Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction
Motivation: Enhancing the signal-to-noise (SNR) of MRSI signals will improve detection sensitivity for clinical and research applications.
Goal(s): To leverage recent advances in machine learning to improve the SNR of MRSI.
Approach: A physics-based machine learning method was proposed for denoising MRSI data, incorporating side information such as PD, T1, T2, QSM, and MWF. A deep conditional generative model was trained to capture the relationship between water and metabolite signals. Denoised signals were obtained via subspace-assisted Langevin dynamics.
Results: The method was validated using both simulated and in vivo data obtained from healthy subjects and patients with tumor or stroke, demonstrating substantial noise reduction.
Impact: This proposed method provides an effective way to enhance the sensitivity of MRSI using physics-based machine learning incorporating side information. The method may further enhance the practical utility of MRSI for research and clinical applications.
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