Abstract #4190

CNN-based three-dimensional superresolution technology in Brain MRI with generalized q-sampling imaging

Chun-Yuan Shin¹, Yi-Ping Chao², Li-Wei Kuo^3,4, Yi-Peng Eve Chang⁵, and Jun-Cheng Weng^1,6,7

¹Department of Medical Imaging and Radiological Sciences, and Department of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan, ²Department of Computer Science and Information Engineering, and Graduate Institute of Artificial Intelligence, Chang Gung University, Taoyuan, Taiwan, ³Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan, ⁴Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan, ⁵Department of Counseling and Clinical Psychology, Columbia University, New York City, NY, United States, ⁶Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan, ⁷Department of Psychiatry, Chang Gung Memorial Hospital, Chiayi, Taiwan

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Diffusion/other diffusion imaging techniquesUnderstanding neural connections helps scientists conduct cognitive behavioral research. There are many nerve fiber intersections in the brain that need to be observed, and the size is between 30-50 nanometers. Improving image resolution has become an important issue. Generalized q-sampling imaging (GQI) was used to reveal the fiber geometry of straight and crossing. However, it is difficult to accurately describe fiber bending, fanning, and diverging with low-resolution imaging. In this work, we tried to achieve superresolution with a deep learning method on diffusion magnetic resonance imaging (MRI) images that has the potential to assess crossing, curving, and splaying fiber structures.

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