Full-spectrum-knowledge-aware unsupervised network for photon-counting CT imaging

Abstract

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.

Semi-centralized federated learning network for low-dose CT imaging

“…Therefore, more advanced loss functions, such as perceptual loss 10 will be included in the SC-FL to preserve more anatomic structures. In the future, we will apply the presented SC-FL to different tasks in CT imaging field, such as photon-counting CT, 11,12 cone-beam CT 13 and cerebral perfusion CT. 14…”

Robust multi-institution low-dose CT imaging with semi-supervised federated learning network

“…Therefore, texture preserving loss functions, such as perceptual loss, 10 can be utilized in the 3SC-FL to further improve the image quality. In addition, the presented 3SC-FL has the potential to be applied to the multi-task in CT imaging, such as cone-beam CT, 11 cerebral perfusion CT 12,13 and spectral CT. [14][15][16]…”