Multi-Slice-Aware Denoising Model for 7T MR Angiography

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.