Advanced planar image reconstruction for targeted alpha therapy

Project Summary Title: Advanced Planar Image Reconstruction for Targeted Alpha Therapy Targeted Alpha Therapy (TAT) based on the alpha-emitting radiopharmaceuticals (AER) has been recently successfully applied as a treatment for many advanced-stage cancers incurable with conventional methods. However, current methods of imaging the biodistribution of AER are sub-optimal, due to very low administered

activity. As a result, AER biodistributions are unknown and hence, absorbed dose distributions for AER are unknown. Thus, our ability to anticipate normal tissue toxicity and prescribe personalized treatment is compromised. Accurate quantitative imaging of the AER biodistribution is necessary to remedy this situation.

To address this need, our project’s long-term goal is development of a method for the generation of accurate projection images of AER (proj-AER) using low-count AER planar data and co-registered low-dose CT images acquired on SPECT/CT cameras. The obtained proj-AER combined with CT will permit accurate AER activity

estimation that will be used as input data for a dosimetry model, which will provide information on the radiation dose to organs/tumor. The objective of this proposal is to develop a novel sparsity promoting reconstruction method based on a physical model for planar AER imaging and perform a proof-of-concept study of its superior

quantitative accuracy vs. standard-of-care (SOC). We will use low-count 225Ac planar images and co-registered low-dose CT images acquired on SPECT/CT cameras. Physical and digital phantoms will be employed. Guided by strong preliminary data, this objective will be attained by pursuing two specific aims: 1) Develop and

validate an accurate projected planar activity distribution reconstruction method for AER; and 2) Compare performance of our method with current SOC in 225Ac quantitation tasks using simulated and physical-phantom planar/CT data acquired on SPECT/CT camera. The approach will rely on a physical imaging model containing

the system kernels and regularization. To control noise, we will use sparsity promoting regularization with envelope of the l0-norm. The fixed-point proximity-operator approach will be used to solve the resulting nonconvex minimization problem. The effects of noise on organ/tumor activity estimates will be accounted for

with an ensemble mean squared error performance metric. The estimation tradeoffs of bias and variance will be explored. Simulated dosimetry tasks will be used to demonstrate performance improvements. Outcomes: We will establish that the novel planar reconstruction method is ready for testing in the clinical environment.

The measure of success is defined as substantial (>50%) improvement in precision and accuracy in the estimation of regional activity concentration of AER. The proposed research is significant because it is expected that the direct quantitative imaging of AER will allow optimized personalized design of TAT including

activity/fractionation schedule. It will also enable rational assessment of the utility of imaging AER surrogates. Ultimately, such knowledge will lead to higher likelihood of response and cure for patients with advanced stages of many cancers.