GPU-based SPECT Reconstruction Using Reverse Monte Carlo Simulations

Project Summary/Abstract
Interest in applications of radiopharmaceutical conjugates has notably increased in the last few years for the
treatment of a variety of cancers. These conjugates are composed of chelators to target cancer cells and
radionuclides to employ the cytotoxicity of ionizing radiation. Radiation dosimetry is required to determine the
dosages, efficacy, and safety of these treatments, and 3D quantitative imaging of the biodistribution of activity
represents the best tool to perform dosimetry. For most radionuclides employed (non-positron-emitters), SPECT
imaging is needed for patient-specific dosimetry. However, multiple physical factors affect SPECT image quality,
such as attenuation, scattering, or the response collimator-detector system in SPECT scans. To account for
them, Monte Carlo techniques can be used due to their remarkable accuracy in representing physical processes
relevant to the transport of ionizing radiation. In particular, 3D SPECT reconstruction from the acquired
bidimensional projections may be obtained by transporting backward the photons detected in the gamma camera
projections, although many photons to be reversely transported require specially optimized architecture and
simulations. This project will develop a new reverse Monte Carlo software for SPECT reconstruction, built from
scratch in the GPU to speed up simulations. First, only the relevant reverse physical processes will be selected
and modeled using inverse processes of the well-characterized TOPAS Monte Carlo code for radiation transport.
Then, specific properties of collimator-detector systems will be integrated into our code to determine the angular
distributions for the photons detected. Finally, these developments will be integrated into a GPU-based platform
and shared with the Informatics Technology for Cancer Research program at NCI for further results of specific
commercial SPECT scans from the research community.

Grant Number: 1R21CA279068-01A1
NIH Institute/Center: NIH
Principal Investigator: Alejandro Bertolet Reina