Deep Learning‐Based Denoising for High‐Resolution Carotid Vessel Wall MRI Using Standard Neurovascular Coils
Year: 2025
Type: article
Abstract: 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.
Source: Magnetic Resonance in Medicine
DOI: 10.1002/mrm.70226
Authors: Lisha Zeng, Yin‐Chen Hsu, Lixia Wang, Meng Lu, Mary Keushkerian, Kim‐Lien Nguyen, Kevin J. Johnson, Maria I. Altbach, H. Douglas Morris, J. Kevin DeMarco, Vibhas Deshpande, Dimitrios Mitsouras, David Saloner, J. Scott McNally, Seong‐Eun Kim, John A. Roberts, J. Rock Hadley, Dennis L. Parker, Gerald S. Treiman, Debiao Li, Yibin Xie
Institutions: Cedars-Sinai Medical Center, University of California, Los Angeles, VA Greater Los Angeles Healthcare System, University of Arizona, Walter Reed National Military Medical Center, University of California, San Francisco, University of California San Francisco Medical Center, University of Utah, VA Salt Lake City Healthcare System
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4417535034