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Medical & Biological Engineering & Computing

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01.12.2017 | Original Article

Multimodal molecular 3D imaging for the tumoral volumetric distribution assessment of folate-based biosensors

verfasst von: Gerardo J. Ramírez-Nava, Clara L. Santos-Cuevas, Isaac Chairez, Liliana Aranda-Lara

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 7/2018

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Abstract

The aim of this study was to characterize the in vivo volumetric distribution of three folate-based biosensors by different imaging modalities (X-ray, fluorescence, Cerenkov luminescence, and radioisotopic imaging) through the development of a tridimensional image reconstruction algorithm. The preclinical and multimodal Xtreme imaging system, with a Multimodal Animal Rotation System (MARS), was used to acquire bidimensional images, which were processed to obtain the tridimensional reconstruction. Images of mice at different times (biosensor distribution) were simultaneously obtained from the four imaging modalities. The filtered back projection and inverse Radon transformation were used as main image-processing techniques. The algorithm developed in Matlab was able to calculate the volumetric profiles of ^99mTc-Folate-Bombesin (radioisotopic image), ¹⁷⁷Lu-Folate-Bombesin (Cerenkov image), and FolateRSense™ 680 (fluorescence image) in tumors and kidneys of mice, and no significant differences were detected in the volumetric quantifications among measurement techniques. The imaging tridimensional reconstruction algorithm can be easily extrapolated to different 2D acquisition-type images. This characteristic flexibility of the algorithm developed in this study is a remarkable advantage in comparison to similar reconstruction methods.

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Titel: Multimodal molecular 3D imaging for the tumoral volumetric distribution assessment of folate-based biosensors
verfasst von: Gerardo J. Ramírez-Nava
Clara L. Santos-Cuevas
Isaac Chairez
Liliana Aranda-Lara
Publikationsdatum: 01.12.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 7/2018
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-017-1755-2

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