Regularized SUPER-CAIPIRINHA: Accelerating 3D variable flip-angle T1 mapping with accurate and efficient reconstruction

Fan Yang; Jian Zhang; Guobin Li; Jiayu Zhu; Xin Tang; Chenxi Hu

doi:10.1002/mrm.29714

Regularized SUPER-CAIPIRINHA: Accelerating 3D variable flip-angle T₁ mapping with accurate and efficient reconstruction

Magn Reson Med. 2023 Oct;90(4):1380-1395. doi: 10.1002/mrm.29714. Epub 2023 May 29.

Authors

Fan Yang¹, Jian Zhang², Guobin Li², Jiayu Zhu², Xin Tang^{1

2}, Chenxi Hu¹

Affiliations

¹ Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
² United Imaging Healthcare Co., Ltd, Shanghai, People's Republic of China.

PMID: 37246893
DOI: 10.1002/mrm.29714

Abstract

Purpose: To propose an acceleration method for 3D variable flip-angle (VFA) T₁ mapping based on a technique called shift undersampling improves parametric mapping efficiency and resolution (SUPER).

Methods: The proposed method incorporates strategies of SUPER, controlled aliasing in volumetric parallel imaging (CAIPIRINHA), and total variation-based regularization to accelerate 3D VFA T₁ mapping. The k-space sampling grid of CAIPIRINHA is internally undersampled with SUPER along the contrast dimension. A proximal algorithm was developed to preserve the computational efficiency of SUPER in the presence of regularization. The regularized SUPER-CAIPIRINHA (rSUPER-CAIPIRINHA) was compared with low rank plus sparsity (L + S), reconstruction of principal component coefficient maps (REPCOM), and other SUPER-based methods via simulations and in vivo brain T₁ mapping. The results were assessed quantitatively with NRMSE and structural similarity index measure (SSIM), and qualitatively by two experienced reviewers.

Results: rSUPER-CAIPIRINHA achieved a lower NRMSE and higher SSIM than L + S (0.11 ± 0.01 vs. 0.19 ± 0.03, p < 0.001; 0.66 ± 0.05 vs. 0.37 ± 0.03, p < 0.001) and REPCOM (0.16 ± 0.02, p < 0.001; 0.46 ± 0.04, p < 0.001). The reconstruction time of rSUPER-CAIPIRINHA was 6% of L + S and 2% of REPCOM. For the qualitative comparison, rSUPER-CAIPIRINHA showed improvement of overall image quality and reductions of artifacts and blurring, although with a lower apparent SNR. Compared with 2D SUPER-SENSE, rSUPER-CAIPIRINHA significantly reduced NRMSE (0.11 ± 0.01 vs. 0.23 ± 0.04, p < 0.001) and generated less noisy reconstructions.

Conclusion: By incorporating SUPER, CAIPIRINHA, and regularization, rSUPER-CAIPIRINHA mitigated noise amplification, reduced artifacts and blurring, and achieved faster reconstructions compared with L + S and REPCOM. These advantages render 3D rSUPER-CAIPIRINHA VFA T₁ mapping potentially useful for clinical applications.

Keywords: CAIPIRINHA; SUPER; VFA T1 mapping; acceleration; model-based reconstruction; regularized SUPER-CAIPIRINHA.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Brain / diagnostic imaging
Image Enhancement / methods
Image Processing, Computer-Assisted / methods
Imaging, Three-Dimensional* / methods
Magnetic Resonance Imaging* / methods
Reproducibility of Results