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

Omer Burak Demirel, Burhaneddin Yaman, Chetan Shenoy, Steen Moeller, Sebastian Weingärtner, Mehmet Akçakaya

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)308-321
Number of pages14
JournalMagnetic resonance in medicine
Volume89
Issue number1
DOIs
StatePublished - Jan 2023

Bibliographical note

Funding Information:
NIH, Grant numbers: R01HL153146, R21EB028369, P41EB027061; NSF, Grant number: CAREER CCF‐1651825; NWO Start‐Up Grant STU.019.024, 4TU Federation, Health Technology Programme TU Delft ‐ LUMC; AHA Predoctoral Fellowship.

Funding Information:
information 4TU Federation, American Heart Association, Grant/Award Number: Predoctoral Fellowship; Health Technology Programme TU Delft - LUMC, National Heart, Lung, and Blood Institute, Grant/Award Number: R01HL153146; National Institute of Biomedical Imaging and Bioengineering, Grant/Award Numbers: P41EB027061; R21EB028369; National Science Foundation, Grant/Award Number: CAREER CCF-1651825; Stichting voor de Technische Wetenschappen, Grant/Award Number: STU.019.024

Publisher Copyright:
© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.

Keywords

  • accelerated imaging
  • cardiac MRI
  • coil maps
  • deep learning
  • image reconstruction
  • myocardial perfusion
  • Artifacts
  • Perfusion
  • Image Processing, Computer-Assisted/methods
  • Magnetic Resonance Imaging/methods
  • Deep Learning
  • Physics

Center for Magnetic Resonance Research (CMRR) tags

  • IRP
  • BI
  • P41

PubMed: MeSH publication types

  • Journal Article
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

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