Dose volume histogram-based optimization of image reconstruction parameters for quantitative Y-PET imaging.

Purpose: Y-microsphere radioembolization or selective internal radiation therapy is increasingly being used as a treatment option for tumors that are not candidates for surgery and external beam radiation therapy. Recently, volumetric Y-dosimetry techniques have been implemented to explore tumor dose-response on the basis of 3D Y-activity distribution from PET imaging. Despite being a theranostic study, the optimization of quantitative Y-PET image reconstruction still uses the mean activity concentration recovery coefficient (RC) as the objective function, which is more relevant to diagnostic and detection tasks than is to dosimetry. The aim of this study was to optimize Y-PET image reconstruction by minimizing errors in volumetric dosimetry via the dose volume histogram (DVH). We propose a joint optimization of the number of equivalent iterations (the product of the iterations and subsets) and the postreconstruction filtration (FWHM) to improve the accuracy of voxel-level Y dosimetry.

Methods: A modified NEMA IEC phantom was used to emulate clinically relevant Y-PET imaging conditions through various combinations of acquisition durations, activity concentrations, sphere-to-background ratios, and sphere diameters. PET data were acquired in list mode for 300 min in a single-bed position; we then rebinned the list mode PET data to 60, 45, 30, 15, and 5 min per bed, with 10 different realizations. Errors in the DVH were calculated as root mean square errors (RMSE) of the differences in the image-based DVH and the expected DVH. The new optimization approach was tested in a phantom study, and the results were compared with the more commonly used objective function of the mean activity concentration RC.

Results: In a wide range of clinically relevant imaging conditions, using 36 equivalent iterations with a 5.2-mm filtration resulted in decreased systematic errors in volumetric Y dosimetry, quantified as image-based DVH, in Y-PET images reconstructed using the ordered subset expectation maximization (OSEM) iterative reconstruction algorithm with time of flight (TOF) and point spread function (PSF) modeling. Our proposed objective function of minimizing errors in DVH, which allows for joint optimization of Y-PET iterations and filtration for volumetric quantification of the Y dose, was shown to be superior to conventional RC-based optimization approaches for image-based absorbed dose quantification.

Conclusion: Our proposed objective function of minimizing errors in DVH, which allows for joint optimization of iterations and filtration to reduce errors in the PET-based volumetric quantification Y dose, is relevant to dosimetry in therapy procedures. The proposed optimization method using DVH as the objective function could be applied to any imaging modality used to assess voxel-level quantitative information.