Approximating the idea observer on list mode data with comparison to CNN methods

Abstract

The ideal observer(IO) sets an upper bound for all other classification observers by employing the full statistics of the imaging system and the random object ensemble.1 This upper bound can be used for the optimization of the imaging system and as a performance measure for other observers. Without binning the data and losing information in the process, the list mode data format has the potential of eliminating null functions2 and supporting better performance for clinical tasks. However, it is not easy to track the performance of the ideal observer. A Monte Carlo Markov Chain (MCMC) method3 was proposed for binned data. Here in this work, we present an example of approximating the ideal observer for list mode data given a background known statistically signal known exactly model (BKS/SKE). The receiver operating characteristic (ROC) curve of the IO on list mode data is compared to the corresponding approximation by use of supervised learning methods proposed for binned data,4 where convolutional neural networks (CNN)5 are used to approximate the probability of the signal present hypothesis given the image, which is a monotone transformation of the IO statistic. The results show the superior performance of IO on list mode data. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.