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Abstract #5143

Evaluation of AI-Assisted Compressed Sensing for Accelerated Shoulder FACT MRI Sequences

Meng Yang1, Chuanshuai Tian1, Mingran Shao1, Zihan Wang1, Yutong Guo2, Zengping Lin2, Xianfeng Yang1, and Xin Zhang1
1Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321, Zhongshan Road, Nanjing, Jiangsu, 210008, China, Nanjing, China, 2Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China., ShangHai, China

Synopsis

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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