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Abstract #0440

Accelerated multi-shot diffusion MRI using deep learning denoising

Or Alus1, Maria El Homsi2, Lee Rodriguez2, Yousef Mazaheri1,2, Youngwook Kee1, Iva Petkovska2, and Ricardo Otazo1,2
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

Synopsis

Keywords: Pelvis, Diffusion/other diffusion imaging techniques, image reconstruction

Multi-shot EPI is commonly used to compensate for geometric distortions and increase spatial resolution in body diffusion MRI, with a price tag of longer scan times. This work presents an alternative technique to k-space undersampling to accelerate the acquisition, which is based on reducing the number of repetitions at high b-value and denoising the resulting images using a convolutional neural network. The proposed deep learning denoising technique is demonstrated to accelerate the acquisition of multi-shot diffusion MRI acquisition of patients with rectal cancer and reduce the scan time beyond the duration of a single-shot diffusion MRI acquisition.

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Keywords