Development of Novel Techniques for 2D and 3D reconstruction in fluorescence imaging
Lead Presenter: Georgios Mallas
Additional Presenters: Yair Mega, Esra Ataer-Cansizoglu, Vasilis Ntziachristos and Dana H. Brooks
Faculty Advisor/Principal Investigator: Dana H. Brooks
Method of Presentation: Poster
Development of Novel Techniques for 2D and 3D reconstruction in fluorescence imaging Georgios Mallas, Yair Mega, Esra Ataer-Cansizoglu, Vasilis Ntziachristos and Dana H. Brooks In collaboration with Farouc A. Jaffer, MGH, and Amir Rosenthal, Pouyan Mohajerani, Maxmillian Koch, Technical University of Munich, and Deniz Erdogmus, NU ECE Fluorescence imaging can reveal molecular information about imaged tissue through the detection of fluorescent agents designed to bind to molecules specific to a process or disease of interest. Here, we describe on-going work on methods for image reconstruction in intravascular near-infrared fluorescence (iNIRF) imaging and in fluorescence molecular tomography (FMT). iNIRF is used to detect plaque- or stent-induced inflammation in smaller coronary vessels, while FMT is used to image small animals with applications such as tumor detection or drug characterization. One significant advantage of iNIRF imaging is its capability to image through blood. However, fluorescence intensity detected through blood is dependent on the distance between the source and the detector, thus preventing quantitative imaging. Additionally, the scattering of photons propagated through blood leads to decreased imaging resolution. We present an algorithm that corrects for the fluorescence intensity dependence on distance and at the same time improves the imaging resolution, thus allowing for quantitative analysis of iNIRF images. Additionally, we present a novel multispectral approach to extract depth information in iNIRF. We also present systematic confirmation of our FMT reconstruction results based on a normalized Born ratio approach, where the ratio of fluorescence and excitation wavelength measurements is analyzed from a statistical perspective.