Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRI

Motivation: The currently limited quality of accelerated cardiac cine reconstruction may potentially be improved by the emerging diffusion models, but the clinically unacceptable long processing time poses a challenge. Goal(s): To develop a clinically feasible diffusion-model-based reconstruction pipeline to improve the image quality of cine MRI. Approach: A multi-in-multi-out diffusion enhancement model together with fast inference strategies were developed to be used in conjunction with a reconstruction model. Results: The diffusion reconstruction reduced spatial and temporal blurring in prospectively undersampled clinical data, as validated by experts’ inspection. The 1.5s/video processing time enabled the approach to be applied in clinical scenarios. Impact: The proposed diffusion reconstruction pipeline provides a practical solution to cardiac cine reconstruction with enhanced quality for clinical usage. This pipeline may be transfered to the clinical application of other diffusion-based methods.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/0343
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRI

Xiao Chen, Terrence Chen, Shaoyan Pan, Shihan Qiu et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced MRI Techniques and Applications
article

Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRI

Xiao Chen, Terrence Chen, Shaoyan Pan, Shihan Qiu, Eric X. Chen, Yikang Liu, Shanhui Sun, Lin Zhao, Qi Liu, Jian Xu
article en

Abstract

Motivation: The currently limited quality of accelerated cardiac cine reconstruction may potentially be improved by the emerging diffusion models, but the clinically unacceptable long processing time poses a challenge. Goal(s): To develop a clinically feasible diffusion-model-based reconstruction pipeline to improve the image quality of cine MRI. Approach: A multi-in-multi-out diffusion enhancement model together with fast inference strategies were developed to be used in conjunction with a reconstruction model. Results: The diffusion reconstruction reduced spatial and temporal blurring in prospectively undersampled clinical data, as validated by experts’ inspection. The 1.5s/video processing time enabled the approach to be applied in clinical scenarios. Impact: The proposed diffusion reconstruction pipeline provides a practical solution to cardiac cine reconstruction with enhanced quality for clinical usage. This pipeline may be transfered to the clinical application of other diffusion-based methods.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Industry, innovation and infrastructure
Openalex Percentile: Top 63%
Advanced MRI Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.