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

• Published in: Magnetic resonance in medicine Vol. 84; no. 2; pp. 825 - 837
• Main Authors: Koktzoglou, Ioannis, Huang, Rong, Ong, Archie L., Aouad, Pascale J., Aherne, Emily A., Edelman, Robert R.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.08.2020
• Subjects: Arteries; Artificial neural networks; Background noise; carotid; Carotid arteries; Carotid Arteries - diagnostic imaging; Carotid artery; Cartesian coordinates; Deep Learning; Feasibility; Feasibility Studies; Filtration; Humans; Image acquisition; Image contrast; Image enhancement; Image Interpretation, Computer-Assisted; Image processing; Image quality; Information processing; Machine learning; Magnetic Resonance Angiography; Matching; MRA; Neural networks; Noise reduction; Protocol (computers); QISS; Quality; radial; Sampling; Shot; Similarity; Veins & arteries; deep learning; radial; MRA; QISS; carotid
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.28179

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.