We explore the feasibility of using combined fluorescence and diffuse reflectance spectroscopy to characterize a myocardial infarct at different developing stages. An animal study is conducted using rats with surgically induced myocaridal infarction (MI). In vivo fluorescence spectra at 337-nm excitation and diffuse reflectance between 400 and 900 nm are measured from the heart. Spectral acquisition is performed: 1. for normal heart tissue; 2. for the area immediately surrounding the infarct; and 3. for the infarcted tissue itself, one, two, three, and four weeks into MI development. Histological and statistical analyses are used to identify unique pathohistological features and spectral alterations associated with the investigated regions. The main alterations (p<0.05) in diffuse reflectance spectra are identified primarily between 450 and 600 nm. The dominant fluorescence alterations are increases in peak fluorescence intensity at 400 and 460 nm. The extent of these spectral alterations is related to the duration of the infarction. The findings of this study support the concept that optical spectroscopy could be useful as a tool to noninvasively determine the in vivo pathophysiological features of a myocardial infarct and its surrounding tissue, thereby providing real-time feedback to surgeons during various surgical interventions for MI.
Early detection of malignant melanoma is critical to improve the survival rates of patients with this aggressive malignancy. We constructed an imaging system employing two liquid-crystal tunable filters to acquire in vivo spectral images of dysplastic lesions from patients at 31 wavelengths from 500 to 950nm. These reflectance images were analyzed in search of optical signatures for quantitative characterization of dysplastic nevi and malignant melanoma. A principal component analysis (PCA) algorithm was developed to examine the spectral imaging data in the component space and an index of spreading of clustering pixels (SCP) was defined to measure the degree of clustering in the distribution of image pixel scores in a component space. We found that SCP of differential polarimetric images correlate strongly with the degree of dysplasia for 4 lesions. However, many questions remain unanswered on the relations between PCA results and the spatial and spectral characteristics of the image data because of limited spectral image data from the patients. To fully improve our understanding on the multivariate analysis of spectral imaging data, we have developed a parallel Monte Carlo code to efficiently generate reflectance images from given distribution of optical parameters in a skin lesion phantom. With this tool, we have investigated numerically the dependence of score distribution and SCP in the component sub-spaces on lesion size and position. These numerical results provide a foundation for our future study to identify optical signature of dysplastic lesion and melanoma in the skin.