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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Abstract
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 QIAL lab advances quantitative imaging by designing novel CT systems, reconstruction algorithms, image analysis and applications, with a core strength in preclinical CT.
- Current efforts center on spectral CT (dual-energy and photon-counting) with nanoparticle contrast agents for theranostics, multidimensional CT for challenging applications such as intracranial aneurysm, cardiac, and perfusion imaging, and modern reconstruction and image processing ( including deep learning).
- In parallel, we lead co-clinical cancer imaging work; I served as PI of the U24 Duke Preclinical Research Resources for Quantitative Imaging Biomarkers within the NCI Co-Clinical Imaging Research Program (CIRP).
- We are also building a virtual preclinical photon-counting CT platform for cancer studies to accelerate method development and translation.
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