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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Veröffentlicht in:	Magma (New York, N.Y.) Jg. 38; H. 2; S. 221 - 237
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.04.2025
Schlagworte:	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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Abstract	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.
AbstractList	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. 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. 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. 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. 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.OBJECTSpatio-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.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.MATERIALS AND METHODSThis 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.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.RESULTSThe 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.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.DISCUSSIONBy 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. 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.
Author	Iyer, Siddharth S. Chatnuntawech, Itthi Cao, Xiaozhi Sandino, Christopher M. Tong, Elizabeth Liao, Congyu Ruengchaijatuporn, Natthanan Setsompop, Kawin Yurt, Mahmut Schauman, S. Sophie
Author_xml	– sequence: 1 givenname: S. Sophie orcidid: 0000-0002-3744-2553 surname: Schauman fullname: Schauman, S. Sophie email: sophie.schauman.academic@gmail.com organization: Department of Radiology, Stanford University, Department of Clinical Neuroscience, Karolinska Institute – sequence: 2 givenname: Siddharth S. surname: Iyer fullname: Iyer, Siddharth S. organization: Department of Radiology, Stanford University, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology – sequence: 3 givenname: Christopher M. surname: Sandino fullname: Sandino, Christopher M. organization: Department of Electrical Engineering, Stanford University – sequence: 4 givenname: Mahmut surname: Yurt fullname: Yurt, Mahmut organization: Department of Electrical Engineering, Stanford University – sequence: 5 givenname: Xiaozhi surname: Cao fullname: Cao, Xiaozhi organization: Department of Radiology, Stanford University – sequence: 6 givenname: Congyu surname: Liao fullname: Liao, Congyu organization: Department of Radiology, Stanford University – sequence: 7 givenname: Natthanan surname: Ruengchaijatuporn fullname: Ruengchaijatuporn, Natthanan organization: Center of Excellence in Computational Molecular Biology, Chulalongkorn University, Center for Artificial Intelligence in Medicine, Chulalongkorn University – sequence: 8 givenname: Itthi surname: Chatnuntawech fullname: Chatnuntawech, Itthi organization: National Nanotechnology Center, National Science and Technology Development Agency – sequence: 9 givenname: Elizabeth surname: Tong fullname: Tong, Elizabeth organization: Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology – sequence: 10 givenname: Kawin surname: Setsompop fullname: Setsompop, Kawin organization: Department of Radiology, Stanford University, Department of Electrical Engineering, Stanford University
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Keywords	Deep learning Brain Algorithms Magnetic resonance imaging Image processing (computer-assisted)
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bioRxiv. 2023 Mar 28:2023.03.28.534431. doi: 10.1101/2023.03.28.534431.
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Snippet	Object Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or... Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively...
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SubjectTerms	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
Title	Deep learning initialized compressed sensing (Deli-CS) in volumetric spatio-temporal subspace reconstruction
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