Deep learning (DL) based methods have been widely adopted in computed tomography (CT) field. And they also show a great potential in photon-counting CT (PCCT) imaging field. They usually require a large quantity of paired data to train networks. However, it is time-consuming and expensive to collect such large-scale PCCT dataset. In addition, lots of energy-integrating detector (EID) data are not yet included in the DL-based PCCT reconstruction network training. In this work, to address the issue of limited PCCT data and take advantage of labeled EID data, we propose a novel unsupervised full-spectrum-knowledge-aware DL-based network (FSANet), which contains supervised and unsupervised networks, to produce high-quality PCCT images. Specifically, the supervised network is trained based on paired EID dataset and serves as the prior knowledge to regularize the unsupervised PCCT network training. Moreover, a data-fidelity term for characterizing the PCCT image characteristics is constructed as a self-supervised term. Finally, we train the PCCT network with the prior knowledge and self-supervised terms following an unsupervised learning strategy. Numerical studies on synthesized clinical data are conducted to validate and evaluate the performance of the presented FSANet method, qualitatively and quantitatively. The experimental results demonstrate that presented FSANet method significantly improves the PCCT image quality in the case of limited photon counts.

Photon-counting computed tomography (PCCT) can simultaneously obtain multi-energy data with abundant energy-dependent material-specific information of the scanned object. However, the photon counts in each energy bin are decreased and the collected data suffers from photon starvation effects, which degrades the quality of the reconstructed PCCT images. To solve it, many statistical iteration reconstruction (SIR) methods have been proposed by constructing data-fidelity and prior information terms to suppress noise and remove artifacts. However, most of the current SIR methods assume the noise in PCCT images follows a Gaussian distribution, which deviates the real distribution of the noise in PCCT images. Therefore, we propose a new statistical iteration reconstruction method by considering more complex noise distribution in reality. Specifically, Gaussian mixture model (GMM), which is a universal approximator for any continuous density function, is utilized to model the noise in PCCT images. Moreover, the multi-energy PCCT images are treated as a 3order tensor which is regularized by three dimensional total variation (3DTV) prior term. Finally, a statistical iteration reconstruction model based on GMM and 3DTV is established for PCCT imaging. For shorten, we call the presented reconstruction model as "GMM-3DTV". We then develop an expectation-maximization (EM) algorithm to solve the presented GMM-3DTV method. Numerical studies demonstrate the improvements of the presented GMM-3DTV method over the competing methods.