Supervised deep learning method improves image quality and SNR in carotid imaging using standard coils, suggesting better evaluation of plaque characteristics.
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.
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Zeng et al. (2025) studied this question.
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