Abstract #2106

Acquisition of High-resolution Time Intensity Curves Using a Deep Learning Reconstruction for Dynamic Contrast Enhanced MRI

Hideaki Kutsuna¹, Hideki Ota^2,3, Yoshimori Kassai⁴, Hidenori Takeshima⁵, Tatsuo Nagasaka⁶, Takashi Nishina⁷, Yoshiaki Morita³, and Kei Takase^3,8

¹MRI Systems Development Department, Canon Medical Systems Corporation, Kanagawa, Japan, ²Department of Advanced MRI Collaboration Research, Tohoku University Graduate School of Medicine, Miyagi, Japan, ³Department of Diagnostic Radiology, Tohoku University Hospital, Miyagi, Japan, ⁴CT-MR Solution Planning Department, Canon Medical Systems Corporation, Tochigi, Japan, ⁵Advanced Technology Research Department, Research and Development Center, Canon Medical Systems Corporation, Kanagawa, Japan, ⁶Department of Radiological Technology, Tohoku University Hospital, Miyagi, Japan, ⁷MRI Sales Department, Canon Medical Systems Corporation, Miyagi, Japan, ⁸Department of Diagnostic Radiology, Tohoku University Graduate School of Medicine, Miyagi, Japan

The purpose is to provide improved time intensity curves (TICs) of dynamic contrast enhanced MRI. In this work, a method based on convolutional neural network (CNN) was compared with a conventional method based on compressed sensing (CS). While both of the methods used radial sampling for free-breathing acquisitions, reconstruction strategies were different.

The experimental results showed that, in comparison with the images reconstructed with CS, the images reconstructed with CNN exhibited higher temporal resolution in the TICs without losing spatial detail.

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