  Dynamic computed tomography perfusion (CTP) imaging is a promising approach
for acute ischemic stroke diagnosis and evaluation. Hemodynamic parametric maps
of cerebral parenchyma are calculated from repeated CT scans of the first pass
of iodinated contrast through the brain. It is necessary to reduce the dose of
CTP for routine applications due to the high radiation exposure from the
repeated scans, where image denoising is necessary to achieve a reliable
diagnosis. In this paper, we proposed a self-supervised deep learning method
for CTP denoising, which did not require any high-dose reference images for
training. The network was trained by mapping each frame of CTP to an estimation
from its adjacent frames. Because the noise in the source and target was
independent, this approach could effectively remove the noise. Being free from
high-dose training images granted the proposed method easier adaptation to
different scanning protocols. The method was validated on both simulation and a
public real dataset. The proposed method achieved improved image quality
compared to conventional denoising methods. On the real data, the proposed
method also had improved spatial resolution and contrast-to-noise ratio
compared to supervised learning which was trained on the simulation data
