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|
|Number of pages||4|
|State||Published - Apr 2019|
|Event||16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy|
Duration: Apr 8 2019 → Apr 11 2019
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Conference||16th IEEE International Symposium on Biomedical Imaging, ISBI 2019|
|Period||4/8/19 → 4/11/19|
Bibliographical noteFunding Information:
This work was partially supported by NIH R00HL111410, P41EB015894, U01EB025144, P41EB027061; NSF CAREER CCF-1651825.
© 2019 IEEE.
Copyright 2019 Elsevier B.V., All rights reserved.
- Accelerated imaging
- Compressed sensing
- Coronary MRI
- Deep learning
- Image reconstruction
- Machine learning
- Neural networks
- Parallel imaging