Estimating Signal Detectability in a Model Diffuse Optical Imaging System Stefano Young, Matthew A. Kupinski, Abhinav K. Jha University of Arizona College of Optical Sciences, 1630 E University Blvd Tucson AZ 85721 Author e-mail address: syoung@optics.arizona.edu Abstract: Diffuse optical imaging (DOI) researchers need metrics for quantifying signal detectability to assess different hardware configurations. Using Monte Carlo and statistical model observers, we estimated DOI signal detectability to compare source, signal, and detector parameters. © 2010 Optical Society of America OCIS codes: (110.3000) Image quality assessment; (170.3660) Light propagation in tissues 1. Introduction Diffuse optical imaging (DOI) remains limited in its clinical scope because the spatial resolution is low and the imaging problem is ill-posed. Researchers have suggested that it may not be necessary to solve the ill-posed and underdetermined imaging problem if one can address a specific clinical task [1]. By optimizing the DOI system parameters for a specific clinical task, the acquired image data carries the maximum possible task-specific information. Researchers need methods for quantifying the performance of DOI systems for a particular clinical task and optimizing the system hardware accordingly. Our group developed a model framework that employs Monte Carlo and statistical model observers to compute task-based figures of merit for DOI systems. This summary demonstrates our task-based approach and shows quantitative comparisons for different source, signal, and detection parameters. For a SKE/BKE signal detection task [2], we quantified our model system’s performance and its dependence on source diameter (for a uniform beam), number of sources (for individual acquisitions), number of input source angles, signal location within the tissue, detector size, and detector resolution. The trends for all system configurations matched with our physical intuitions. These findings suggest that we can quantify signal detectability in a model DOI system using a task-based approach with mathematical observers. To extend our model approach for use in optimizing clinical DOI systems, our approach should incorporate faster and more accurate models for the forward problem and noise sources. To generate more realistic image data in less time, we are developing a non- uniform RTE solver which may be substituted for Monte Carlo in our model framework. 2. Methods: Forward problem and figure of merit An imaging system can be described by the following general equation: (1) where is the data, is a nonlinear operator, is the object, and is the system noise (not necessarily additive) [1]. We modeled a 3D slab phantom ( ) with centered and non-centered embedded signals at different depths (Fig. 1) and generated image data ( ) using the open-source tMCimg monte carlo modeling software ( ) [3]. The source(s) occupied the -plane at the top surface of the slab phantom. We computed figures of merit from the raw projection data; we did not perform image reconstruction because reconstruction adds unwanted artifacts when our main purpose is system optimization. OSA / BIOMED/DH 2010 BSuD26.pdf