Michal Meiri

AI
Operational applications drive AI adoption in radiology
When talking about artificial intelligence (AI) in radiology, one immediately thinks of the classic clinical use case of computer vision and powerful algorithms identifying abnormalities on imaging studies. Operational applications of AI, however, will likely be the first to be widely adopted.
March 16, 2020
2018 08 28 18 59 9066 Artificial Intelligence Ai 400
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Figure: Improved image quality with a generative adversarial network architecture for low- to high-field-strength image translation, called LowGAN. (A–C) Top: High-field-strength (3-T) and low-field-strength (64-mT) images and LowGAN outputs for a single participant across (A) fluid-attenuated inversion recovery (FLAIR), (B) T1-weighted (T1w), and (C) T2-weighted (T2w) contrasts. Bottom: Graphs demonstrate the structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), and feature similarity index (FSIM) between high field strength and low field strength (64-mT) and high field strength and LowGAN outputs. a.u. = arbitrary units.****p ≤ 0.0001. Images and caption courtesy of Radiology and the RSNA.
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