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PurposeTo develop a physics‐guided deep learning (PG‐DL) reconstruction strategy based on a signal intensity informed multi‐coil (SIIM) encoding operator for highly‐accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR). MethodsFirst‐pass perfusion CMR acquires highly‐accelerated images with dynamically varying signal intensity/SNR following the administration of a gadolinium‐based contrast agent. Thus, using PG‐DL reconstruction with a conventional multi‐coil encoding operator leads to analogous signal intensity variations across different time‐frames at the network output, creating difficulties in generalization for varying SNR levels. We propose to use a SIIM encoding operator to capture the signal intensity/SNR variations across time‐frames in a reformulated encoding operator. This leads to a more uniform/flat contrast at the output of the PG‐DL network, facilitating generalizability across time‐frames. PG‐DL reconstruction with the proposed SIIM encoding operator is compared to PG‐DL with conventional encoding operator, split slice‐GRAPPA, locally low‐rank (LLR) regularized reconstruction, low‐rank plus sparse (L + S) reconstruction, and regularized ROCK‐SPIRiT. ResultsResults on highly accelerated free‐breathing first pass myocardial perfusion CMR at three‐fold SMS and four‐fold in‐plane acceleration show that the proposed method improves upon the reconstruction methods use for comparison. Substantial noise reduction is achieved compared to split slice‐GRAPPA, and aliasing artifacts reduction compared to LLR regularized reconstruction, L + S reconstruction and PG‐DL with conventional encoding. Furthermore, a qualitative reader study indicated that proposed method outperformed all methods. ConclusionPG‐DL reconstruction with the proposed SIIM encoding operator improves generalization across different time‐frames /SNRs in highly accelerated perfusion CMR.
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Improved simultaneous multislice cardiac MRI using readout concatenated k‐space SPIRiT (ROCK‐SPIRiT)
PurposeTo develop and evaluate a simultaneous multislice (SMS) reconstruction technique that provides noise reduction and leakage blocking for highly accelerated cardiac MRI. MethodsReadOutConcatenatedk‐space SPIRiT (ROCK‐SPIRiT) uses the concept of readout concatenation in image domain to represent SMS encoding, and performs coil self‐consistency as in SPIRiT‐type reconstruction in an extended k‐space, while allowing regularization for further denoising. The proposed method is implemented with and without regularization, and validated on retrospectively SMS‐accelerated cine imaging with three‐fold SMS and two‐fold in‐plane acceleration. ROCK‐SPIRiT is compared with two leakage‐blocking SMS reconstruction methods: readout‐SENSE‐GRAPPA and split slice–GRAPPA. Further evaluation and comparisons are performed using prospectively SMS‐accelerated cine imaging. ResultsResults on retrospectively three‐fold SMS and two‐fold in‐plane accelerated cine imaging show that ROCK‐SPIRiT without regularization significantly improves on existing methods in terms of PSNR (readout‐SENSE‐GRAPPA: 33.5 ± 3.2, split slice–GRAPPA: 34.1 ± 3.8, ROCK‐SPIRiT: 35.0 ± 3.3) and SSIM (readout‐SENSE‐GRAPPA: 84.4 ± 8.9, split slice–GRAPPA: 85.0 ± 8.9, ROCK‐SPIRiT: 88.2 ± 6.6 [in percentage]). Regularized ROCK‐SPIRiT significantly outperforms all methods, as characterized by these quantitative metrics (PSNR: 37.6 ± 3.8, SSIM: 94.2 ± 4.1 [in percentage]). The prospectively five‐fold SMS and two‐fold in‐plane accelerated data show that ROCK‐SPIRiT and regularized ROCK‐SPIRiT have visually improved image quality compared with existing methods. ConclusionThe proposed ROCK‐SPIRiT technique reduces noise and interslice leakage in accelerated SMS cardiac cine MRI, improving on existing methods both quantitatively and qualitatively.
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- Award ID(s):
- 1651825
- PAR ID:
- 10452186
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Magnetic Resonance in Medicine
- Volume:
- 85
- Issue:
- 6
- ISSN:
- 0740-3194
- Page Range / eLocation ID:
- p. 3036-3048
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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