Computed tomography perfusion (CTP) is a functional imaging that allows for providing capillary-level hemodynamics information of the desired tissue in clinics. In this paper, we aim to offer insight into CTP imaging which covers the basics and current state of CTP imaging, then summarize the technical applications in the CTP imaging as well as the future technological potential. At first, we focus on the fundamentals of CTP imaging including systematically summarized CTP image acquisition and hemodynamic parameter map estimation techniques. A short assessment is presented to outline the clinical applications with CTP imaging, and then a review of radiation dose effect of the CTP imaging on the different applications is presented. We present a categorized methodology review on known and potential solvable challenges of radiation dose reduction in CTP imaging. To evaluate the quality of CTP images, we list various standardized performance metrics. Moreover, we present a review on the determination of infarct and penumbra. Finally, we reveal the popularity and future trend of CTP imaging.

Deep learning technology has been utilized in computed tomography, but, it needs centralized dataset to train the neural networks. To solve it, federated learning has been proposed, which collaborates the data from different local medical institutions with privacy-preserving decentralized strategy. However, lots of unpaired data is not included in the local models training and directly aggregating the parameters would degrade the performance of the updated global model. In order to deal with the issues, we present a semi-supervised and semi-centralized federated learning method to promote the performance of the learned global model. Specifically, each local model is trained with an unsupervised strategy locally at a fixed round. After that, the parameters of the local models are shared to aggregate on the server to update the global model. Then, the global model is further trained with a standard dataset, which contains well paired training samples to stabilize and standardize the global model. Finally, the global model is distributed to local models for the next training step. For shorten, we call the presented federated learning method as “3SC-FL”. Experiments demonstrate the presented 3SC-FL outperforms the compared methods, qualitatively and quantitatively.

Federated learning method shows great potential in computed tomography imaging field by utilizing a decentralized strategy with data privacy-preserving for local medical institutions. However, directly aggregating the parameters of each local model would degrade the generalization performance of the updated global model. In addition, well paired centralized training datasets can be collected in real world, which are not included in the current federated learning methods. To address the issue, we present a semi-centralized federated learning method to promote the generalization performance of the learned global model. Specifically, each local model is firstly trained locally at a fixed round, then, the parameters are aggregated on server to initialized the global model. After that, the global model is further trained with a standard dataset on the server, which contains well paired training samples to stabilize and standardize the global model. For shorten, we call the presented semi-centralized federated learning method as “SC-FL”. Experimental results on different local datasets demonstrate the presented SC-FL outperforms the competing methods.