Optimization of Adaptive Threshold in Tumor Segmentation Based on I-124 PET/CT NEMA Phantom Study

Abstract

Numerous methods have been introduced to segment the thyroid tumors in iodine-124 (124I) Positron Emission Tomography/Computer Tomography (PET/CT) imaging. An adaptive threshold-based is preferable to a manual and fixed threshold-based approach for forecasting accurate and precise tumor volume in real-patient studies, as it was reported to be more resistant to noise and resolution. Manual segmentation was reported to be time-consuming, subjective, subject to inter- and intra-observer variability and prone to segmentation errors [1]. This study aimed to determine the optimal adaptive threshold for thyroid tumor segmentation in 124I PET/CT imaging.
 
 The objective of this study was firstly to determine the segmented volume using the standardized uptake value threshold method. Secondly, to compare the adaptive threshold and recovery coefficient (RC) value between the time of flight (TOF) and non-time of flight (non-TOF) PET/CT reconstructions in 124I phantom studies. Finally, to determine the accuracy and RC for 124I PET/CT studies. This study was done by placing NEMA 2012/IEC 2008 PET IQ Phantom and filled with 1 kBq/ml and 20 kBq/ml in its background and sphere each to model tumor background ratio (TBR) 20:1 of 124I PET/CT imaging. The phantom was scanned using TOF and non-TOF PET at 3, 4 and 5 minutes per bed position and reconstructed using 600, 1200 and 2000 beta penalization factors. Image registration, contouring and segmentation were performed using MIM Encore Software. A set of different adaptive thresholds (Tadaptive: 10%-50%) was applied to each sphere in the TBR image of the phantom using the tri-dimensional automatic segmentation tool. The optimal Tadaptive for each of the parameter assessed was defined based on the lowest goodness value calculated. The RC was calculated using mean value and known activity. Finally, the RC against the percentage of maximum was plotted to analyse the descending order of beta penalization factors for time per bed position.
 
 Figure 1 shows the image of 124I contoured VOIs on the spheres. The results showed that segmented volumes were not affected by the time per bed position in 124I PET/CT imaging. Table 1 shows the descending order beta penalization factors for time per bed position. Beta penalization factors of 600 to 2000 resulted in either underestimation or overestimation of the measured volume. Comparing TOF to non-TOF PET/CT 124I imaging, consistent and reproducible data were observed for the TOF PET for the three beta penalization factors. Tadaptive of 28.0% consistently gave the lowest goodness value shown by the closest R2 value to one, thus can produce lowest error if it is being used to reconstruct images.
 As a conclusion, 28% Tadaptive is suggested as the optimal threshold for 124I TOF PET/CT imaging due to consistent data obtained for all beta penalization factors assessed in this study. The inconsistent data obtained by non-TOF PET thus requires further optimization.