arXiv:2610.08983v1 Announce Type: new Abstract: Generative inpainting of brain MRI volumes is essential for synthesizing healthy tissue in pathological regions, improving the accuracy and reliability...
arXiv:2609.00960v1 Announce Type: new Abstract: Magnetic resonance images of the same subject look markedly different across field strengths, which complicates the comparison and pooling of data acro...
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.
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.
RARF is a task‑agnostic, region‑aware rectified flow framework designed for 3D brain MRI inpainting. It limits stochastic interpolation to the inpainting region while keeping observed voxels fixed, using a 3D neural network that processes a partially voided image, Gaussian noise, a mask, and a timestep. The model is trained with masked flow‑matching and reconstruction‑consistency objectives, and during inference it transports noise toward a plausible reconstruction that preserves anatomical consistency, achieving competitive results on the BraTS Inpainting Challenge 2026.
FlowMoDL is an unrolled neural network designed for highly accelerated 4D flow MRI reconstruction, optimizing both anatomical magnitude and phase-derived velocity accuracy. It alternates a learned (3+1)D spatiotemporal denoiser with conjugate‑gradient data‑consistency updates, using a dual‑pathway conditioning scheme to handle acceleration factors from 10× to 50×. Trained with a deep‑supervision composite loss that penalizes velocity magnitude and angular errors, FlowMoDL outperforms classical and deep‑learning baselines on the multi‑center CMRx4DFlow dataset, achieving superior gradient‑step efficiency and robust convergence across all acceleration factors.
arXiv:2507. 21164v2 Announce Type: replace-cross Abstract: Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are rare or not available.
arXiv:2606.13315v2 Announce Type: replace Abstract: MRI self-supervised learning (SSL) has largely emphasized segmentation and dense prediction, while controlled studies of how alternative pretrainin...
arXiv:2606. 30374v1 Announce Type: cross Abstract: Multimodal MRI is essential for accurate brain tumor segmentation.
arXiv:2603. 03710v3 Announce Type: replace-cross Abstract: Zero-shot MRI reconstruction relies on generative priors, but single-modality unconditional priors produce hallucinations under severe ill-posedness.
arXiv:2605. 00941v5 Announce Type: replace Abstract: Flow matching provides a highly effective framework for generative modeling, yet estimating the uncertainty of its generated samples remains a fundamental challenge.
MIRTO is an evaluation protocol for unsupervised anomaly segmentation in brain MRI that explicitly documents key methodological choices—such as registration alignment, threshold setting, and false‑positive budgeting—and measures their impact. It applies a registration check, uses validation data for thresholding, reports realized false‑positive volumes, and repeats each comparison across 15,552 evaluation pipelines with bootstrap intervals. In a study on four UAD methods and 312 BraTS 2020 subjects, MIRTO revealed that an axis‑order mismatch dramatically lowered a diffusion model’s voxel AUROC, and that many performance differences were driven by lesion definition and threshold transfer rather than model quality.