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Abstract #4695

Deep Learning Reconstructed Reduced Field-of-View Diffusion Weighted Imaging in Rectal Cancer: Comparison of Image Quality

Yuqi Tan1, Zheng Ye1, Miaoqi Zhang2, Bo Zhang2, Chunchao Xia1, and Zhenlin Li1
1Radiology, West China Hospital of Sichuan University, Chengdu, China, 2GE Healthcare, MR Research, Beijing, China, Beijing, China

Synopsis

Keywords: AI/ML Image Reconstruction, Pelvis

Motivation: Reduced field-of-view DWI (rDWI) can improve the image quality (IQ) and lesion conspicuity in rectal cancer (RC) while reducing signal-to-noise ratio. Deep learning reconstruction (DLR) can denoise images. The feasibility and performance of DLR in rDWI for RC remains unclear.

Goal(s): To compare the IQ and apparent diffusion coefficient (ADC) values between DLR-rDWI and noDLR-rDWI in RC.

Approach: Objective and subjective IQ analysis were performed. ADC values were calculated. Results of IQ analysis and ADC between DLR-rDWI and noDLR-rDWI were compared.

Results: The IQ of DLR-rDWI was better than noDLR-rDWI (except lesion conspicuity, P=0.157). ADC values was not affected by DLR.

Impact: DLR-rDWI could be considered for inclusion in routine rectal MRI protocols. However, whether DLR can improve lesion conspicuity in RC should be further investigated with smaller lesions. The ability of DLR-rDWI in diagnosis and prediction for RC could be studied.

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Keywords