The Impact of Bayesian Penalized Likelihood Reconstruction Algorithm on Quantitative Accuracy of Positron Emission Tomography Volumetric Measurements

Abstract

Objectives The metabolic tumor volume (MTV) of positron emission tomography/computed tomography (PET/CT) is an important index to evaluate the prognosis and the responses of treatments. The purpose of this study is to assess the impact of Bayesian Penalized Likelihood (BPL) reconstruction algorithm and segmentation methods on the accuracy of MTV via a phantom study.Methods Using the National Electrical Manufactures Association/International Electrotechnical Commission (NEMA/IEC) image quality phantom, six hot spheres and background were filled with 21.56 KBq/ml and 5.39 KBq/ml Na 18 F (a sphere to background ratio of 4: 1). Acquired images were reconstructed using BPL (β = 400) and non-BPL (Ordered subsets expectation maximization + time of flight + point spread function, OSEM+TOF+PSF) algorithms, respectively. MTVs of six spheres were delineated using maximum standardized uptake value (SUV max ) percentage threshold method and iterative adaptive method, respectively. The actual measured volumes of spheres were used as the standard for comparative analysis.Results The MTVs measurement errors in BPL were 4.96%, -3.00%, 6.18%, 5.20%, -10.00% and 18.33%, which was significantly lower than that in non-BPL ( Z = - 2.562, p = 0.009), and the measurement errors in non-BPL were 16.70%, 10.77%, 26.00%, 30.00%, 61.82% and 113.33%. The optimal percentage SUV max threshold of spheres in BPL algorithm was raged in 40% - 45%, which was not affected by the ball size. And there was no significant difference of MTVs measurement accuracy between the 42%SUV max and iterative adaptive threshold (Z = -0.48, p = 0.699). However, using the non-BPL algorithm, the measurement errors of 42%SUV max and iterative adaptive delineation methods were 16.70%, 10.77%, 26.00%, 30.00%, 61.82%, 113.33%, and -7.70%, -9.00%, -8.73%, -5.20%, -12.91%, 38.33% respectively. The MTVs measurement accuracy of iterative adaptive was significantly better than that of the 42%SUV max threshold (Z = -2.24, p = 0.026). The iterative adaptive and 42%SUV max threshold methods had excellent interobserver reliability (ICCs=1.00 for all of six spheres) for MTVs measurement.Conclusion BPL reconstruction algorithm can improve the accuracy of MTVs measurements, especially for small lesions. In the case of using non-BPL methods, the iterative adaptive delineation method should be adopted to improve the accuracy of MTVs measurements.