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.

Abstract

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. 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. 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 image processing provided 24% and 195% increases in arterial-to-background contrast (P < .001) and apparent contrast-to-noise ratio (P < .001), and provided source images that were preferred by radiologists (P < .001). Rapid, sub-3-minute evaluation of the extracranial carotid arteries is feasible with ungated single-shot radial QISS, and benefits from the use of deep learning-based image processing to enhance source image quality.