Peer-reviewedLicence not statedJournal of Medical Imaging
Learning optimized sampling patterns for accelerated MRI reconstruction via diffusion models
- 1The University of Texas at Austin, Chandra Family Department of Electrical and Computer Engineering, Austin, Texas, United States
- 2University of California, Berkeley, Electrical Engineering and Computer Sciences, Berkeley, California, United States
Related
- Evaluation of accelerated whole-body diffusion weighted imaging with deep learning reconstruction in patients with metastatic prostate cancer: assessment of image quality and ADC estimates.
- Automated Brain Segmentation in Accelerated T2-Weighted MRI: Effects of Deep Learning-Based Reconstruction
- AI-accelerated compressed sensing and deep learning reconstruction for colon MRI: prospective validation of image quality and preliminary assessment of staging accuracy versus contrast-enhanced CT
- Deep learning reconstruction-applied PROPELLER diffusion-weighted imaging in uterine malignancies: a comparison study with MUSE
- PROSPECTIVE COMPARISON OF OPTIMIZED COMPRESSED SENSING AND GRAPPA-ACCELERATED 3D-TOF-MRA FOR EVALUATION OF UNTREATED INTRACRANIAL ANEURYSMS AT 1.5 T: A NON-INFERIORITY STUDY
BibTeX
@article{ravula2026learning,
title = {{Learning optimized sampling patterns for accelerated MRI reconstruction via diffusion models}},
author = {Sriram Ravula and Brett Levac and Yamin Arefeen and Alexandros G. Dimakis and Jonathan I. Tamir},
journal = {Journal of Medical Imaging},
year = {2026},
month = oct,
doi = {10.1117/1.jmi.13.s1.s11210},
url = {https://doi.org/10.1117/1.jmi.13.s1.s11210},
}
RIS
TY - JOUR
TI - Learning optimized sampling patterns for accelerated MRI reconstruction via diffusion models
AU - Sriram Ravula
AU - Brett Levac
AU - Yamin Arefeen
AU - Alexandros G. Dimakis
AU - Jonathan I. Tamir
PY - 2026
DA - 2026/10/28
JO - Journal of Medical Imaging
DO - 10.1117/1.jmi.13.s1.s11210
UR - https://doi.org/10.1117/1.jmi.13.s1.s11210
SN - 2329-4302
SN - 2329-4310
ER -