High-resolution reconstruction of fluorescent inclusions in mouse thorax using anatomically guided sampling and parallel Monte Carlo computing.
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2011-09-01
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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.
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Zhang, X, C Badea, G Hood, A Wetzel, Y Qi, J Stiles and GA Johnson (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 https://hdl.handle.net/10161/11254.
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Scholars@Duke
Cristian Tudorel Badea
- Our lab's research focus lies primarily in developing novel quantitative imaging systems, reconstruction algorithms and analysis methods. My major expertise is in preclinical CT.
- Currently, we are particularly interested in developing novel strategies for spectral CT imaging using nanoparticle-based contrast agents for theranostics (i.e. therapy and diagnostics).
- We are also engaged in developing new approaches for multidimensional CT image reconstruction suitable to address difficult undersampling cases in cardiac and spectral CT (dual energy and photon counting) using compressed sensing and/or deep learning.
- We are involved in co-clinical cancer trials and I serve as the Principal Investigator on the U24 Duke Preclinical Research Resources for Quantitative Imaging Biomarkers part of the NCI Co-Clinical Imaging Research Resources Program network (CIRP).
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