Implementation of CNN-based multi-slice model observer for 3D cone beam CT

In this study, we implement CNN-based multi-slice model observer for 3D CBCT images and compare it with a conventional linear model observer. To evaluate detection performance of the model observer, we considered SKE/BKS four alternative detection task for 3D CBCT images. To generate training and testing datasets, we used a power law spectrum to generate anatomical noise structure. Generated anatomical noise was reconstructed by using FDK algorithm with a CBCT geometry. We employed msCHO and vCHO with LG channels as a comparative linear model observer. We implemented CNN-based multi-slice model observer mimicked msCHOa, which was composed of multiple CNNs. Each CNN consisted of convolutional operator, the batch normalization, a Leaky-ReLU as activation function, and had the following characteristics. (1) To reduce the number of variables, we used full convolutional network and set the filter size as 3×3. (2) Since downscaling layer ignores high frequency components, we did not use any kind of downscaling layer. We used ADAM optimizer and the cross-entropy loss function to train the network. We compared the detection performance of CNN-based multislice model observer, vCHO and msCHO using 1,000 trial cases when the number of slices was three, five and seven. For all numbers of slices, CNN-based multi-slice model observer provided higher detection performance than conventional linear model observers. CNN-based multi-slice model observer required more than 50,000 signal-present and signal-absent images to provide optimized performance, while msCHO required about 5,000 image pairs. Strategy to reduce the amount of training dataset will be a future research topic.