Impact of image reconstruction method on dose distributions derived from 90Y PET images: phantom and liver radioembolization patient studies.

PET images acquired after liver 90Y 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 90Y and imaged using a GE Discovery 690 PET/CT scanner with time-of-flight enabled. The images were reconstructed using Q.Clear (with ß parameter ranging from 0 to 8000) and ordered subsets expectation maximization. The image quality and quantification accuracy were evaluated by computing the hot ([Formula: see text]) and cold ([Formula: see text]) contrast recovery coefficients, background variability (BV) and activity bias. Next, dose distributions and dose volume histograms were generated using MIM® software's SurePlan LiverY90 toolbox. Subsequently, parameters optimized in these phantom studies were applied to five patient datasets. Dose parameters, such as Dmax, Dmean, D70, and V100Gy, were estimated, and their variability for different reconstruction methods was investigated. Based on phantom studies, the ß parameter values optimized for image quality and quantification accuracy were 2500 and 300, respectively. When all investigated reconstructions were applied to patient studies, Dmean, D50, D70, and V100Gy showed coefficients of variation below 8%; whereas the variability of Dmax was up to 30% for both phantom and patient images. Although ß = 300-1000 would provide accurate activity quantification for a region of interest, when considering activity/dose voxelized distribution, higher ß value (e.g. 4000-5000) would provide the greatest accuracy for dose distributions. In this 90Y radioembolization PET/CT study, the ß 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 ß 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 Dmax), should not be employed for 90Y PET images, since their values would highly depend on the image reconstruction.

Segmentation-free direct tumor volume and metabolic activity estimation from PET scans.

Tumor volume and metabolic activity are two robust imaging biomarkers for predicting early therapy response in F-fluorodeoxyglucose (FDG) positron emission tomography (PET), which is a modality to image the distribution of radiotracers and thereby observe functional processes in the body. To date, estimation of these two biomarkers requires a lesion segmentation step. While the segmentation methods requiring extensive user interaction have obvious limitations in terms of time and reproducibility, automatically estimating activity from segmentation, which involves integrating intensity values over the volume is also suboptimal, since PET is an inherently noisy modality. Although many semi-automatic segmentation based methods have been developed, in this paper, we introduce a method which completely eliminates the segmentation step and directly estimates the volume and activity of the lesions. We trained two parallel ensemble models using locally extracted 3D patches from phantom images to estimate the activity and volume, which are derivatives of other important quantification metrics such as standardized uptake value (SUV) and total lesion glycolysis (TLG). For validation, we used 54 clinical images from the QIN Head and Neck collection on The Cancer Imaging Archive, as well as a set of 55 PET scans of the Elliptical Lung-Spine Body Phantom™with different levels of noise, four different reconstruction methods, and three different background activities, namely; air, water, and hot background. In the validation on phantom images, we achieved relative absolute error (RAE) of 5.11 % ±3.5% and 5.7 % ±5.25% for volume and activity estimation, respectively, which represents improvements of over 20% and 6% respectively, compared with the best competing methods. From the validation performed using clinical images, we found that the proposed method is capable of obtaining almost the same level of agreement with a group of trained experts, as a single trained expert is, indicating that the method has the potential to be a useful tool in clinical practice.

Estimation of organ and effective doses from newborn radiography of the chest and abdomen.

Neonatal intensive care patients undergo frequent chest and abdomen radiographic imaging. In this study, the organ doses and the effective dose resulting from combined chest-abdomen radiography of the newborn child are determined. These values are calculated using the Monte Carlo simulation software PCXCM 2.0 and compared with direct dose measurements obtained from thermoluminescent detectors (TLDs) in a physical phantom. The effective dose obtained from PCXMC is 21.2 ± 0.7 µSv and that obtained from TLD measurements is 22.0 ± 0.5 µSv. While the two methods are in close agreement with regard to the effective dose, there is a wide range of variation in organ doses, ranging from 85 % difference for the testes to 1.4 % for the lungs. Large organ dose variations are attributed to organs at the edge of the field of view, or organs with large experimental error or simulation uncertainty. This study suggests that PCXMC can be used to estimate organ and effective doses for newborn patients.