MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping
This work addresses accuracy issues in QSM for brain disease diagnosis, representing an incremental improvement by combining physical models with deep learning.
The paper tackled the ill-posed inverse problem in quantitative susceptibility mapping (QSM) by proposing MoDL-QSM, a model-based deep learning architecture that integrates physical models to reduce artifacts and improve accuracy, achieving superior performance in metrics like RSME, SSIM, and HFEN compared to existing deep learning methods.
Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (ki13,ki23,ki33) and the acquired single-orientation phase. The convolution neural networks are embedded into the physical model to learn a regularization term containing prior information. ki33 and phase induced by ki13 and ki23 terms were used as the labels for network training. Quantitative evaluation metrics (RSME, SSIM, and HFEN) were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications.