ACCELERATED CORONARY MRI USING 3D SPIRIT-RAKI WITH SPARSITY REGULARIZATION.

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ID: 80492
2019
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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 SPIRiT-RAKI, 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.
Reference Key
hossein-hosseini2019acceleratedproceedings Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hossein Hosseini, Seyed Amir;Moeller, Steen;Weingärtner, Sebastian;Uǧurbil, Kȃmil;Akçakaya, Mehmet;
Journal proceedings ieee international symposium on biomedical imaging
Year 2019
DOI
10.1109/ISBI.2019.8759459
URL
Keywords
Deep learning Machine learning neural networks coronary mri accelerated imaging compressed sensing image reconstruction parallel imaging

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