JSENSE-Pro: Joint sensitivity estimation and image reconstruction in parallel imaging using pre-learned subspaces of coil sensitivity functions

Lihong Tang, Yibo Zhao, Yudu Li, Rong Guo, Bingyang Cai, Jia Wang, Yao Li, Zhi Pei Liang, Xi Peng, Jie Luo

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To improve calibrationless parallel imaging using pre-learned subspaces of coil sensitivity functions. Theory and Methods: A subspace-based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method. Results: With no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state-of-the-art methods including JSENSE, DeepSENSE, P-LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T2w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system. Conclusion: A subspace-based method named JSENSE-Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited.

Original languageEnglish (US)
Pages (from-to)1531-1542
Number of pages12
JournalMagnetic Resonance in Medicine
Volume89
Issue number4
DOIs
StatePublished - Apr 2023

Keywords

  • joint reconstruction
  • learned-subspace
  • parallel imaging
  • probabilistic subspace model
  • sensitivity encoding

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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