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Abstract #1717

Evaluation of Variable-TE computed Diffusion Weighted Imaging Technique using Deep Learning based Noise Reduction

Hiroshi Kusahara1, Yuki Takai1, Kensuke Shinoda1, and Yoshimori Kassai1

1MRI development department, Canon Medical Systems Corporation, Tochigi, Japan

In this study to the authors adapted the variable-TE cDWI(vTE-cDWI) technique applying denoise approach with deep learning reconstruction(dDLR) to the abdominal region, using ADC-map, T2-map and T1-map with IR-based images. The algorithm under evaluation allows computing diffusion images for arbitrary combinations of TE, b-value and TI based on four acquisitions(4-points method). This technique was shown to generate vTE-cDWI with higher SNR compared to the acquired DWI, and dDLR increased the SNR more, as well as obtain ADC-maps and T1-maps with optimal TI for any arbitrary tissue. The clinical benefits of the method and results on volunteers are discussed.

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