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Intraoperative fluorescence perfusion assessment should be corrected by a measured subject-specific arterial input function

Significance: The effects of varying the indocyanine green injection dose, injection rate, physiologic dispersion of dye, and intravenous tubing volume propagate into the shape and magnitude of the arterial input function (AIF) during intraoperative fluorescence perfusion assessment, thereby alterin...

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Detalles Bibliográficos
Autores principales: Elliott, Jonathan T., Addante, Rocco R., Slobogean, Gerard P., Jiang, Shudong, Henderson, Eric R., Pogue, Brian W., Gitajn, Ida Leah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282620/
https://www.ncbi.nlm.nih.gov/pubmed/32519522
http://dx.doi.org/10.1117/1.JBO.25.6.066002
Descripción
Sumario:Significance: The effects of varying the indocyanine green injection dose, injection rate, physiologic dispersion of dye, and intravenous tubing volume propagate into the shape and magnitude of the arterial input function (AIF) during intraoperative fluorescence perfusion assessment, thereby altering the observed kinetics of the fluorescence images in vivo. Aim: Numerical simulations are used to demonstrate the effect of AIF on metrics derived from tissue concentration curves such as peak fluorescence, time-to-peak (TTP), and egress slope. Approach: Forward models of tissue concentration were produced by convolving simulated AIFs with the adiabatic approximation to the tissue homogeneity model using input parameters representing six different tissue examples (normal brain, glioma, normal skin, ischemic skin, normal bone, and osteonecrosis). Results: The results show that AIF perturbations result in variations in estimates of total intensity of up to 80% and TTP error of up to 200%, with the errors more dominant in brain, less in skin, and less in bone. Interestingly, error in ingress slope was as high as 60% across all tissue types. These are key observable parameters used in fluorescence imaging either implicitly by viewing the image or explicitly through intensity fitting algorithms. Correcting by deconvolving the image with a measured subject-specific AIF provides an intuitive means of visualizing the data while also removing the source of variance and allowing intra- and intersubject comparisons. Conclusions: These results suggest that intraoperative fluorescence perfusion assessment should be corrected by patient-specific AIFs measured by pulse dye densitometry.