Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction

Object Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction time using deep learning. Materials and methods Th...

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Published in:Magma (New York, N.Y.) Vol. 38; no. 2; pp. 221 - 237
Main Authors: Schauman, S. Sophie, Iyer, Siddharth S., Sandino, Christopher M., Yurt, Mahmut, Cao, Xiaozhi, Liao, Congyu, Ruengchaijatuporn, Natthanan, Chatnuntawech, Itthi, Tong, Elizabeth, Setsompop, Kawin
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.04.2025
Subjects:
Algorithms
Basic Science - Reconstruction algorithms and artificial intelligence
Biomedical Engineering and Bioengineering
Brain - diagnostic imaging
Brain Mapping - methods
Computer Appl. in Life Sciences
Data Compression - methods
Deep Learning
Health Informatics
Humans
Image Processing, Computer-Assisted - methods
Imaging
Imaging, Three-Dimensional
Magnetic Resonance Imaging - methods
Medicine
Medicine & Public Health
Phantoms, Imaging
Radiology
Research Article
Solid State Physics
Spatio-Temporal Analysis
Deep learning
Brain
Algorithms
Magnetic resonance imaging
Image processing (computer-assisted)
ISSN:1352-8661, 0968-5243, 1352-8661
Online Access:Get full text
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Summary:Object Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction time using deep learning. Materials and methods This study focuses on accelerating the reconstruction of volumetric multi-axis spiral projection MRF, aiming for whole-brain T1 and T2 mapping, while ensuring a streamlined approach compatible with clinical requirements. To optimize reconstruction time, the traditional method is first revamped with a memory-efficient GPU implementation. Deep Learning Initialized Compressed Sensing (Deli-CS) is then introduced, which initiates iterative reconstruction with a DL-generated seed point, reducing the number of iterations needed for convergence. Results The full reconstruction process for volumetric multi-axis spiral projection MRF is completed in just 20 min compared to over 2 h for the previously published implementation. Comparative analysis demonstrates Deli-CS’s efficiency in expediting iterative reconstruction while maintaining high-quality results. Discussion By offering a rapid warm start to the iterative reconstruction algorithm, this method substantially reduces processing time while preserving reconstruction quality. Its successful implementation paves the way for advanced spatio-temporal MRI techniques, addressing the challenge of extensive reconstruction times and ensuring efficient, high-quality imaging in a streamlined manner.
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ISSN:1352-8661
0968-5243
1352-8661
DOI:10.1007/s10334-024-01222-2