Color as a High-Value Quantitative Tool for PET/CT Imaging

The successful application of artificial intelligence (AI) techniques for the quantitative analysis of hybrid medical imaging data such as PET/CT is challenged by the differences in the type of information and image quality between the two modalities. The purpose of this work was to develop color-based, pre-processing methodologies for PET/CT data that could yield a better starting point for subsequent diagnosis and image processing and analysis. Two methods are proposed that are based on the encoding of Hounsfield Units (HU) and Standardized Uptake Values (SUVs) in separate transformed .png files as reversible color information in combination with .png basic information metadata based on DICOM attributes. Linux Ubuntu using Python was used for the implementation and pilot testing of the proposed methodologies on brain 18F-FDG PET/CT scans acquired with different PET/CT systems. The range of HUs and SUVs was mapped using novel weighted color distribution functions that allowed for a balanced representation of the data and an improved visualization of anatomic and metabolic differences. The pilot application of the proposed mapping codes yielded CT and PET images where it was easier to pinpoint variations in anatomy and metabolic activity and offered a potentially better starting point for the subsequent fully automated quantitative analysis of specific regions of interest or observer evaluation. It should be noted that the output .png files contained all the raw values and may be treated as raw DICOM input data.