A guaranteed convergence analysis for the projected fast iterative soft-thresholding algorithm in parallel MRI

Sparse sampling and parallel imaging techniques are two effective approaches to alleviate the lengthy magnetic resonance imaging (MRI) data acquisition problem. Promising data recoveries can be obtained from a few MRI samples with the help of sparse reconstruction models. To solve the optimization models, proper algorithms are indispensable. The pFISTA, a simple and efficient algorithm, has been successfully extended to parallel imaging. However, its convergence criterion is still an open question. Besides, the existing convergence criterion of single-coil pFISTA cannot be applied to the parallel imaging pFISTA, which, therefore, imposes confusions and difficulties on users about determining the only parameter -step size. In this work, we provide the guaranteed convergence analysis of the parallel imaging version pFISTA to solve the two well-known parallel imaging reconstruction models, SENSE and SPIRiT. Along with the convergence analysis, we provide recommended step size values for SENSE and SPIRiT reconstructions to obtain fast and promising reconstructions. Experiments on in vivo brain images demonstrate the validity of the convergence criterion. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Sparse sampling and parallel imaging techniques are effective approaches to alleviate the lengthy MRI data acquisition problem. Promising data recoveries can be obtained from a few MRI samples with the help of sparse reconstruction models. In this work, the guaranteed convergence analysis of the parallel imaging version pFISTA for solving SENSE and SPIRiT reconstruction models is provided, along with recommended step size values for fast and promising reconstructions.