Repeatability‐encouraging self‐supervised learning reconstruction for quantitative MRI

Abstract Purpose The clinical value of quantitative MRI hinges on its measurement repeatability. Deep learning methods to reconstruct undersampled quantitative MRI can accelerate reconstruction but do not aim to promote quantitative repeatability. This study proposes a repeatability‐encouraging self‐supervised learning (SSL) reconstruction method for quantitative MRI. Methods The proposed SSL reconstruction network minimized cross‐data‐consistency between two equally sized, mutually exclusive temporal subsets of k‐t‐space data, encouraging repeatability by enabling each subset's reconstruction to predict the other's k‐t‐space data. The method was evaluated on cardiac MR Multitasking T 1 mapping data and compared with supervised learning methods trained on full 60‐s inputs (Sup60) and on split 30‐s inputs (Sup30/30). Reconstruction quality was evaluated on full 60‐s inputs, comparing results to iterative wavelet‐regularized references using Bland–Altman limits of agreement (LOAs). Repeatability was evaluated by splitting the 60‐s data into two 30‐s inputs, evaluating T 1 differences between reconstructions from the two halves of the scan. Results On 60‐s inputs, the proposed method produced comparable‐quality images and T 1 maps to the Sup60 method, with T 1 values in general agreement with the wavelet reference (LOA Sup60 = ±75 ms, SSL = ±81 ms), whereas the Sup30/30 method generated blurrier results and showed poor T 1 agreement (LOA Sup30/30 = ±132 ms). On back‐to‐back 30‐s inputs, the proposed method had the best T 1 repeatability (coefficient of variation SSL = 6.3%, Sup60 = 12.0%, Sup30/30 = 6.9%). Of the three deep learning methods, only the SSL method produced sharp and repeatable images. Conclusion Without the need for labeled training data, the proposed SSL method demonstrated superior repeatability compared with supervised learning without sacrificing sharpness, and reduced reconstruction time versus iterative methods.