Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI.

Autor: Javadi M; Medical Robotics and Imaging Lab, Department of Computer Science, University of Houston, 501, Philip G. Hoffman Hall, 4800 Calhoun Road, Houston, TX, 77204, USA., Sharma R; Medical Robotics and Imaging Lab, Department of Computer Science, University of Houston, 501, Philip G. Hoffman Hall, 4800 Calhoun Road, Houston, TX, 77204, USA., Tsiamyrtzis P; Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.; Department of Statistics, Athens University of Economics and Business, Athens, Greece., Webb AG; C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands., Leiss E; Department of Computer Science, University of Houston, Houston, TX, USA., Tsekos NV; Medical Robotics and Imaging Lab, Department of Computer Science, University of Houston, 501, Philip G. Hoffman Hall, 4800 Calhoun Road, Houston, TX, 77204, USA. nvtsekos@central.uh.edu.
Jazyk: angličtina
Zdroj: Journal of imaging informatics in medicine [J Imaging Inform Med] 2024 Jul 31. Date of Electronic Publication: 2024 Jul 31.
DOI: 10.1007/s10278-024-01205-8
Abstrakt: Adversarial training has attracted much attention in enhancing the visual realism of images, but its efficacy in clinical imaging has not yet been explored. This work investigated adversarial training in a clinical context, by training 206 networks on the OASIS-1 dataset for improving low-resolution and low signal-to-noise ratio (SNR) magnetic resonance images. Each network corresponded to a different combination of perceptual and adversarial loss weights and distinct learning rate values. For each perceptual loss weighting, we identified its corresponding adversarial loss weighting that minimized structural disparity. Each optimally weighted adversarial loss yielded an average SSIM reduction of 1.5%. We further introduced a set of new metrics to assess other clinically relevant image features: Gradient Error (GE) to measure structural disparities; Sharpness to compute edge clarity; and Edge-Contrast Error (ECE) to quantify any distortion of the pixel distribution around edges. Including adversarial loss increased structural enhancement in visual inspection, which correlated with statistically consistent GE reductions (p-value << 0.05). This also resulted in increased Sharpness; however, the level of statistical significance was dependent on the perceptual loss weighting. Additionally, adversarial loss yielded ECE reductions for smaller perceptual loss weightings, while showing non-significant increases (p-value >> 0.05) when these weightings were higher, demonstrating that the increased Sharpness does not adversely distort the pixel distribution around the edges in the image. These studies clearly suggest that adversarial training significantly improves the performance of an MRI enhancement pipeline, and highlights the need for systematic studies of hyperparameter optimization and investigation of alternative image quality metrics.
(© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)
Databáze: MEDLINE
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