  CT perfusion imaging (CTP) is used in the diagnostic workup of acute ischemic
stroke (AIS). CTP may be performed within the angio suite using flat detector
CT (FDCT) to help reduce patient management time. In order to significantly
improve FDCT perfusion (FDCTP) imaging, data-processing algorithms need to be
able to compensate for the higher levels of noise, slow rotation speed, and a
lower frame rate of current FDCT devices. We performed a realistic simulation
of FDCTP acquisition based on CTP data from seven subjects. We used the time
separation technique (TST) as a model-based approach for FDCTP data processing.
We propose a novel dimension reduction in which we approximate the time
attenuation curves by a linear combination of trigonometric functions. Our goal
was to show that the TST can be used even without prior assumptions on the
shape of the attenuation profiles. We first demonstrated that a trigonometric
basis is suitable for dimension reduction of perfusion data. Using simulated
FDCTP data, we have shown that a trigonometric basis in the TST provided better
results than the classical straightforward processing even with additional
noise. Average correlation coefficients of perfusion maps were improved for
cerebral blood flow (CBF), cerebral blood volume, mean transit time (MTT) maps.
In a moderate noise scenario, the average Pearson's coefficient for the CBF map
was improved using the TST from 0.76 to 0.81. For the MTT map, it was improved
from 0.37 to 0.45. Furthermore, we achieved a total processing time from the
reconstruction of FDCTP data to the generation of perfusion maps of under 5
min. In our study cohort, perfusion maps created from FDCTP data using the TST
with a trigonometric basis showed equivalent perfusion deficits to classic CT
perfusion maps. It follows, that this novel FDCTP technique has potential to
provide fast and accurate FDCTP imaging for AIS patients.
