APHABAMAS: An analytical phantom-based scheme for assessing the accuracy of high-resolution 3D MRI motion-artifact simulations

SIINR: Structurally Informed Implicit Neural Representations for super-resolution with uncertainty quantification of clinical quality diffusion MRI datasets

Diffusion Magnetic Resonance Imaging (dMRI) is a powerful tool for probing brain microstructure, but clinical acquisitions are often limited by low out-of-plane resolution, resulting in degraded structural information and reduced utility for advanced analysis. We introduce SIINR (Structurally Informed Implicit Neural Representations), a general framework for super-resoltion of clinical dMRI datasets while quantifying uncertainty in the reconstructed outputs. SIINR utilizes a supervised 3D U-net as a prior and combines it with a self-supervised implicit neural representation (INR) that fuses the high-resolution prior and the original low-resolution data. The INR enables joint modeling across spatial and angular domains, enforces data consistency, and provides analytic approximate posterior distributions for downstream uncertainty quantification. We validate the framework on a diverse set of open-access dMRI datasets, demonstrating that SIINR outperforms standard interpolation methods in both quantitative error metrics and qualitative anatomical fidelity. Experiments on clinical cases, including subjects with multiple sclerosis and brain lesions, illustrate the framework its ability to propagate intensity changes and flag uncertain regions in challenging scenarios. SIINR is flexible, modular, and can be adapted to different upsampling ratios and downstream tasks, providing a principled approach for enhancing clinical dMRI and supporting robust interpretation of derived neuroimaging metrics.

BART Online Open-Source Sequence Toolbox for Computational MRI

This work enables reproducibility of advanced computational MRI methods within a comprehensive end-to-end open-source framework and proves that quantitative MRI methods consisting of acquisition and reconstruction were successfully implemented in BART.

AI-based reconstruction from low-resolution acquisitions for diffusion MRI: evaluation of FOD similarity.

Fast and Robust IVIM Parameter Estimation in Breast MRI Using Physics-Informed Deep Learning

3D Uncertainty Quantification for the Photo-Acoustic Tomography

Photoacoustic tomography (PAT) is a promising modality for high-resolution biomedical imaging, motivating the need for reliable uncertainty quantification (UQ) of reconstructed images. Bayesian approaches provide a rigorous framework for UQ but remain computationally challenging for realistic three-dimensional PAT and are sensitive to numerical approximations in the governing wave equation. We develop a finite-element Bayesian UQ framework for PAT that accommodates complex computational domains and detector geometries while enabling large-scale three-dimensional inference. The proposed methodology reformulates the randomize-then-optimize (RTO) sampling strategy as a matrix-free algorithm that generates independent posterior samples using only forward and adjoint wave propagations. Particular attention is given to constructing an adjoint discretization that forms an exact transpose pair with the discrete forward operator while remaining consistent with the continuous PAT adjoint, enabling efficient least-squares solvers within the sampling procedure. We investigate the influence of temporal discretization, artificial boundary conditions, and adjoint consistency on posterior uncertainty and identify discretization strategies that avoid numerical artifacts. The framework is validated against exact posterior statistics, existing Bayesian PAT methods, and Hamiltonian Monte Carlo using the No-U-Turn Sampler (NUTS), and is demonstrated on a three-dimensional problem with approximately $2\times 10^5$ unknowns on a general finite-element domain. To the best of our knowledge, this is the first large-scale Bayesian PAT study on general three-dimensional finite-element geometries, and the methodology extends naturally to a broad class of linear PDE-constrained inverse problems.

Automated patient-derived noise power spectrum estimation in abdominal CT: a preliminary validation

Background. Established methods are available for measuring the noise power spectrum (NPS) in computed tomography (CT) by using uniform phantoms. However, in clinical practice, direct estimation of this metric from patient images is desirable for continuous image quality monitoring and protocol optimization. Methodology. An automated workflow was developed through the integration of liver segmentation with TotalSegmentator, optimized selection of square patches in potentially homogeneous regions using a greedy algorithm, and NPS computation employing the PyLinac library. CT images from five patients and one homogeneous phantom were analyzed using two reconstruction kernels (lung and standard). Validation was performed using Kullback–Leibler divergence ( DKL) to compare the NPS distributions obtained from patients and phantoms under equivalent acquisition conditions. Results. NPS estimation from abdominal images was feasible. In nearly all paired comparisons (phantom versus patient under identical acquisition and reconstruction conditions), DKL values between normalized NPS distributions were below 0.05, indicating strong agreement in the shape of the spatial noise distribution between the homogeneous phantom and clinical studies. Conclusion. An automated workflow for NPS estimation in the hepatic parenchyma is presented. The similarity between the characteristic NPS profile obtained from the patient images and that derived from a homogeneous phantom acquired under equivalent conditions was subsequently validated. These preliminary results support the potential integration of this metric as a complementary quantitative tool for CT quality control programs, directly on patient images.