Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects. In supervised machine learning frameworks trained on simulated data, discrepancies between the noise characteristics of simulated and acquired signals introduce a form of covariate shift, whereby the input signal distribution differs between training and inference.
arXiv:2606. 02044v1 Announce Type: new Abstract: Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects.
Brain tissue microstructure estimation with machine learning provides higher computational efficiency than conventional fitting. However, machine learning still presents important limitations that hamper its clinical utility.
arXiv:2606. 18354v1 Announce Type: cross Abstract: Recent advances in generative machine learning models have significantly improved medical imaging, offering promising solutions for data augmentation, privacy preservation, and improved model generalization.
Objective: Radiomic texture features are usually computed in voxel-index neighborhoods, implicitly assuming isotropic spatial relationships. In anisotropic images, this can confound voxel geometry with interpolation-induced signal changes.
arXiv:2608.08693v2 Announce Type: replace Abstract: Quantitative T2* maps have strong potential for biomarker discovery but are limited by long scan times, rendering them impractical in clinical sett...
The study introduces a voxel-spacing-aware radiomic framework that incorporates physical geometry into texture computation without resampling, modifying PyRadiomics to preserve native image signals. Four extraction configurations—native non-resampled (NR), isotropic resampling (RS), voxel-spacing-aware (VS), and fake-isotropic preprocessing (FK)—were compared across 685 CT pulmonary nodules and 209 MRI breast cases, evaluating 196 radiomic descriptors. Results show that VS closely matches NR (median ICC(A,1) ≈0.998) while RS and FK exhibit lower agreement, indicating that spacing metadata alone can significantly influence radiomic features.
arXiv:2606. 28513v1 Announce Type: cross Abstract: Positron emission tomography (PET) seeks to balance diagnostic quality with ra-diation dose.
arXiv:2609.39184v1 Announce Type: new Abstract: Fiber orientation and compartmental microstructure are central to the characterization of white matter tissue in diffusion MRI, yet existing methods ei...
The paper introduces a synthetic training framework for segmenting long‑tail haemorrhagic lesions such as cerebral microbleeds (CMBs) and cortical superficial siderosis (cSS) without requiring real lesion annotations. By starting from anatomical brain parcellations, the method applies spatial augmentation, voxel resampling, and procedural insertion of lesion labels guided by clinical priors, then synthesises images with randomized intensity, blurring, and Rician noise. Models trained on these synthetic image‑label pairs outperformed classical filter baselines, achieving higher AUPRC and AUROC for both cSS and CMB segmentation.
Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution input to a high-resolution target. This overlooks a key property of MRI acquisition physics: spatial resolution and signal-to-noise ratio (SNR) are inherently coupled, making any given low-resolution scan merely one of many possible realizations under varying acquisition trade-offs.
arXiv:2606. 15457v1 Announce Type: cross Abstract: 3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns.
The paper introduces a 3D residual wavelet diffusion model for super‑resolving ultra low‑field MRI scans. By using a lossless wavelet reparameterisation, residual shifting, and domain randomisation, the method fits whole‑brain data on a single GPU, speeds up sampling, and generalises across scanners. It achieves volumetric accuracy comparable to leading regression approaches while producing per‑voxel uncertainty maps that reveal under‑determined regions and preserves disease‑relevant atrophy in cognitively impaired subjects.