Positron Emission Tomography

Research Summary

Our work in PET is focused on optimizing image quality through improved statistical and physical modeling of data acquisition and system response. We have addressed this problem using MAP (maximum a posteriori) estimation combining detector response modeling with accurate statistical modeling of random and scatter correction with rapidly convergent algorithms. Using spatially variant priors we are able to achieve count-independent image resolution that can be controlled by a single global smoothing parameter. By modeling the positron emissions from each voxel as a time inhomogeneous Poisson process, we extend the MAP approach to 4D (3 spatial + 1 temporal) reconstruction.

Our current emphasis is on extending the MAP approach to time of flight (TOF) PET data and exploring the trade-off between resolution/noise performance of rebinning fully 3D TOF data to either 2D or 3D non-TOF data prior to MAP reconstruction. We are collaborating in this effort with Frank DiFilippo at the Cleveland Clinic.

Our other current area of interest is in using dynamic data to better characterize tracer uptake in tumors. In collaboration with Quanzheng Li (MGH, and a former lab member) we are developing techniques for whole body Patlak imaging using novel methods for Patlak parameter estimation from partial data to enable parametric imaging from data using multiple bed positions.

Current Collaborators

Research Support

Molecular_Imaging (last edited 2019-06-03 22:00:03 by ?VijaySai)