Journal of Biomedical Optics
Thayer School of Engineering
Fluorescence molecular tomography (FMT) systems coupled to conventional imaging modalities such as magnetic resonance imaging (MRI) and computed tomography provide unique opportunities to combine data sets and improve image quality and content. Yet, the ideal approach to combine these complementary data is still not obvious. This preclinical study compares several methods for incorporating MRI spatial prior information into FMT imaging algorithms in the context of in vivo tissue diagnosis. Populations of mice inoculated with brain tumors that expressed either high or low levels of epidermal growth factor receptor (EGFR) were imaged using an EGF-bound near-infrared dye and a spectrometer-based MRI-FMT scanner. All data were spectrally unmixed to extract the dye fluorescence from the tissue autofluorescence. Methods to combine the two data sets were compared using student's t-tests and receiver operating characteristic analysis. Bulk fluorescence measurements that made up the optical imaging data set were also considered in the comparison. While most techniques were able to distinguish EGFR(+) tumors from EGFR(-) tumors and control animals, with area-under-the-curve values=1, only a handful were able to distinguish EGFR(-) tumors from controls. Bulk fluorescence spectroscopy techniques performed as well as most imaging techniques, suggesting that complex imaging algorithms may be unnecessary to diagnose EGFR status in these tissue volumes.
Davis SC, Samkoe KS, O'Hara JA, Gibbs-Strauss SL, Paulsen KD, Pogue BW. Comparing implementations of magnetic-resonance-guided fluorescence molecular tomography for diagnostic classification of brain tumors. J Biomed Opt. 2010 Sep-Oct;15(5):051602. doi: 10.1117/1.3483902. PMID: 21054076; PMCID: PMC2951993.
Dartmouth Digital Commons Citation
Davis, Scott C.; Samkoe, Kimberley S.; O’Hara, Julia A.; Gibbs-Strauss, Summer L.; Paulsen, Keith D.; and Pogue, Brian W., "Comparing Implementations of Magnetic-Resonance-Guided Fluorescence Molecular Tomography for Diagnostic Classification of Brain Tumors" (2010). Dartmouth Scholarship. 3716.