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 Lee^2,3, 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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