ArticleResearch in diagnostic and interventional imaging2022
MADplots: A methodology for visualizing and characterizing energy-dependent attenuation of tissues in spectral computed tomography.
Article in Research in diagnostic and interventional imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Rationale and objectives: A method for visualizing and analyzing the complete information contained in spectral CT scans using two-dimensional histograms (i.e. Material Attenuation Decomposition plots - MADplots) of the water-photoelectric attenuation versus water-scatter attenuation at the cohort (combination of multiple studies across patients), examination, series, slice, and organ/ROI levels is described. Materials and methods: The appearance of a MADplot with several standard biological materials was predicted using ideal material properties available from NIST and the ICRU to generate a map for this non-spatial data space. Software tools were developed to generate MADplots as new DICOM series that facilitate spectral analysis. Illustrative examples were selected from an IRB-approved, retrospective cohort of Spectral Basis Images (SBIs) scanned using a pre-release, dual-layer detector spectral CT. Results: By combining all of the voxels for contrast and non-contrast studies, the predicted appearance of the MADplot was confirmed. Locations of several kinds of tissue, the shape of the tissue distributions in normal lung, and the variations in the manner in which organ-specific MADplots change with pathology are demonstrated for the presence of fat in both the liver and pancreas highlighting the potential use for identifying pathologies on spectral CT images. Conclusions: The examples of MADplots shown at cohort (combined studies), examination, series, slice, organ, and ROI levels illustrate their potential utility in analyzing and displaying spectral CT data. Future studies are directed at developing MADplot based organ segmentation and the automated detection and display of organ based pathologies.
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