Influence of absorption and scattering on the quantification of fluorescence diffuse optical tomography using normalized data

Abascal, Juan Felipe Pérez Juste; Aguirre, Juan; Chamorro Servent, Judit; Schweiger, Martin; Arridge, Simon; Ripoll, Jorge; Vaquero, Juan J.

doi:http://dx.doi.org/10.1117/1.JBO.17.3.036013

Influence of absorption and scattering on the quantification of fluorescence diffuse optical tomography using normalized data

Citation

Abascal JFPJ, Aguirre J, Chamorro Servent J, Schweiger M, Arridge S, Ripoll J, Vaquero JJ. Influence of absorption and scattering on the quantification of fluorescence diffuse optical tomography using normalized data. Journal of biomedical optics. Journal of Biomedical Optics. 2012;17(3):036013. DOI: 10.1117/1.JBO.17.3.036013

Abstract

Reconstruction algorithms for imaging fluorescence in near infrared ranges usually normalize fluorescence light with respect to excitation light. Using this approach, we investigated the influence of absorption and scattering heterogeneities on quantification accuracy when assuming a homogeneous model and explored possible reconstruction improvements by using a heterogeneous model. To do so, we created several computer- simulated phantoms: a homogeneous slab phantom (P1), slab phantoms including a region with a two- to six-fold increase in scattering (P2) and in absorption (P3), and an atlas-based mouse phantom that modeled different liver and lung scattering (P4). For P1, reconstruction with the wrong optical properties yielded quantification errors that increased almost linearly with the scattering coefficient while they were mostly negligible regarding the absorption coefficient. This observation agreed with the theoretical results. Taking the quantification of a homogeneous phantom as a reference, relative quantification errors obtained when wrongly assuming homogeneous media were in the range þ41 to þ94% (P2), 0.1 to −7% (P3), and −39 to þ44% (P4). Using a heterogeneous model, the overall error ranged from −7 to 7%. In conclusion, this work demonstrates that assuming homogeneous media leads to noticeable quantification errors that can be improved by adopting heterogeneous models.