The Computed Tomography perfusion (CTp) is a promising tool in oncology to characterize hemodynamics of tissues, based on fast and repeated CT scans of the region of interest after contrast agent administration. However, it has difficulty in entering the clinical routine (substantially, except for brain and heart) because of the difficulty of achieving same perfusion maps as equipment or perfusion software change. In this work, we present a proper computing chain for CTp parameters, relying on a robust and accurate voxel-based computation, that permits two widely used techniques, Maximum Slope (MS) and Deconvolution (DV) to reproduce, for the first time, the same perfusion maps. The experiments carried out on 25 examinations of oncologic patients proved an excellent correlation between MS and DV maps, with the worst R^2 = 0:971. This outcome represents a marked step forward in the standardization of CTp studies and encourages further multi-centre analyses.

Colormaps of CT perfusion parameters computed using different methods visually match

Alessandro Bevilacqua;Margherita Mottola
2019

Abstract

The Computed Tomography perfusion (CTp) is a promising tool in oncology to characterize hemodynamics of tissues, based on fast and repeated CT scans of the region of interest after contrast agent administration. However, it has difficulty in entering the clinical routine (substantially, except for brain and heart) because of the difficulty of achieving same perfusion maps as equipment or perfusion software change. In this work, we present a proper computing chain for CTp parameters, relying on a robust and accurate voxel-based computation, that permits two widely used techniques, Maximum Slope (MS) and Deconvolution (DV) to reproduce, for the first time, the same perfusion maps. The experiments carried out on 25 examinations of oncologic patients proved an excellent correlation between MS and DV maps, with the worst R^2 = 0:971. This outcome represents a marked step forward in the standardization of CTp studies and encourages further multi-centre analyses.
Proceedings of the 16th IEEE International Symposium on Biomedical Imaging (ISBI 2019)
1769
1773
Alessandro Bevilacqua; Margherita Mottola
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/652924
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