Keywords: Vessels, Blood vessels, MR Angiogram, Super-resolution neural networkUTE-MRA is used to visualize the vasculature of the body. But the visibility of smaller blood vessels is highly dependent on the resolution which requires a long scanning time. We applied a customized 3D-SRGAN network on UTE-MRA data to increase the resolution and have achieved a structural similarity, peak signal-to-noise ratio, and mean squared error of 0.932, 32.001, and 0.00064 respectively compared with the ground truth high-resolution data. The proposed method performed better than 3D-SRGAN and cubic spline interpolation and can be used reduce the scanning time significantly and provide better image quality.
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