Theranostic SPECT reconstruction for improved resolution: application to radionuclide therapy dosimetry

BackgroundSPECT-derived dose estimates in tissues of diameter less than 3x system resolution are subject to significant losses due to the limited spatial resolution of the gamma camera. Incorporating resolution modelling (RM) into the SPECT reconstruction has been proposed as a possible solution; however, the images produced are prone to noise amplification and Gibbs artefacts. We propose a novel approach to SPECT reconstruction in a theranostic setting, which we term SPECTRE (single photon emission computed theranostic reconstruction); using a diagnostic PET image, with its superior resolution, to guide the SPECT reconstruction of the therapeutic equivalent. This report demonstrates a proof in principle of this approach.MethodsWe have employed the hybrid kernelised expectation maximisation (HKEM) algorithm implemented in STIR, with the aim of producing SPECT images with PET-equivalent resolution. We demonstrate its application in both a dual Ga-68/Lu-177 IEC phantom study and a clinical example using Cu-64/Cu-67.ResultsSPECTRE is shown to produce images comparable in accuracy and recovery to PET with minimal introduction of artefacts and amplification of noise.ConclusionThe SPECTRE approach to image reconstruction shows improved quantitative accuracy with a reduction in noise amplification. SPECTRE shows great promise as a method of improving SPECT radioactivity concentrations, directly leading to more accurate dosimetry estimates in small structures and target lesions. Further investigation and optimisation of the algorithm parameters is needed before this reconstruction method can be utilised in a clinical setting.

Automated summary

The SPECTRE approach to image reconstruction in theranostic setting shows promise in improving accuracy of radioactivity concentrations and treatment assessment by utilizing PET images to guide higher-resolution SPECT reconstructions. Further investigation and optimization of the algorithm parameters are needed before clinical implementation.