Model observers that replicate human observers are useful tools for assessing image quality based on detection tasks. Linear model observers including nonprewhitening matched filters (NPWMFs) and channelized Hotelling observers (CHOs) have been widely studied and applied successfully to evaluate and optimize detection performance. However, there is still room for improvement in predicting human observer responses in detection tasks. In this study, we used a convolutional neural network to predict human observer responses in a two-alternative forced choice (2AFC) task for PET imaging. Lesion-absent and lesion-present images were reconstructed from clinical PET data with simulated lesions added to the liver and lungs and were used for the 2AFC task. We trained the convolutional neural network to discriminate images that human observers chose as lesion-present and lesion-absent in the 2AFC task. We evaluated the performance of the trained network by calculating the concordance between human observer responses and predicted responses from the network output and compared it to those of NPWMF and CHO. The trained network showed better agreement with human observers than the linear NPWMF and CHO model observers. The results demonstrate the potential for convolutional neural networks as model observers that better predict human performance. Such model observers can be used for optimizing scanner design, imaging protocols, and image reconstruction to improve lesion detection in PET imaging.
Recently, deep learning has become the mainstream method in multiple fields of artificial intelligence / machine learning (AI/ML) applications, including medical imaging. Encouraged by the neural diversity in the human body, our group proposed to replace the inner product in the current artificial neuron with a quadratic operation on inputs (called quadratic neuron) for deep learning. Since the representation capability at the cellular level is enhanced by the quadratic neuron, we are motivated to build network architectures and evaluate the potential of quadratic neurons towards “quadratic deep learning”. Along this direction, our previous theoretical studies have shown advantages of quadratic neurons and quadratic networks in terms of efficiency and representation. In this paper, we prototype a quadratic residual neural network (Q-ResNet) by incorporating quadratic neurons into a convolutional residual structure, and then deploy it for CT metal artifact reduction. Also, we report our experiments on a simulated dataset to show that Q-ResNet performs better than the classic NMAR algorithm.
Recently, deep learning has transformed many fields including medical imaging. Inspired by diversity of biological neurons, our group proposed quadratic neurons in which the inner product in current artificial neurons is replaced with a quadratic operation on inputs, thereby enhancing the capability of an individual neuron. Along this direction, we are motivated to evaluate the power of quadratic neurons in representative network architectures, towards “quadratic neuron based deep learning”. In this regard, our prior theoretical studies have shown important merits of quadratic neurons and networks. In this paper, we use quadratic neurons to construct an encoder-decoder structure, referred to as the quadratic autoencoder, and apply it for low-dose CT denoising. Then, we perform experiments on the Mayo low-dose CT dataset to demonstrate that the quadratic autoencoder yields a better denoising performance.