arXiv Computer Vision
C-WFDM: A ControlNet-Guided Wavelet-Fusion Diffusion Model for Multimodal Brain MRI Completion
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arXiv Computer Vision
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Learning Sparse Latent Predictive Foundation Model for Multimodal Neuroimaging
The paper introduces Neuro‑JEPA, a sparse multimodal foundation model that learns unified representations of brain MRI across T1w, T2w, and FLAIR sequences using a latent predictive objective and a Mixture‑of‑Experts architecture. It was pretrained on over 1.5 million scans from 428,647 studies and systematically evaluates architectural, masking, objective, and sparsity choices for robust multimodal representation learning. Across 47 tasks from three health systems and 12 public datasets, Neuro‑JEPA consistently outperforms a simple CNN baseline, demonstrating its effectiveness for both clinical and research applications.
arXiv Computer Vision
Aug 28
Tissue-Mixture Entropy-Weighted Reconstruction for Partial-Volume-Aware Brain MRI Super-Resolution
The paper introduces AGW-PBR, a brain MRI super‑resolution method that emphasizes tissue‑transition regions affected by the partial‑volume effect. It uses a low‑resolution backbone guided by Sobel edges, soft latent‑basis assignment, and grid‑anchored warping, while training‑time weights are derived from tissue‑mixture entropy computed from registered T1/T2/PD images. Experiments on IXI T2‑weighted scans at 2×, 4×, and 6× show improved full‑image reconstruction and regional fidelity, and the backbone also performs well on fastMRI data without PVE supervision.
arXiv Computer Vision
Sep 2
Conditional Flow Matching for Cross-Field MRI Harmonisation
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...
arXiv Computer Vision
Oct 28
Retrospective macromolecular pool fraction mapping from conventional clinical MRI: a physics-guided self-supervised deep learning framework
The study introduces a physics‑guided, self‑supervised deep learning framework that estimates macromolecular pool fraction (MPF) maps directly from conventional clinical brain MRI sequences (T1‑, T2‑, and FLAIR) without requiring specialized quantitative magnetization transfer scans. By embedding an analytical two‑pool magnetization transfer model and conditioning on acquisition parameters, the method was trained on 7,705 multisite, multivendor MRI sessions and achieved consistent MPF values across vendors and protocols, with a 35.6% reduction in lesions of demyelinating disease compared to normal‑appearing white matter. Cross‑protocol validation in a healthy volunteer showed a mean voxel‑wise absolute difference of 7.7% and a concordance correlation coefficient of 0.888, demonstrating robust cross‑protocol performance.
arXiv AI
Oct 15
Generalizable field of view extension for diffusion MRI via conditional flow matching
arXiv:2610.17401v1 Announce Type: new Abstract: Incomplete field-of-view (FOV) diffusion MRI (dMRI) acquisitions compromise whole-brain white matter analysis by excluding brain regions from tractogra...
arXiv AI
Sep 25
CATCH: Counterfactual Anatomical Tissue Inpainting with Conditional Haar Diffusion
CATCH is a conditional 3D diffusion model operating in an invertible Haar-wavelet domain designed to inpaint masked regions in T1‑weighted brain MRI with plausible, tumor‑free tissue while preserving observed anatomy. The model’s denoiser uses noisy target coefficients, voided‑image coefficients, and a signed mask, guided by tumor‑excluded wavelet reconstruction and a hole‑focused loss, and hard compositing ensures observed voxels remain unchanged. Experiments on BraTS data show that a weighted mixture of tumor‑derived, irregular‑blob, and ellipsoidal masks yields the best performance, achieving higher SSIM, PSNR, and lower MSE compared to fixed or random augmentation baselines.