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

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.