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European Radiology
Published in: European Radiology 2/2023

25-08-2022 | Angiography | Imaging Informatics and Artificial Intelligence

Super-resolution application of generative adversarial network on brain time-of-flight MR angiography: image quality and diagnostic utility evaluation

Authors: Krishna Pandu Wicaksono, Koji Fujimoto, Yasutaka Fushimi, Akihiko Sakata, Sachi Okuchi, Takuya Hinoda, Satoshi Nakajima, Yukihiro Yamao, Kazumichi Yoshida, Kanae Kawai Miyake, Hitomi Numamoto, Tsuneo Saga, Yuji Nakamoto

Published in: European Radiology | Issue 2/2023

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Abstract

Objectives

To develop a generative adversarial network (GAN) model to improve image resolution of brain time-of-flight MR angiography (TOF-MRA) and to evaluate the image quality and diagnostic utility of the reconstructed images.

Methods

We included 180 patients who underwent 1-min low-resolution (LR) and 4-min high-resolution (routine) brain TOF-MRA scans. We used 50 patients’ datasets for training, 12 for quantitative image quality evaluation, and the rest for diagnostic validation. We modified a pix2pix GAN to suit TOF-MRA datasets and fine-tuned GAN-related parameters, including loss functions. Maximum intensity projection images were generated and compared using multi-scale structural similarity (MS-SSIM) and information theoretic-based statistic similarity measure (ISSM) index. Two radiologists scored vessels’ visibilities using a 5-point Likert scale. Finally, we evaluated sensitivities and specificities of GAN-MRA in depicting aneurysms, stenoses, and occlusions.

Results

The optimal model was achieved with a lambda of 1e5 and L1 + MS-SSIM loss. Image quality metrics for GAN-MRA were higher than those for LR-MRA (MS-SSIM, 0.87 vs. 0.73; ISSM, 0.60 vs. 0.35; p.adjusted < 0.001). Vessels’ visibility of GAN-MRA was superior to LR-MRA (rater A, 4.18 vs. 2.53; rater B, 4.61 vs. 2.65; p.adjusted < 0.001). In depicting vascular abnormalities, GAN-MRA showed comparable sensitivities and specificities, with greater sensitivity for aneurysm detection by one rater (93% vs. 84%, p < 0.05).

Conclusions

An optimized GAN could significantly improve the image quality and vessel visibility of low-resolution brain TOF-MRA with equivalent sensitivity and specificity in detecting aneurysms, stenoses, and occlusions.

Key Points

• GAN could significantly improve the image quality and vessel visualization of low-resolution brain MR angiography (MRA).
• With optimally adjusted training parameters, the GAN model did not degrade diagnostic performance by generating substantial false positives or false negatives.
• GAN could be a promising approach for obtaining higher resolution TOF-MRA from images scanned in a fraction of time.
Appendix
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Metadata
Title
Super-resolution application of generative adversarial network on brain time-of-flight MR angiography: image quality and diagnostic utility evaluation
Authors
Krishna Pandu Wicaksono
Koji Fujimoto
Yasutaka Fushimi
Akihiko Sakata
Sachi Okuchi
Takuya Hinoda
Satoshi Nakajima
Yukihiro Yamao
Kazumichi Yoshida
Kanae Kawai Miyake
Hitomi Numamoto
Tsuneo Saga
Yuji Nakamoto
Publication date
25-08-2022
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 2/2023
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-022-09103-9

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