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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Vydané v:	Magma (New York, N.Y.) Ročník 38; číslo 2; s. 221 - 237
Hlavní autori:	Schauman, S. Sophie, Iyer, Siddharth S., Sandino, Christopher M., Yurt, Mahmut, Cao, Xiaozhi, Liao, Congyu, Ruengchaijatuporn, Natthanan, Chatnuntawech, Itthi, Tong, Elizabeth, Setsompop, Kawin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.04.2025
Predmet:	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
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Popis
Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1352-8661 0968-5243 1352-8661
DOI:	10.1007/s10334-024-01222-2