Comparative assessment of commonly used color lookup tables to determine key performance indicators for perfusion map data visualization - Alkhimova, Svitlana; Sorokina, Viktoriia; Kabala, Illia (Technology audit and production reserves, 2026)
The object of this research is color lookup table schemes that are most commonly used to visualize perfusion maps in the scope of assessment of brain hemodynamic parameters. The problem is that such color schemes differ significantly in the number of colors, their distribution, and the rules for converting grayscale image data into color. As a result, the same perfusion map may appear different depending on the selected scheme, which complicates the visual assessment of hemodynamic parameters and significantly biases the precision of their interpretation. The research provides a comprehensive analysis of the ten commonly used color lookup table schemes for perfusion map visualization. Assessment of both direct schemes and patient-derived data is provided. Among quantitative metrics are RMSE, PSNR, SSIM, FSIM, ISSM, SRE, SAM, and UIQ. The CIELAB color space is used to provide a perceptual assessment of the color impact across neighboring levels in the schemes. It also used to analyze the relationship between local intensity differences in greyscale perfusion maps and resulting color perceptual differences once the lookup table is applied. Analysis reveals that the selection of color lookup table schemes is critical for preserving signal intensity and structural integrity. Spectral rainbow and block-structured schemes lag behind others in performance, making them less effective due to distorted structural features. The results can be applied in practice to visualize perfusion map data in medical software to assess key hemodynamic parameters, such as blood volume, blood flow, and mean transit time. Also, the results can be helpful for standardization and selecting optimal color lookup table schemes in clinical practice, and for validating algorithms used to calculate perfusion maps during medical software development.