Special Section on Pioneers in Biomedical Optics: Prof. Tayyaba Hasan

Comparing implementations of magnetic-resonance-guided fluorescence molecular tomography for diagnostic classification of brain tumors

[+] Author Affiliations
Scott C. Davis, Kimberley S. Samkoe, Julia A. O’Hara

Dartmouth College, Thayer School of Engineering, HB 8000, Hanover, New Hampshire 03755

Summer L. Gibbs-Strauss

Dartmouth College, Thayer School of Engineering, HB 8000, Hanover, New Hampshire 03755 and Beth Israel Deaconess Medical Center, Division of Hematology/Oncology, 330 Brookline Avenue, Boston, Massachusetts 02215

Keith D. Paulsen

Dartmouth College, Thayer School of Engineering, HB 8000, Hanover, New Hampshire 03755 and Dartmouth Medical School, Department of Diagnostic Radiology, Lebanon, New Hampshire 03756

Brian W. Pogue

Dartmouth College, Thayer School of Engineering, HB 8000, Hanover, New Hampshire 03755 and Dartmouth Medical School, Department of Surgery, Lebanon, New Hampshire 03756

J. Biomed. Opt. 15(5), 051602 (September 15, 2010). doi:10.1117/1.3483902
History: Received January 14, 2010; Revised May 10, 2010; Accepted May 17, 2010; Published September 15, 2010; Online September 15, 2010
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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.

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© 2010 Society of Photo-Optical Instrumentation Engineers

Citation

Scott C. Davis ; Kimberley S. Samkoe ; Julia A. O’Hara ; Summer L. Gibbs-Strauss ; Keith D. Paulsen, et al.
"Comparing implementations of magnetic-resonance-guided fluorescence molecular tomography for diagnostic classification of brain tumors", J. Biomed. Opt. 15(5), 051602 (September 15, 2010). ; http://dx.doi.org/10.1117/1.3483902


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