Emission Computed Tomography and Parallel Computing

DESCRIPTION (Provided by Applicant): The long-term objective of this grant is to develop a rigorous theoretical and experimental framework for objective assessment of image quality and to apply it to the development and optimization of reconstruction algorithms and imaging systems for single-photon emission computed tomography (SPECT). We shall upgrade significantly our computational capabilities and apply this new power to fundamental issues of relevance not only to SPECT but to the broader field of image science. A Beowuif cluster will be implemented for parallel image reconstruction, simulation of objects and images, computation of figures of merit for image quality and systematic, task-based optimization of imaging systems. New methods for experimental determination of object statistics from clinical images will be developed, with emphasis on statistical models related to wavelet filters, and parallel computational techniques will be devised for simulating realistic SPECT images consistent with these models. New methods of computing figures of merit for detection and classification tasks will be developed and validated by psychophysical studies. New reconstruction algorithms based on listmode data will be developed, along with parallel algorithms for real-time reconstruction of SPECT images during acquisition. Hardware configurations for data acquisition in SPECT will be evaluated and optimized with respect to task performance, and a comprehensive theory of artifacts in tomographic imaging will be developed.