← Back to all news
arXiv AI

Pixelwise Uncertainty Quantification of Accelerated MRI Reconstruction

arXiv:2601. 13236v3 Announce Type: replace-cross Abstract: Parallel imaging techniques reduce magnetic resonance imaging (MRI) scan time but image quality degrades as the acceleration factor increases.

Related stories

arXiv AI
Aug 24

Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising

The paper introduces CM-RED, a fast MRI reconstruction method that combines a pretrained consistency model with the regularization by denoising framework. By integrating controlled noise injection into accelerated proximal gradient updates, CM-RED achieves high‑quality reconstructions on fastMRI knee and brain datasets with only four network function evaluations. It consistently outperforms existing diffusion‑ and consistency‑based approaches in quantitative metrics, visual fidelity, and robustness to hyperparameter changes.

By Merve G\"ulle, Junno Yun, Ya\c{s}ar Utku Al\c{c}alar, Mehmet Ak\c{c}akaya
diffusion
More like this →
arXiv Computer Vision
Sep 23

Uncertainty-Aware 3D Residual Wavelet Diffusion for Ultra Low-Field MRI Super-Resolution

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.

By Rui W. Yeow, Millie Beament, Fred Dick, Raha Razin, Martina Bocchetta, David L. Thomas, Henry F. J. Tregidgo, Daniel C. Alexander, James H. Cole
diffusioncomputer-vision
More like this →
arXiv Computer Vision
Sep 3

SliceBridge: context-consistent repair of corrupted slice intervals in T1-weighted MRI

SliceBridge is a new framework for repairing corrupted slice intervals in T1‑weighted MRI by using rectified flow matching conditioned on surrounding intact slices and their relative positions. It enforces through‑plane consistency by coupling slices within the interval through shared noise, flow time, and synchronized sampling, then reinserts the restored interval without altering other slices. In experiments on 9,877 brain MRIs and 581 external subjects, SliceBridge reduced slice‑to‑slice error by 32.9%–41.3% and improved SSIM across interval lengths, while lowering downstream segmentation volume estimation error from 1.95% to 1.05%.

By Jiheng Li, Michael E. Kim, Trent Schwartz, Gaurav Rudravaram, Derek B. Archer, Timothy J. Hohman, the Alzheimer's Disease Neuroimaging Initiative, Lianrui Zuo, Bennett A. Landman
diffusioncomputer-visionsafety
More like this →
arXiv Computer Vision
Sep 22

Anatomically Faithful Artifact Suppression in SENSE Accelerated Brain MRI

The study introduces ART‑Net, an anatomy‑aware residual attention network designed to refine four‑fold accelerated SENSE brain MRI. In a prospective paired study of 80 participants, ART‑Net achieved the highest peak signal‑to‑noise ratio and structural similarity index among evaluated methods, and it preserved anatomical fidelity with superior Dice coefficients for medial temporal and whole‑brain structures. Radiologist assessments also indicated improved gradient fidelity, regional contrast, and overall structural quality.

By Changjing Chai, Bin Huang, Libo Xu, Jian Zhou, Boyang Pan, Kristen W Yeom, Qiyong Gong, Nan-Jie Gong
computer-vision
More like this →