Feasibility of a sub‐3‐minute imaging strategy for ungated quiescent interval slice‐selective MRA of the extracranial carotid arteries using radial k‐space sampling and deep learning–based image processing

作者：Ioannis Koktzoglou, Rong Hwa Huang, Archie L. Ong, Pascale J. Aouad, Emily Alanna Aherne, Robert R. Edelman · 发表于：Magnetic Resonance in Medicine · 年份：2020 · DOI：10.1002/mrm.28179 · 被引用次数：19 · 研究领域：Cerebrovascular and Carotid Artery Diseases、Advanced MRI Techniques and Applications、Cardiovascular Health and Disease Prevention

PURPOSE: To develop and test the feasibility of a sub-3-minute imaging strategy for non-contrast evaluation of the extracranial carotid arteries using ungated quiescent interval slice-selective (QISS) MRA, combining single-shot radial sampling with deep neural network-based image processing to optimize image quality. METHODS: The extracranial carotid arteries of 12 human subjects were imaged at 3 T using ungated QISS MRA. In 7 healthy volunteers, the effects of radial and Cartesian k-space sampling, single-shot and multishot image acquisition (1.1-3.3 seconds/slice, 141-423 seconds/volume), and deep learning-based image processing were evaluated using segmental image quality scoring, arterial temporal SNR, arterial-to-background contrast and apparent contrast-to-noise ratio, and structural similarity index. Comparison of deep learning-based image processing was made with block matching and 3D filtering denoising. RESULTS: Compared with Cartesian sampling, radial k-space sampling increased arterial temporal SNR 107% (P < .001) and improved image quality during 1-shot imaging (P < .05). The carotid arteries were depicted with similar image quality on the rapid 1-shot and much lengthier 3-shot radial QISS protocols (P = not significant), which was corroborated in patient studies. Deep learning-based image processing outperformed block matching and 3D filtering denoising in terms of structural similarity index (P < .001). Compared with original QISS source images, deep learning i...