SUSEP-Net: Simulation-Supervised and Contrastive Learning-based Deep Neural Networks for Susceptibility Source Separation

TL;DR

SUSEP-Net is a novel deep learning framework that improves susceptibility source separation in QSM by combining simulation-supervised training and contrastive learning, outperforming existing methods on simulated and real data.

Contribution

The paper introduces SUSEP-Net, a dual-branch U-net with a new training strategy and contrastive learning, advancing susceptibility source separation in QSM imaging.

Findings

SUSEP-Net outperforms three state-of-the-art methods in numerical metrics.

It enhances lesion contrast and reduces artifacts in pathological brains.

Validated on simulated, in vivo, and phantom data for robustness.

Abstract

Quantitative susceptibility mapping (QSM) provides a valuable tool for quantifying susceptibility distributions in human brains; however, two types of opposing susceptibility sources (i.e., paramagnetic and diamagnetic), may coexist in a single voxel, and cancel each other out in net QSM images. Susceptibility source separation techniques enable the extraction of sub-voxel information from QSM maps. This study proposes a novel SUSEP-Net for susceptibility source separation by training a dual-branch U-net with a simulation-supervised training strategy. In addition, a contrastive learning framework is included to explicitly impose similarity-based constraints between the branch-specific guidance features in specially-designed encoders and the latent features in the decoders. Comprehensive experiments were carried out on both simulated and in vivo data, including healthy subjects and patients with pathological conditions, to compare SUSEP-Net with three state-of-the-art susceptibility source separation methods (i.e., APART-QSM, \c{hi}-separation, and \c{hi}-sepnet). SUSEP-Net consistently showed improved results compared with the other three methods, with better numerical metrics, improved high-intensity hemorrhage and calcification lesion contrasts, and reduced artifacts in brains with pathological conditions. In addition, experiments on an agarose gel phantom data were conducted to validate the accuracy and the generalization capability of SUSEP-Net.