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.
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.
By Muge Zhang, Muhammad Ali Khaliq, Jamal Alsakran, Byeong Kil Lee, Jeeho Ryoo
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...
By Gideon N. L. Rouwendaal, Natascha Niessen, Hannah Eichhorn, Dirk H. J. Poot, Christine Preibisch, Julia A. Schnabel
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.
By Yuan Cao, Sumeet Dash, Antonia Zachariadis, Stefanie Schreiber, Katja Neumann, Jose Bernal
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.