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Understanding 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.
Shin et al. (Wed,) studied this question.