Premium
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
Author(s) -
Koktzoglou Ioannis,
Huang Rong,
Ong Archie L.,
Aouad Pascale J.,
Aherne Emily A.,
Edelman Robert R.
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28179
Subject(s) - image quality , medicine , nuclear medicine , image processing , artificial intelligence , biomedical engineering , computer science , image (mathematics)
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.