High-resolution reconstruction of fluorescent inclusions in mouse thorax using anatomically guided sampling and parallel Monte Carlo computing.
Repository Usage Stats
We present a method for high-resolution reconstruction of fluorescent images of the mouse thorax. It features an anatomically guided sampling method to retrospectively eliminate problematic data and a parallel Monte Carlo software package to compute the Jacobian matrix for the inverse problem. The proposed method was capable of resolving microliter-sized femtomole amount of quantum dot inclusions closely located in the middle of the mouse thorax. The reconstruction was verified against co-registered micro-CT data. Using the proposed method, the new system achieved significantly higher resolution and sensitivity compared to our previous system consisting of the same hardware. This method can be applied to any system utilizing similar imaging principles to improve imaging performance.
Subject(110.0113) Imaging through turbid media
(170.3010) Image reconstruction techniques
(170.3880) Medical and biological imaging
Published Version (Please cite this version)10.1364/BOE.2.002449
Publication InfoBadea, Cristian Tudorel; Hood, G; Johnson, G Allan; Qi, Y; Stiles, J; Wetzel, A; & Zhang, X (2011). High-resolution reconstruction of fluorescent inclusions in mouse thorax using anatomically guided sampling and parallel Monte Carlo computing. Biomed Opt Express, 2(9). pp. 2449-2460. 10.1364/BOE.2.002449. Retrieved from http://hdl.handle.net/10161/11254.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
More InfoShow full item record
Professor in Radiology
Dr. Cristian T. Badea is a Professor in the Department of Radiology and faculty in the Departments of Biomedical Engineering and Medical Physics. His research focuses on pre-clinical imaging. Dr. Badea has research interests in the physics and biomedical applications of computed tomography (CT), micro-CT, tomosynthesis, and image reconstruction algorithms.