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. 2020 Apr 14;295(3):552–561. doi: 10.1148/radiol.2020192173

Figure 3:

Box-and-whisker plots compare performance based on the structural similarity index (SSIM) for each super-resolution method across multiple upsampling factors. Boxes encapsulate interquartile ranges, whiskers demarcate the central 95% of data points, and black bars lie on the median (n = 9907 short-axis slices from testing set). A, Aggregate performance for each super-resolution method. B, Pairwise comparison of performance between each method and zero padding (z-pad). Deep learning–based methods consistently outperformed conventional methods on bulk and per-slice bases. Neural network–based methods outperformed traditional bicubic and zero padding for nearly every slice evaluated. Zero padding outperformed the bicubic method for nearly every slice evaluated. k = single frame, kt = multiframe.

Box-and-whisker plots compare performance based on the structural similarity index (SSIM) for each super-resolution method across multiple upsampling factors. Boxes encapsulate interquartile ranges, whiskers demarcate the central 95% of data points, and black bars lie on the median (n = 9907 short-axis slices from testing set). A, Aggregate performance for each super-resolution method. B, Pairwise comparison of performance between each method and zero padding (z-pad). Deep learning–based methods consistently outperformed conventional methods on bulk and per-slice bases. Neural network–based methods outperformed traditional bicubic and zero padding for nearly every slice evaluated. Zero padding outperformed the bicubic method for nearly every slice evaluated. k = single frame, kt = multiframe.