrasound permeability in patients who had previously experienced unsuccessful transcranial MR-guided focused ultrasound (MRgFUS) ablation. The ability of alendronate treatment to modify skull bone characteristics and enhance the success rate of repeat MRgFUS procedures was assessed. Methods: Five patients with initially unsuccessful MRgFUS ablations underwent a 6-12 month regimen of alendronate to improve bone density. Repeat MRgFUS procedures were performed, and changes in skull density ratio (SDR) and peak focal temperatures were evaluated statistically using CT and MR imaging. Histograms of skull bone density were introduced and analysed as an additional metric. Results: After therapy, SDR increased in four out of five patients (from 0.378$\pm$0.037 to 0.424$\pm$0.045, p>0.05). All repeated procedures were successful. The maximum focal temperature, averaged over sonications, increased from 53.6$\pm$4.0{\deg}C to 55.7$\pm$4.1{\deg}C (p=0.018), while the maximum temperature per patient rose from 57.0$\pm$2.4{\deg}C to 60.2$\pm$1.8{\deg}C (p=0.031). Histograms of CT scans showed a reduction in low-density voxels, indicating trabecular bone densification. 3D CT scan registration revealed local density changes, defect filling, and void reduction. Conclusions: Alendronate therapy enhanced skull bone density distribution and thus ultrasound permeability, which has facilitated successful repeat MRgFUS. By visually analysing CT changes, healthcare professionals can better inform their decision-making regarding repeat surgeries. This method broadens the pool of patients with low SDR eligible for MRgFUS treatment and underscores the potential benefits of alendronate in improving treatment outcomes.

The application of information theory in medical imaging, particularly in magnetic resonance imaging (MRI), offers powerful quantitative tools for analyzing structural differences in brain tissues. This study utilizes mutual information (MI) and pixel intensity distributions to differentiate between normal and tumor-affected brain MRI images. Mutual information analyses revealed significantly higher MI values in tumor images compared to normal ones, indicating greater internal similarity within tumor images. Pixel intensity analysis further demonstrated distinct distribution patterns between the two groups: tumor images showed pronounced pixel frequency concentrations within a specific intensity range (0.3, 0.4), suggesting predictable structural characteristics. Conversely, normal images exhibited broader, more uniform pixel intensity distributions across most intensity ranges, except for an initial peak observed in both groups. These findings highlight the capability of information-theoretic metrics, such as mutual information and pixel intensity analysis, to effectively distinguish tumor tissue from normal brain structures, providing promising avenues for enhanced diagnostic and analytical methods in neuroimaging.

The upcoming phase of space exploration not only includes trips to Mars and beyond, but also holds great promise for human progress. However, the harm caused by cosmic radiation, consisting of Galactic Cosmic Rays and Solar Particle Events, is an important safety concern for astronauts and other living things that will accompany them. Research exploring the biological effects of cosmic radiation includes experiments conducted in space itself and in simulated space environments on Earth. Notably, NASA's Space Radiation Laboratory has taken significant steps forward in simulating cosmic radiation by using particle accelerators and is currently pioneering the progress in this field. Curiously, much of the research emphasis thus far has been on understanding how cosmic radiation impacts living organisms, instead of finding ways to help them resist the radiation. In this paper, we briefly talk about current research on the biological effects of cosmic radiation and propose possible protective measures through biological interventions. In our opinion, biological response pathways responsible for coping with stressors on Earth can provide effective solutions for protection against the stress caused by cosmic radiation. We also recommend establishing the Dedicated International Accelerator Laboratory for Space Travel related radiation research (DIAL-ST) to advance this field and evaluate protective biological pathways through particle accelerator experiments simulating cosmic radiation.

Vascular imaging is critical for understanding human health and disease. Most established non-contact and label-free optical techniques capture predominantly structural information about vasculature. However, in many pathologies, functional changes often precede visible morphological changes. This limits the ability of established modalities to prevent negative patient outcomes. In this study, a new in vivo Photon Absorption Remote Sensing (PARS) microscope is proposed. PARS enables the label-free non-contact structural and functional imaging of vascular structures and their microenvironment. PARS aims to capture the dominant light matter interactions surrounding an absorption event including both non-radiative and radiative relaxation. System performance is demonstrated through wide field of view in vivo imaging of vascular contrast in both mouse ear and chicken embryo. Additionally, video rate imaging of chicken embryo capillaries shows first feasibility for blood flow measurement using PARS. This work represents a promising step for vascular imaging techniques where there is significant demand for a method of non-contact label-free functional imaging.

BabelBrain is an open-source standalone graphic-user-interface application designed for studies of neuromodulation using transcranial focused ultrasound. It calculates the transmitted acoustic field in the brain tissue, taking into account the distortion effects caused by the skull barrier. The simulation is prepared using scans from magnetic resonance imaging (MRI) and, if available, computed tomography and zero-echo time MRI scans. It also calculates the thermal effects based on a given ultrasound regime, such as the total duration of exposure, the duty cycle, and acoustic intensity. The tool is designed to work in tandem with neuronavigation and visualization software, such as 3DSlicer. It uses image processing to prepare domains for ultrasound simulation and uses the BabelViscoFDTD library for transcranial modeling calculations. BabelBrain supports multiple GPU backends, including Metal, OpenCL, and CUDA, and works on all major operating systems including Linux, MacOS, and Windows. This tool is particularly optimized for Apple ARM64 systems, which are common in brain imaging research. The paper presents the modeling pipeline used in BabelBrain and a numerical study where different methods of acoustic properties mapping were tested to select the best method that can reproduce the transcranial pressure transmission efficiency reported in the literature.

Determining brain hemodynamics plays a critical role in the diagnosis and treatment of various cerebrovascular diseases. In this work, we put forth a physics-informed deep learning framework that augments sparse clinical measurements with fast computational fluid dynamics (CFD) simulations to generate physically consistent and high spatiotemporal resolution of brain hemodynamic parameters. Transcranial Doppler (TCD) ultrasound is one of the most common techniques in the current clinical workflow that enables noninvasive and instantaneous evaluation of blood flow velocity within the cerebral arteries. However, it is spatially limited to only a handful of locations across the cerebrovasculature due to the constrained accessibility through the skull's acoustic windows. Our deep learning framework employs in-vivo real-time TCD velocity measurements at several locations in the brain and the baseline vessel cross-sectional areas acquired from 3D angiography images, and provides high-resolution maps of velocity, area, and pressure in the entire vasculature. We validated the predictions of our model against in-vivo velocity measurements obtained via 4D flow MRI scans. We then showcased the clinical significance of this technique in diagnosing the cerebral vasospasm (CVS) by successfully predicting the changes in vasospastic local vessel diameters based on corresponding sparse velocities measurements. The key finding here is that the combined effects of uncertainties in outlet boundary condition subscription and modeling physics deficiencies render the conventional purely physics-based computational models unsuccessful in recovering accurate brain hemodynamics. Nonetheless, fusing these models with clinical measurements through a data-driven approach ameliorates predictions of brain hemodynamic variables.

Purpose: This study aimed to investigate the actual changes of central corneal thickness (CCT) in keratoconus and normal corneas during air puff indentation, by using corneal visualization Scheimpflug technology (Corvis ST). Methods: A total of 32 keratoconic eyes and 46 normal eyes were included in this study. Three parameters of CCTinitial, CCTfinal and CCTpeak were selected to represent the CCT at initial time, final time and highest corneal concavity, respectively, during air puff indentation. Wilcoxon signed rank test (paired sample test) was used to assess the differences between these 3 parameters in both keratoconus and normal groups. Univariate linear regression analysis was performed to determine the effect of CCTinitial on CCTpeak and CCTfinal, as well as the impact of air puff force on CCT in each group. Receiver operating characteristic (ROC) curves were constructed to evaluate the discriminative ability of the 3 parameters. Results: The results demonstrated that CCTpeak and CCTfinal were significantly decreased (p<0.01) compared to CCTinitial in both keratoconus and normal groups. Regression analysis indicated a significant positive correlation between CCTpeak and CCTinitial in normal cornea group (R2=0.337, p<0.01), but not in keratoconus group (R2=0.029, p=0.187). Likewise, regression models of air puff force and CCT revealed the different patterns of CCT changes between keratoconus and normal cornea groups. Furthermore, ROC curves showed that CCTpeak exhibited the greatest AUC (area under ROC curve) of 0.940, with accuracy, sensitivity and specificity of 94.9%, 87.5% and 100%, respectively. Conclusions: CCT may change during air puff indentation, and is significantly different between keratoconus and normal cornea groups. The changing pattern is useful for the diagnosis of keratoconus, and lays the foundation for corneal biomechanics.

Cone-beam breast computed tomography (CT) provides true 3D breast images with isotropic resolution and high-contrast information, detecting calcifications as small as a few hundred microns and revealing subtle tissue differences. However, breast is highly sensitive to x-ray radiation. It is critically important for healthcare to reduce radiation dose. Few-view cone-beam CT only uses a fraction of x-ray projection data acquired by standard cone-beam breast CT, enabling significant reduction of the radiation dose. However, insufficient sampling data would cause severe streak artifacts in CT images reconstructed using conventional methods. In this study, we propose a deep-learning-based method to establish a residual neural network model for the image reconstruction, which is applied for few-view breast CT to produce high quality breast CT images. We respectively evaluate the deep-learning-based image reconstruction using one third and one quarter of x-ray projection views of the standard cone-beam breast CT. Based on clinical breast imaging dataset, we perform a supervised learning to train the neural network from few-view CT images to corresponding full-view CT images. Experimental results show that the deep learning-based image reconstruction method allows few-view breast CT to achieve a radiation dose <6 mGy per cone-beam CT scan, which is a threshold set by FDA for mammographic screening.

We report an experimental and numerical investigation to study the role of asymmetry in the expansion-contraction of the acinar wall on the particle transport in the acinus. We model the acinar flow feature using a T-section by appropriately matching the dimensionless numbers to that in the acinus of healthy human subjects. We show that asymmetry in the expansion-contraction process (quantified by $\phi$) is required for chaotic advection. We show the stretch and fold process leading to chaos for a range of $\phi$ and scaled oscillation frequency $Sr$. We show a regime map in this generalize $\phi$ and $Sr$ space and show that most mammalian lungs fall at the boundary of chaotic regime.

Computed tomography (CT) images containing metallic objects commonly show severe streaking and shadow artifacts. Metal artifacts are caused by nonlinear beam-hardening effects combined with other factors such as scatter and Poisson noise. In this paper, we propose an implant-specific method that extracts beam-hardening artifacts from CT images without affecting the background image. We found that in cases where metal is inserted in the water (or tissue), the generated beam-hardening artifacts can be approximately extracted by subtracting artifacts generated exclusively by metals. We used a deep learning technique to train nonlinear representations of beam-hardening artifacts arising from metals, which appear as shadows and streaking artifacts. The proposed network is not designed to identify ground-truth CT images (i.e., the CT image before its corruption by metal artifacts). Consequently, these images are not required for training. The proposed method was tested on a dataset consisting of real CT scans of pelvises containing simulated hip prostheses. The results demonstrate that the proposed deep learning method successfully extracts both shadowing and streaking artifacts.

Background: Recent studies reported postural balance disorders in patients and soccer players with groin pain (GP) compared to controls. Since postural balance asymmetry identified after an initial injury contributes for subsequent injuries, identification of this asymmetry in soccer players with GP may highlight the risk of sustaining subsequent noncontact lower extremity musculoskeletal injuries in these players. Therefore, the aims of this study were to (i) examine static and dynamic unipedal postural balance asymmetry in soccer players with GP compared to healthy ones, and (ii) quantify the risk of subsequent noncontact lower extremity injuries in these players. Research question: Do soccer players with GP exhibit higher static and dynamic unipedal postural balance asymmetry, and higher risk of sustaining subsequent injuries compared to controls. Methods: In this prospective case control study, 27 soccer players with non-time loss GP (GP group: GPG), and 27 healthy ones (control group: CG) were enrolled. Static and dynamic unipedal postural balance asymmetry were evaluated with a force platform using symmetry index (SI), and Y-balance test (Y-BT), respectively. Additionally, subsequent noncontact lower extremity musculoskeletal injuries were tracked for 10 months. Results: The GPG revealed higher (p < 0.01) SI in eyes closed condition, higher (p < 0.001) side-to-side asymmetry in anterior, posteromedial and posterolateral reach distances and in composite Y-BT score compared to CG. They showed lower (p < 0.001) composite score for injured limb and higher (p < 0.001) side-to-side asymmetry in posteromedial reach distance compared to the cutoff values of 89.6 % and 4 cm, respectively. Moreover, GPG exhibited higher odds (OR= 7.48; 95 % CI = 2.15, 26.00; p < 0.01) of sustaining subsequent injuries compared to CG. Significance: The Y-BT should be instituted into existing pre-participation physical examinations to screen for soccer players with non-time loss GP at an elevated risk of sustaining subsequent injuries. This could help coaches and clinicians make valid return to play decisions.

This study aims to identify and parameterize the optimal survival curves for 33 fundamental microorganisms subject to UVC exposure through experimental measurements. We compile published data on UVC doses and corresponding survival fractions for these microorganisms to estimate parameters for four prominent survival models: Single-target (ST), Multi-target (MT), Linear Quadratic (LQ), and Two-Stage Decay (TSD). The best-fitting model for each microorganism is determined by selecting the one with the lowest mean squared error (MSE) compared to the experimental data. Our analysis indicates that the MT model is the most frequently appropriate, accurately fitting 21 of the 33 microorganisms. The TSD model is the best fit for only three, while the LQ model, though occasionally suitable at lower doses, is often excluded due to unreliable performance at higher doses. The assessed models, particularly the MT model, demonstrate strong predictive capabilities for UVC surface sterilization of microorganisms. However, caution is warranted with the LQ model at higher doses due to its potential limitations.

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background.

This paper describes a model for nonlinear acoustic wave propagation through absorbing and weakly dispersive media, and its numerical solution by means of finite differences in time domain method (FDTD). The attenuation is based on multiple relaxation processes, and provides frequency dependent absorption and dispersion without using computational expensive convolutional operators. In this way, by using an optimization algorithm the coefficients for the relaxation processes can be obtained in order to fit a frequency power law that agrees the experimentally measured attenuation data for heterogeneous media over the typical frequency range for ultrasound medical applications. Our results show that two relaxation processes are enough to fit attenuation data for most soft tissues in this frequency range including the fundamental and the first ten harmonics. Furthermore, this model can fit experimental attenuation data that do not follow exactly a frequency power law over the frequency range of interest. The main advantage of the proposed method is that only one auxiliary field per relaxation process is needed, which implies less computational resources compared with time-domain fractional derivatives solvers based on convolutional operators.