Recent & Upcoming Talks

Why & How Seminar Series, recorded 12 March 2020 This invited talk was part of a series of seminars organized by the Athinoula A. Martinos Center for Biomedical Imaging, and curated by Valeria Barletta, Avery Berman, Aina Frau-Pascual, and Mainak Jas

Abstract Dynamic positron emission tomography (dPET) is known for its ability to extract spatiotemporal information of a radio tracer in living tissue. In this paper, a novel direct reconstruction framework is presented, which include concurrent clustering as a potential aid in addressing high levels of noise typical of voxel-wise kinetic modeling.

Introduction Parametric images provide insight into the spatial distribution of physiological parameters, but they are often extremely noisy, due to low SNR of tomographic data. Direct estimation from projections allows accurate noise modeling, improving the results of post-reconstruction fitting.

Introduction Parametric images provide insight into the spatial distribution of physiological parameters, but they are often extremely noisy, due to low SNR of tomographic data. Direct estimation from projections allows accurate noise modeling, improving the results of post-reconstruction fitting.

Abstract In the present work a study is carried out in order to assess the efficiency of the direct reconstruction algorithms on noisy dynamic PET data. The study is performed via Monte Carlo simulations of a uniform cylindrical phantom whose emission values change in time according to a kinetic law.

Introduction Dynamic positron emission tomography (PET) studies allow to quantify tissue-specific biochemical properties. Conventional pharmacokinetic analysis requires the voxel-wise time activity curve fitting performed on a sequence of independently reconstructed PET images.