Optimized reconstruction of undersampled Dixon sequences using new memory-efficient unrolled deep neural networks: HalfVarNet and HalfDIRCN.

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Abstract

Fat fraction (FF) quantification in individual muscles using quantitative MRI is of major importance for monitoring disease progression and assessing disease severity in neuromuscular diseases. Undersampling of MRI acquisitions is commonly used to reduce scanning time. The present paper introduces novel unrolled neural networks for the reconstruction of undersampled MRI acquisitions. These networks are designed with the aim of maintaining accurate FF quantification while reducing reconstruction time and memory usage. The proposed approach relies on a combination of a simplified architecture (Half U-Net) with unrolled networks that achieved high performance in the well-known FastMRI challenge (variational network [VarNet] and densely interconnected residual cascading network [DIRCN]). The algorithms were trained and evaluated using 3D MRI Dixon acquisitions of the thigh from controls and patients with neuromuscular diseases. The study was performed by applying a retrospective undersampling with acceleration factors of 4 and 8. Reconstructed images were used to computed FF maps. Results disclose that the novel unrolled neural networks were able to maintain reconstruction, biomarker assessment, and segmentation quality while reducing memory usage by 24% to 16% and reducing reconstruction time from 21% to 17%. Using an acceleration factor of 8, the proposed algorithms, HalfVarNet and HalfDIRCN, achieved structural similarity index (SSIM) scores of 93.76 ± 0.38 and 94.95 ± 0.32, mean squared error (MSE) values of 12.76 ± 1.08 × 10^-2 and 10.25 ± 0.87 × 10^-2, and a relative FF quadratic error of 0.23 ± 0.02% and 0.17 ± 0.02%, respectively. The proposed method enables time and memory-efficient reconstruction of undersampled 3D MRI data, supporting its potential for clinical application.

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