Although radiopharmaceutical therapy (RPT) has worked well in patients with lymphoma, late-stage, metastatic prostate cancer, and neuroendocrine tumors, it is well known that because of variability in patient pharmacokinetics, standard dosing leads to clinical outcomes that are difficult to predict and drastically vary. However, it is possible to make RPT safer and more effective by first measuring the radiation emitted by the RPT agent in vivo using quantitative SPECT imaging and then calculating the radiation energy deposited in tumors and normal tissues using dosimetry software. A personalized RPT prescription can be derived that maximizes effectiveness and minimizes side effects. However, because commercially available SPECT reconstruction algorithms do not accurately correct for scatter, they are insufficient for determining personalized RPT prescriptions. Therefore, there is a clinically unmet need for better reconstruction algorithms which enable true quantitative SPECT imaging. The aim of this contract proposal is to build a graphics processing unit (GPU) software platform that can perform SPECT reconstruction with Monte Carlo (MC)-based scatter correction within 5 minutes, making it clinically viable. Fast and accurate GPU MC-based SPECT reconstruction (Torch Recon) will further improve the accuracy of RPT dosimetry and thereby accelerate the clinical adoption of our current SBIR-funded MC dose engine (Torch).
