Highly efficient detection in fluorescence tomography of quantum dots using time-gated acquisition and ultrafast pulsed laser.

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Quantum dots (QDs) are widely used in fluorescence tomography due to its unique advantages. Despite the very high quantum efficiency of the QDs, low fluorescent signal and autofluorescence are the most fundamental limitations in optical data acquisition. These limitations are particularly detrimental to image reconstruction for animal imaging, e.g., free-space in vivo fluorescence tomography. In animals studies, fluorescent emission from exogenous fluorescent probes (e.g. QDs) cannot be effectively differentiated from endogenous broad-spectral substances (mostly proteins) using optical filters. In addition, a barrow-band fluorescent filter blocks the majority of the fluorescent light and thus makes signal acquisition very inefficient. We made use of the long fluorescent lifetime of the QDs to reject the optical signal due to the excitation light pulse, and therefore eliminated the need for a fluorescent filter during acquisition. Fluorescent emission from the QDs was excited with an ultrafast pulsed laser, and was detected using a time-gated image intensifier. A tissue-simulating imaging phantom was used to validate the proposed method. Compared to the standard acquisition method that uses a narrow-band fluorescent filter, the proposed method is significantly more efficient in data acquisition (by a factor of >10 in terms of fluorescent signal intensity) and demonstrated reduction in autofluorescence. No additional imaging artifact was observed in the tomographic reconstruction.






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Zhang, X, and CT Badea (2011). Highly efficient detection in fluorescence tomography of quantum dots using time-gated acquisition and ultrafast pulsed laser. Proc SPIE Int Soc Opt Eng, 7896. 10.1117/12.875502 Retrieved from https://hdl.handle.net/10161/13278.

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Cristian Tudorel Badea

Professor in Radiology

  • 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.

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