Statistical approach for detection and localization of a fluorescing mouse tumor in Intralipid.
We present a method for detecting and localizing a fluorescing tumor obscured underneath several millimeters of a multiply scattering, homogeneous medium from fluorescence measurements made above the surface. Using a statistical model of the measurement system, we develop approaches for detection by use of a binary hypothesis testing approach and localization by use of maximum-likelihood estimation. We also compute the probability of tumor detection and the Cramér-Rao lower bound for the localization estimate error, which are performance metrics that could potentially be optimized in an experimental design. We validate the methods in an experimental study involving an excised mouse tumor tagged with a new folate-indocyanine dye and obscured under a tissue-simulating lipid suspension.
Milstein
Adam B
AB
School of Electrical and Computer Engineering, Purdue University, 465 Northwestern Avenue, West Lafayette, Indiana 47907-2035, USA. milstein@ll.mit.edu
Kennedy
Michael D
MD
Low
Philip S
PS
Bouman
Charles A
CA
Webb
Kevin J
KJ
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D018929:Cell Culture Techniques / Q000379:methods; D045744:Cell Line, Tumor; D003198:Computer Simulation; D005217:Fat Emulsions, Intravenous; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007939:Leukemia L1210 / Q000473:pathology*; D016013:Likelihood Functions; D051379:Mice; D008819:Mice, Nude; D008856:Microscopy, Fluorescence / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D009367:Neoplasm Staging / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013050:Spectrometry, Fluorescence / Q000379:methods*
D005217:Fat Emulsions, Intravenous