Keywords: Diagnosis/Prediction, Quantitative Imaging
Motivation: This study investigates the application of AI-assisted Compressed Sensing (ACS) acceleration in shoulder fat Analysis and Calculation Technique (FACT) sequences, assessing its impact on image quality across different tissues
Goal(s): To evaluate the effect of ACS on image quality and quantitative accuracy when accelerating FACT sequences in shoulder.
Approach: Twenty-one patients were scanned using a PET/MR system with both FACT and FACT ACS protocols. Quantitative parameters, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared across various anatomical structures.
Results: The integration of FACT with ACS maintained image quality in most regions and significantly reduced scan times.
Impact: The study demonstrates that ACS improves the clinical efficiency of FACT sequences in shoulder imaging by reducing scan times without affecting quantitative metrics or image quality.
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