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Abstract #1429

Scan Time Reduction in High Resolution Knee Imaging using Compressed Sensing and Denoising Deep Learning Reconstruction

Mitsue Miyazaki1,2, Masaaki Umeda2, Shinichi Kitane2, Cheng Ouyang1, Sheronda Statum1,3, Won C Bae1,3, and Christine Chung1,3

1Radiology, UCSD, La Jolla, CA, United States, 2Canon Medical Systems Corp., Otawara, Japan, 3VA San Diego Healthcare System, La Jolla, CA, United States

Compressed sensing (CS) uses undersampling at the expense of image blurring and increased noise. We have developed denoising deep learning reconstruction (dDLR) to reduce noise and regain signal-to-noise ratio (SNR) in highly undersampled (4-4.5x) CS images. Feasibility study was performed in fat-suppressed T2 and proton density knee images, by evaluating SNR and image quality (sharpness, blurring, and artifact scores). Compared to reference (no CS or dDLR), images obtained with CS had lower SNR (by 25 to 40%) and image scores due to sharpness and blurring. After processing with dDLR, SNR and image scores were restored the reference levels.

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