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Abstract #3841

AI-Enhanced Super-Resolution for Metabolite MRI Imaging

Erin Beate Bjørkeli1,2, Jonn Terje Geitung1,2, and Morteza Esmaeili1,3
1Department of Diagnostic Imaging, Akershus University Hospital, Lørenskog, Norway, 2Institute of Clinical Medicine, University of Oslo, Oslo, Norway, 3Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway

Synopsis

Keywords: Analysis/Processing, Spectroscopy, MRSI, Metabolite Maps

Motivation: Metabolite images from Magnetic Resonance Spectroscopic Imaging (MRSI) suffer from lower quality and reduced detail due to larger voxel sizes compared to anatomical MRI.

Goal(s): To improve the visual quality of MRSI by using a deep learning-based super-resolution approach to enhance spatial resolution.

Approach: Synthetic metabolic maps were generated using anatomical images from 350 patients. Our CNN-transformer model was trained on 70% of the dataset and tested on the remaining 30%, with performance compared to spline and nearest-neighbor methods.

Results: Our model significantly upscaled MRSI resolution to 128×128, achieving significantly higher PSNR, SSIM, and LPIPS scores than spline and nearest-neighbor (p<.01).

Impact: This improved SR approach significantly enhances metabolite map quality, offering clinicians a valuable tool for detailed neurological assessment.

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