Abstract
PURPOSE: To accelerate coronary MRI acquisitions with arbitrary undersampling patterns by using a novel reconstruction algorithm that applies coil self-consistency using subject-specific neural networks.
METHODS: Self-consistent robust artificial-neural-networks for k-space interpolation (sRAKI) performs iterative parallel imaging reconstruction by enforcing self-consistency among coils. The approach bears similarity to SPIRiT, but extends the linear convolutions in SPIRiT to nonlinear interpolation using convolutional neural networks (CNNs). These CNNs are trained individually for each scan using the scan-specific autocalibrating signal (ACS) data. Reconstruction is performed by imposing the learned self-consistency and data-consistency, which enables sRAKI to support random undersampling patterns. Fully-sampled targeted right coronary artery MRI was acquired in six healthy subjects. The data were retrospectively undersampled, and reconstructed using SPIRiT, l1-SPIRiT and sRAKI for acceleration rates of 2 to 5. Additionally, prospectively undersampled whole-heart coronary MRI was acquired to further evaluate reconstruction performance.
RESULTS: sRAKI reduces noise amplification and blurring artifacts compared with SPIRiT and l1-SPIRiT, especially at high acceleration rates in targeted coronary MRI. Quantitative analysis shows that sRAKI outperforms these techniques in terms of normalized mean-squared-error (~44% and ~21% over SPIRiT and [Formula: see text]-SPIRiT at rate 5) and vessel sharpness (~10% and ~20% over SPIRiT and l1-SPIRiT at rate 5). Whole-heart data shows the sharpest coronary arteries when resolved using sRAKI, with 11% and 15% improvement in vessel sharpness over SPIRiT and l1-SPIRiT, respectively.
CONCLUSION: sRAKI is a database-free neural network-based reconstruction technique that may further accelerate coronary MRI with arbitrary undersampling patterns, while improving noise resilience over linear parallel imaging and image sharpness over l1 regularization techniques.
| Original language | English (US) |
|---|---|
| Article number | e0229418 |
| Journal | PloS one |
| Volume | 15 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 1 2020 |
Bibliographical note
Funding Information:Author M. A. has received the following grants: National Institutes of Health (NIH) R00HL111410, National Science Foundation (NSF) CCF-1651825. Author K.U. has received the following grants: NIH P41EB015894, NIH U01EB025144, NIH P41EB027061. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Funding Information:
Funding:AuthorM.A.hasreceivedthefollowing grants:NationalInstitutesofHealth(NIH) R00HL111410,NationalScienceFoundation(NSF) CCF-1651825.AuthorK.U.hasreceivedthe followinggrants:NIHP41EB015894,NIH U01EB025144,NIHP41EB027061.Thefunders hadnoroleinstudydesign,datacollectionand
Publisher Copyright:
© 2020 Hosseini et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords
- Adult
- Algorithms
- Coronary Vessels/anatomy & histology
- Female
- Follow-Up Studies
- Heart/anatomy & histology
- Humans
- Image Processing, Computer-Assisted/methods
- Magnetic Resonance Imaging/methods
- Male
- Neural Networks, Computer
- Prospective Studies
- Retrospective Studies
Center for Magnetic Resonance Research (CMRR) tags
- IRP
- BI
- P41
PubMed: MeSH publication types
- Research Support, U.S. Gov't, Non-P.H.S.
- Journal Article
- Research Support, N.I.H., Extramural
