Accelerated coronary mri using 3d spirit-raki with sparsity regularization

Seyed Amir Hossein Hosseini; Steen Moeller; Sebastian Weingartner; Kamil Ugurbil; Mehmet Akcakaya

doi:10.1109/ISBI.2019.8759459

Accelerated coronary mri using 3d spirit-raki with sparsity regularization

Seyed Amir Hossein Hosseini, Steen Moeller, Sebastian Weingartner, Kamil Ugurbil, Mehmet Akcakaya

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Scopus citations

Abstract

Coronary MRI is a non-invasive radiation-free imaging tool for the diagnosis of coronary artery disease. One of its limitations is the long scan time, due to the need for high resolution imaging in the presence of respiratory and cardiac motions. Machine learning (ML) methods have been recently utilized to accelerate MRI. In particular, a scan-specific ML technique, called Robust Artifical-neural-network for k-space Interpolation (RAKI) has shown promise in cardiac MRI. However, it requires uniform undersampling. In this study, we sought to extend this approach to arbitrary sampling patterns, using coil self-consistency. This technique, called SPIRiTRAKI, utilizes scan-specific convolutional neural networks to nonlinearly enforce coil self-consistency. Additionally, regularization terms can also be incorporated. SPIRiT-RAKI was used to accelerate right coronary MRI. Reconstructions were compared to SPIRiT for different undersampling patterns and acceleration rates. Results show SPIRiT-RAKI reduces residual aliasing and blurring artifacts compared to SPIRiT.

Original language	English (US)
Title of host publication	ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
Publisher	IEEE Computer Society
Pages	1692-1695
Number of pages	4
ISBN (Electronic)	9781538636411
DOIs	https://doi.org/10.1109/ISBI.2019.8759459
State	Published - Apr 2019
Event	16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy Duration: Apr 8 2019 → Apr 11 2019

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2019-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/Territory	Italy
City	Venice
Period	4/8/19 → 4/11/19

Bibliographical note

Funding Information:
This work was partially supported by NIH R00HL111410, P41EB015894, U01EB025144, P41EB027061; NSF CAREER CCF-1651825.

Publisher Copyright:
© 2019 IEEE.

Keywords

Accelerated imaging
Compressed sensing
Coronary MRI
Deep learning
Image reconstruction
Machine learning
Neural networks
Parallel imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ISBI.2019.8759459

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Cite this

Hosseini, S. A. H., Moeller, S., Weingartner, S., Ugurbil, K., & Akcakaya, M. (2019). Accelerated coronary mri using 3d spirit-raki with sparsity regularization. In ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging (pp. 1692-1695). Article 8759459 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2019.8759459

Accelerated coronary mri using 3d spirit-raki with sparsity regularization. / Hosseini, Seyed Amir Hossein; Moeller, Steen; Weingartner, Sebastian et al.
ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2019. p. 1692-1695 8759459 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hosseini, SAH, Moeller, S, Weingartner, S, Ugurbil, K & Akcakaya, M 2019, Accelerated coronary mri using 3d spirit-raki with sparsity regularization. in ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging., 8759459, Proceedings - International Symposium on Biomedical Imaging, vol. 2019-April, IEEE Computer Society, pp. 1692-1695, 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, Italy, 4/8/19. https://doi.org/10.1109/ISBI.2019.8759459

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abstract = "Coronary MRI is a non-invasive radiation-free imaging tool for the diagnosis of coronary artery disease. One of its limitations is the long scan time, due to the need for high resolution imaging in the presence of respiratory and cardiac motions. Machine learning (ML) methods have been recently utilized to accelerate MRI. In particular, a scan-specific ML technique, called Robust Artifical-neural-network for k-space Interpolation (RAKI) has shown promise in cardiac MRI. However, it requires uniform undersampling. In this study, we sought to extend this approach to arbitrary sampling patterns, using coil self-consistency. This technique, called SPIRiTRAKI, utilizes scan-specific convolutional neural networks to nonlinearly enforce coil self-consistency. Additionally, regularization terms can also be incorporated. SPIRiT-RAKI was used to accelerate right coronary MRI. Reconstructions were compared to SPIRiT for different undersampling patterns and acceleration rates. Results show SPIRiT-RAKI reduces residual aliasing and blurring artifacts compared to SPIRiT.",

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Accelerated coronary mri using 3d spirit-raki with sparsity regularization

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access

OpenUrl availability

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