Fluorescence imaging of tissues has gained significant attention in recent years due to the emergence of appropriate reporter technologies that enable noninvasive sensing of molecular function in vivo. Two major approaches have been used so far for fluorescence molecular imaging, i.e., epi-illumination (reflectance) imaging and fluorescence molecular tomography. Transillumination is an alternative approach that has been employed for imaging tissues in the past and could be similarly beneficial for fluorescence molecular imaging. We investigate data normalization schemes in reflectance and transillumination mode and experimentally demonstrate that normalized transillumination offers significant advantages over planar reflectance imaging and over nonnormalized methods. Our observations, based on phantoms and on postmortem and in vivo mouse measurements display image quality improvement, superior depth sensitivity, and improved imaging accuracy over the nonnormalized methods examined. Normalized planar imaging retains implementation simplicity and could be used to improve on standard fluorescence reflectance imaging and as a simplified alternative to the more integrated and accurate tomographic methods.

Using gene expression profiling, we identified cathepsin cysteine proteases as highly up-regulated genes in a mouse model of human lung adenocarcinoma. Overexpression of cathepsin proteases in these lung tumors was confirmed by immunohistochemistry and Western blotting. Therefore, an optical probe activated by cathepsin proteases was selected to detect murine lung tumors in vivo as small as 1 mm in diameter and spatially separated. We generated 3D maps of the fluorescence signal and fused them with anatomical computed tomography images to show a close correlation between fluorescence signal and tumor burden. By serially imaging the same mouse, optical imaging was used to follow tumor progression. This study demonstrates the capability for molecular imaging of a primary lung tumor by using endogenous proteases expressed by a tumor. It also highlights the feasibility of using gene expression profiling to identify molecular targets for imaging lung cancer.