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Quantitative predictions in small-animal X-ray fluorescence tomography
KTH, School of Engineering Sciences (SCI), Applied Physics, Biomedical and X-ray Physics.
KTH, School of Engineering Sciences (SCI), Applied Physics, Biomedical and X-ray Physics.ORCID iD: 0000-0002-9637-970X
KTH, School of Engineering Sciences (SCI), Applied Physics, Biomedical and X-ray Physics.ORCID iD: 0000-0003-2723-6622
2019 (English)In: Biomedical Optics Express, ISSN 2156-7085, E-ISSN 2156-7085, Vol. 10, no 8, p. 3773-3788, article id 364926Article in journal (Refereed) Published
Abstract [en]

X-ray fluorescence (XRF) tomography from nanoparticles (NPs) shows promise for high-spatial-resolution molecular imaging in small-animals. Quantitative reconstruction algorithms aim to reconstruct the true distribution of NPs inside the small-animal, but so far there has been no feasible way to predict signal levels or evaluate the accuracy of reconstructions in realistic scenarios. Here we present a GPU-based computational model for small-animal XRF tomography. The unique combination of a highly accelerated Monte Carlo tool combined with an accurate small-animal phantom allows unprecedented realistic full-body simulations. We use this model to simulate our experimental system to evaluate the quantitative performance and accuracy of our reconstruction algorithms on large-scale organs as well as mm-sized tumors. Furthermore, we predict the detection limits for sub-mm tumors at realistic NP concentrations. The computational model will be a valuable tool for optimizing next-generation experimental arrangements and reconstruction algorithms.

Place, publisher, year, edition, pages
Optical Society of America, 2019. Vol. 10, no 8, p. 3773-3788, article id 364926
National Category
Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:kth:diva-262546DOI: 10.1364/BOE.10.003773ISI: 000478097400005PubMedID: 31452974Scopus ID: 2-s2.0-85070837935OAI: oai:DiVA.org:kth-262546DiVA, id: diva2:1362769
Note

QC 20191021

Available from: 2019-10-21 Created: 2019-10-21 Last updated: 2019-10-21Bibliographically approved

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Shaker, KianLarsson, Jakob C.

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