Deep Learning-Based Denoising for High-Resolution Carotid Vessel Wall MRI Using Standard Neurovascular Coils

Purpose: To develop a deep learning (DL) denoising method to enhance high-resolution carotid vessel wall MRI quality acquired using a standard head-and-neck clinical coil. Methods: Fifty-five scans were performed as part of an ongoing multicenter study. Routine carotid VWI protocol including 2D T1- and T2-weighted TSE, 3D TOF-MRA, and MPRAGE was performed using simultaneous acquisition from a standard 20-channel head-and-neck coil and a high-sensitivity Neck-Shape-Specific (NSS) surface coil. Paired retrospective reconstructions with and without NSS coil elements served as the reference and input, respectively. A supervised DL model employing a residual UNet architecture was optimized and trained to map low-SNR inputs to high-SNR references, benchmarked against conventional denoising algorithms using quantitative and qualitative metrics. Results: The DL denoiser substantially reduced noise while preserving vessel-wall structures across contrast-weighted sequences. It achieved PSNR > 31 dB and structural similarity index (SSIM) > 0.93 versus reference slices. In segmented vessel-wall and lumen regions of interest (ROIs), the DL approach achieved significantly higher SNR and CNR values than input images (p < 0.05), closely approaching the reference. Furthermore, inner-wall edge sharpness was maintained (Average ERD 7.50–8.51 mm with DL vs. 7.15–8.28 mm with references), supporting confident downstream plaques assessment. Radiologists' Likert ratings corroborated these image-quality improvements. Conclusion: A DL-based method was developed to improve high-resolution, multi-contrast carotid vessel wall MRI acquired using low-SNR standard head-and-neck coils. The resulting image quality was comparable to that obtained with specialized neck surface coils, potentially enabling broader access to advanced carotid imaging without the need for additional hardware.