This approach integrates deep learning techniques for lipid segmentation in brain MRSI, improving accuracy and robustness.
Motivation: Accurate anatomical constraints are critical for removal of lipid signals from brain MRSI data, but obtaining them through lipid segmentation from anatomical scans remains challenging due to intensity heterogeneity, water-lipid ambiguity, and high-dimensionality of spatial-intensity distribution of lipid-dominating tissues. Goal(s): To develop an accurate lipid segmentation method for brain MRSI scans. Approach: We synergistically integrated a deep translation model to facilitate water-lipid separation, a subspace-based probability model to capture the global lipid spatial prior, and a deep diffusion-based lipid support learning to segment lipid-dominating tissues. Results: The proposed method was validated on experimental data, producing significantly improved lipid segmentation than existing methods. Impact: The proposed method will significantly improve the robustness of lipid removal and help generate high-quality metabolite maps from brain MRSI data, especially those obtained without lipid suppression.
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Zhang et al. (2025) studied this question.
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