The long-period CT perfusion imaging leads to an excess amount of radiation dose to the patient. However, the radiation dose could be significantly reduced if a previous normal-dose image is acquired before a set of low-dose scans of perfusion, and a filtering processing is performed on the differences between the current low-dose images and the previous normal-dose image, then the results are added to the previous image. But the selection of plenty of parameters makes the algorithm complicated. This paper proposes an innovative approach performed in sinogram domain instead of in image domain. First a normal-dose image and a set of low-dose projection data are acquired before the perfusion. Second the perfusion information is commendably reconstructed with sparsity constraints of the differences between current low-dose perfusion sinograms and previous low-dose sinogram. Finally, the reconstructed perfusion information is added to the previous normal-dose image. The proposed method was validated by simulated experiments with a set of brain CT perfusion images, which showed that the new method provided more accurate perfusion information; the time-attenuation curve was more close to that for normal-dose scan and the mean transit time more repeatable.
Algorithms Brain