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

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 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). Conclusion: 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.