Specialized Coil Informed Deep Learning for High SNR Carotid Imaging

Motivation: Noise from standard commercial head-neck coils used for carotid MR hinders effective evaluation of high-risk plaque characteristics, while specialized carotid coils remain costly and are not widely available. Goal(s): We aim to match the signal-to-noise ratio and quality of images obtained from standard head-neck coils to those of dedicated carotid coils. Approach: A supervised deep learning method was developed to improve carotid MR image quality using data acquired with specialized carotid coil as the reference. Results: Significant improvements in SNR and image quality were observed for multi-contrast images and T1/ T2 maps. Impact: With the use of our DL model, high SNR images are achievable with standard head-neck coils, which may help radiologists to be more confident and efficient in evaluating carotid plaque characteristics.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/3858
Primary Topic
Cerebrovascular and Carotid Artery Diseases
Type
article
Field-Weighted Citation Impact
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article

Specialized Coil Informed Deep Learning for High SNR Carotid Imaging

Lisha Zeng, Scott McNally, Yibin Xie, H Douglas Morris et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Cerebrovascular and Carotid Artery Diseases
article

Specialized Coil Informed Deep Learning for High SNR Carotid Imaging

Lisha Zeng, Scott McNally, Yibin Xie, H Douglas Morris, David Saloner, Dennis L. Parker, J. J. Roberts, Kim‐Lien Nguyen, Lu Meng, Gerald S. Treiman, María I. Altbach, Vibhas Deshpande, Dimitrios Mitsouras, Seong Eun Kim, Kevin Johnson, Yin-Chen Hsu, Debiao Li, Rock Hadley, J. Kevin DeMarco, Mary Keushkerian
article en

Abstract

Motivation: Noise from standard commercial head-neck coils used for carotid MR hinders effective evaluation of high-risk plaque characteristics, while specialized carotid coils remain costly and are not widely available. Goal(s): We aim to match the signal-to-noise ratio and quality of images obtained from standard head-neck coils to those of dedicated carotid coils. Approach: A supervised deep learning method was developed to improve carotid MR image quality using data acquired with specialized carotid coil as the reference. Results: Significant improvements in SNR and image quality were observed for multi-contrast images and T1/ T2 maps. Impact: With the use of our DL model, high SNR images are achievable with standard head-neck coils, which may help radiologists to be more confident and efficient in evaluating carotid plaque characteristics.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 64%
Cerebrovascular and Carotid Artery Diseases
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