ISDU-QSMNet: Iteration Specific Denoising With Unshared Weights for Improved QSM Reconstruction

Quantitative susceptibility mapping (QSM) estimates the tissue magnetic susceptibility from magnetic resonance (MR) phase measurements by solving an inverse problem. This study introduces iteration specific denoising via unshared weights for QSM reconstruction, also referred to as ISDU-QSMNet, an end-to-end model-based deep learning framework designed to effectively solve the inverse problem of QSM reconstruction from the local field. ISDU-QSMNet introduces significant modifications to existing model-based deep learning approaches by incorporating unshared denoiser weights and random subset sampling during training, leading to a more powerful, robust, and training-efficient model that improves the performance with full training data, reduces the overall training time, and effectively handles different datasets. The proposed method was evaluated against other model-based deep learning approaches, such as learned proximal networks for QSM reconstruction (LPCNN) and Schatten p-norm driven regularizer-based QSM reconstruction (SpiNet-QSM), as well as pure deep learning methods, such as QSMnet, DeepQSM, and xQSM, by performing reconstructions on 94 imaging volumes with varying acquisition parameters under two scenarios: full training data and limited training data. In the full training data scenario, the proposed approach demonstrated substantial improvements over all existing methods in both model-based and pure deep learning categories, achieving significant reductions in high-frequency error norm (HFEN) by up to 3.5% across 60 data volumes. In the limited training data scenario, the proposed approach matched the performance of state-of-the-art model-based deep learning models. Additionally, it demonstrated strong generalization capabilities by effectively handling data with different acquisition parameters and consistently performed well in ROI analysis.