Abstract #5151

DMnet: A Reliable Magnetic Resonance Spectroscopy Quantification Network and the Verification on 5T scanners

Zhangren Tu¹, Jialue Zhang², Yanxing Yang³, Di Guo⁴, and Xiaobo Qu^5,6

¹Department of Electronic Science, School of Electronic Science and Technology, Xiamen University, Xiamen, China, ²Pen-Tung Sah Institute of Micro-nano Science and Technology, Xiamen University, Xiamen, China, ³United Imaging Healthcare, Shanghai, China, Shanghai, China, ⁴School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China, ⁵First Affiliated Hospital of Xiamen University, Xiamen, China, ⁶Xiamen University, Xiamen, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: To create a robust and generalized artificial intelligent method for quantification of MRS.

Goal(s): Our goal is to prove the reliability of the DMnet by illustrating its quantification results on data from 5T 1H-MRS in the brain.

Approach: Conducting separate Bland-Altman analyses between DM and typical quantification methods before and after accelerated acquisition, and examining concentration changes under varying resolution voxel conditions.

Results: The results indicate that DMNet and LCModel demonstrate high consistency in 8 healthy volunteers, with good agreement in quantified concentrations before and after acceleration by a factor of 2.58. Furthermore, the quantification results are more robust under varying resolution conditions.

Impact: The DMnet can quantitate MRS more quickly and robustly, even in scenarios with lower SNRs. This method has been integrated into the CloudBrain-MRS platform for convenient one-click access by healthcare professionals, further aiding clinical treatments.

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