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Abstract #4659

Parallel Imaging in Time-of-Flight Magnetic Resonance Angiography Using Deep Multi-Stream Convolutional Neural Networks

Yohan Jun1, Taejoon Eo1, Hyungseob Shin1, Taeseong Kim1, Hojoon Lee2,3, and Dosik Hwang1

1Electrical Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea, Republic of, 3Department of Radiology, Inje University College of Medicine, Busan, Korea, Republic of

A deep parallel imaging network (“DPI-net”) was developed to reconstruct 3D multi-channel MRA from undersampled data. It comprises two deep-learning networks: a network of multi-stream CNNs for extracting feature maps of multi-channel images and a network of reconstruction CNNs for reconstructing images from the multi-stream network output feature maps. DPI-net was effective in reconstructing 3D time-of-flight MRA from highly undersampled multi-channel MR data, achieving superior performance, both quantitatively and qualitatively, over conventional parallel imaging and other deep-learning methods.

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