Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
Motivation: Noise in 7T MRA images can make it challenging to diagnose vascular diseases, and conventional denoisers tend to over-smooth in a way that blur entire image or does not preserve vascular information.
Goal(s): Our goal was to reduce noise in 7T MRA images while maintaining vascular information using deep learning method.
Approach: We devised an unsupervised denoising model using multiple slice information and cycleGAN-based neural networks.
Results: Our approach not only suppressed noise in 7T MRA images, but it also successfully preserved vessel information among the compared models.
Impact: We developed a denoising method in 7T MRA images while preserving vascular information. Clinically, our findings will help diagnose vascular-related diseases. High SNR is preserved by averaging adjacent slices, and we contribute to increase usability of 7T MRI.
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