Impact Of Image Reconstruction Method On Dose Distributions Derived From Y-90 Pet Images: Phantom And Liver Radioembolization Patient Studies

PET images acquired after liver Y-90 radioembolization therapies are typically very noisy, which significantly challenges both visualization and quantification of activity distributions. To improve their noise characteristics, regularized iterative reconstruction algorithms such as block sequential regularized expectation maximization (Q.Clear for GE Healthcare, USA) have been proposed. In this study, we aimed to investigate the effects which different reconstruction algorithms may have on patient images, with reconstruction parameters initially narrowed down using phantom studies. Moreover, we evaluated the impact of these reconstruction methods on voxel-based dose distribution in phantom and patient studies (lesions and healthy livers). The International Electrotechnical Commission (IEC)/NEMA phantom, containing six spheres, was filled with Y-90 and imaged using a GE Discovery 690 PET/CT scanner with time-of-flight enabled. The images were reconstructed using Q.Clear (with beta parameter ranging from 0 to 8000) and ordered subsets expectation maximization. The image quality and quantification accuracy were evaluated by computing the hot (Q(H)) and cold (Q(C)) contrast recovery coefficients, background variability (BV) and activity bias. Next, dose distributions and dose volume histograms were generated using MIM (R) software's SurePlan LiverY90 toolbox. Subsequently, parameters optimized in these phantom studies were applied to five patient datasets. Dose parameters, such as D-max, D-mean, D-70, and V-100Gy, were estimated, and their variability for different reconstruction methods was investigated. Based on phantom studies, the beta parameter values optimized for image quality and quantification accuracy were 2500 and 300, respectively. When all investigated reconstructions were applied to patient studies, D-mean, D-50, D-70, and V-100Gy showed coefficients of variation below 8%; whereas the variability of D-max was up to 30% for both phantom and patient images. Although beta = 300-1000 would provide accurate activity quantification for a region of interest, when considering activity/dose voxelized distribution, higher beta value (e.g. 4000-5000) would provide the greatest accuracy for dose distributions. In this Y-90 radioembolization PET/CT study, the beta parameter in regularized iterative (Q.Clear) reconstruction was investigated for image quality, accurate quantification and dose distributions based on phantom experiments and then applied to patient studies. Our results indicate that more accurate dose distribution can be achieved from smoother PET images, reconstructed with larger beta values than those yielding the best activity quantifications but noisy images. Most importantly, these results suggest that quantitative measures, which are commonly used in clinics, such as SUVmax or SUVpeak (equivalent of D-max), should not be employed for Y-90 PET images, since their values would highly depend on the image reconstruction.