In recent years, x-ray photon-counting detectors (PCDs) have become increasingly popular due to their ability to discriminate energy and low noise levels. However, technical issues (e.g., charge splitting and pulse pileup effects) can affect the data quality by distorting the energy spectrum. To address those issues, based on a deep neural network-based approach using a Wasserstein generative adversarial network (WGAN) framework for PCD data correction, we evaluate the effectiveness of pre-trained and training-from-scratch convolutional neural networks (CNNs) as perceptual loss functions to address charge splitting and pulse pileup correction challenges in photon counting computed tomography (CT) data. Different CNN architectures, including VGG11, VGG13, VGG16, VGG19, ResNet50, and Xception, are evaluated. Compared with the method using a pre-trained network, our findings indicate that training the CNNs from scratch on our dataset produces better results. It significantly affects the performance for the choice of CNN architecture as a perceptual loss in the WGAN framework. Furthermore, because recent explosive interest on transformers has suggested their potential to be useful for computer vision tasks, we also evaluate transformers to maximize the attribute-related information contained in the image feature by texture features extraction. Our study emphasizes the importance of selecting appropriate network architecture and training strategy when implementing the WGAN framework for photon counting CT data correction.
Single photon emission computed tomography (SPECT) is commonly used with radioiodine scintigraphy to evaluate patients with multiple diseases such as thyroid cancer. The clinical gamma camera for SPECT contains a mechanical collimator that greatly compromises dose efficiency and limits diagnostic performance. The Compton camera is emerging as a promising alternative for mapping the distribution of radio-pharmaceuticals in the thyroid, since the Compton camera does not require mechanical collimation and in principle does not reject gamma ray photons. In this study, a high-efficiency tomographic imaging system is designed with a Compton camera for thyroid cancer imaging. A Timepix3-based Compton camera is selected for collecting gamma photons emitted from an I-131 phantom, and the backprojection filtration algorithm is applied for image reconstruction. The results demonstrate the feasibility of the Compton camera for high efficiency SPECT imaging and also the limitations that need further efforts to address.
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