Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR

Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR

Demirel, Omer Burak (University of Minnesota)
Yaman, Burhaneddin (University of Minnesota)
Shenoy, Chetan (University of Minnesota)
Moeller, Steen (University of Minnesota)
Weingärtner, S.D. (TU Delft ImPhys/Computational Imaging; TU Delft ImPhys/Medical Imaging)
Akçakaya, Mehmet (University of Minnesota)

2022

Purpose: To 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). Methods: First-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. Results: Results 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. Conclusion: PG-DL reconstruction with the proposed SIIM encoding operator improves generalization across different time-frames /SNRs in highly accelerated perfusion CMR.

accelerated imaging
cardiac MRI
coil maps
deep learning
image reconstruction
myocardial perfusion

http://resolver.tudelft.nl/uuid:44a7598e-6222-470d-9092-93e146d026e4

https://doi.org/10.1002/mrm.29453

0740-3194

Magnetic Resonance in Medicine, 89 (1), 308-321

Institutional Repository

journal article

© 2022 Omer Burak Demirel, Burhaneddin Yaman, Chetan Shenoy, Steen Moeller, S.D. Weingärtner, Mehmet Akçakaya