Abstract #3300

High Resolution Intracranial MR Angiography at 3T and 7T using a Deep Learning based Image Reconstruction

Naoyuki Takei¹, Baolian Yang², Brian Burns³, Yoichiro Ikushima¹, R Marc Lebel⁴, Vince Magnotta⁵, Fumiyasu Tsushima⁶, Shingo Kakeda⁶, Atsushi Nozaki¹, and Tetsuya Wakayama¹

¹GE Healthcare, Tokyo, Japan, ²GE Healthcare, Waukesha, WI, United States, ³GE Healthcare, Menlo Park, CA, United States, ⁴GE Healthcare, Calagary, AB, Canada, ⁵University of Iowa, Iowa City, IA, United States, ⁶Hirosaki University Graduate School of Medicine, Aomori, Japan

Synopsis

Keywords: Blood vessels, Machine Learning/Artificial Intelligence, 7T MRI

Ultra-high-field MRA shows the promise to improve visualize the microvasculature and become an important investigation tool for research. However, increasing the consistency in image quality and reliability of small vessel visualization is necessary to demonstrate useful MR applications in clinical practice. We developed a deep learning-based reconstruction method to provide reduced noise and enhanced spatial resolution. The technique was evaluated by applying it to 3DTOF MRA data at 3T and 7T, demonstrating improved small vessel depiction.

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