Purpose: Proton radiation therapy may achieve precise dose delivery to the tumor while sparing non-cancerous surrounding tissue, owing to the distinct Bragg peaks of protons. Aligning the high-dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors for CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution, and filtering of electronic noise. We assessed the potential of photon-counting computed tomography (PCCT) for improving SPR estimation by training a deep neural network on a domain transform from PCCT images to SPR maps. Approach: The XCAT phantom was used to simulate PCCT images of the head with CatSim, as well as to compute corresponding ground truth SPR maps. The tube current was set to 260 mA, tube voltage to 120 kV, and number of view angles to 4000. The CT images and SPR maps were used as input and labels for training a U-Net. Results: Prediction of SPR with the network yielded average root mean square errors (RMSE) of 0.26% to 0.41%, which was an improvement on the RMSE for methods based on physical modeling developed for single-energy CT at 0.40% to 1.30% and dual-energy CT at 0.41% to 3.00%, performed on the simulated PCCT data. Conclusions: These early results show promise for using a combination of PCCT and deep learning for estimating SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.

Motion artifacts are among the most important factors degrading the diagnostic performance of x-ray CT images, in particular for photon-counting CT where these artifacts can degrade the higher spatial resolution and quantitative imaging capabilities. The purpose of this simulation study is to evaluate the capability of deep neural networks to correct for motion artifacts in spectral photon-counting cardiac CT, by generating motion-corrected virtual monoenergetic images at a range of different keVs. We used CatSim to generate synthetic training data by simulating motion-corrupted and motion-free CT imaging (100 kVp, 1 s rotation) of the dynamic XCAT phantom including heart and respiratory motion. In total 2160 image pairs were generated. We trained two different neural networks for the task of estimating motion-artifact-reduced images from motion-corrupted images: one based on UNet and one based on a Wasserstein generative adversarial network with a gradient penalty (WGAN-GP). To make these networks applicable to virtual monoenergetic images at different energies, we trained them with 40 keV and 70 keV monoenergetic images as inputs and used a loss function with two terms: 1) L1 -loss on soft tissue and bone basis images and 2) perceptual loss on 70 keV monoenergetic images. Our results show that the motion artifacts from 40 keV to 100 keV are reduced substantially. In conclusion, these results demonstrate the potential of image-domain deep neural networks to correct for motion artifacts in spectral cardiac CT images.

One advantage of photon-counting CT compared to dual-energy CT is the possibility to perform K-edge imaging, where contrast agents such as iodine can be distinguished from other substances based on spectral characteristics. However, for iodine K-edge imaging in clinical CT, the three-basis decomposition problem is ill-conditioned due to the low K-edge energy of iodine, meaning that the decomposition is highly sensitive to both noise and miscalibrations. This makes robust three-basis decomposition difficult using standard techniques. In this simulation study we evaluate a novel method of performing K-edge imaging, which circumvents the challenging three-basis decomposition step by replacing it with multiple two-basis decompositions followed by a deep convolutional neural network to generate three basis images. Based on the XCAT phantom, we generated 1224 spectral phantom image slices of the neck, with iodine-filled blood vessels and calcifications, and simulated CT imaging in CatSim with a silicon-based detector model without quantum noise, i.e. in the high-dose limit. For each simulated slice, we used maximum likelihood to perform three two-basis decompositions, into PE-PVC, PE-iodine, and PVC-iodine, yielding six basis images in total. We then trained a U-Net to map these six input images to the ground-truth basis images, PE, PVC and iodine. Our results show that the proposed method can reproduce PE, PVC and iodine basis images with high accuracy, in the high-dose limit. This suggests that the proposed three-basis decomposition method may be a feasible way of performing K-edge CT imaging with iodine, with important potential implications for imaging of the carotid arteries.

Proton radiation therapy has the potential of achieving precise dose delivery to the tumor while sparing noncancerous surrounding tissue, owing to the sharp Bragg peaks of protons. Aligning the high dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors within CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution and filtering of electronic noise. In this study, the potential of photon-counting computed tomography for improving SPR estimation was assessed by training a deep neural network on a domain transform from photon-counting CT images to SPR maps. XCAT phantoms of the head were generated and used to simulate photon-counting CT images with CatSim, as well as to compute corresponding ground truth SPR maps. The CT images and SPR maps were then used as input and labels to a neural network. Prediction of SPR with the network yielded mean root mean square errors (RMSE) of 0.26-0.41 %, which is an improvement on errors reported for methods based on dual energy CT (DECT). These early results show promise for using a combination of photon-counting CT and deep learning for predicting SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.