A novel use of time separation technique to improve flat detector CT perfusion imaging in stroke patients

Abstract

Background: 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. Purpose: 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. Methods: 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. Results: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. Conclusions: 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.

Multimodal CT imaging characteristics may predict post-reperfusion infarct volume in wake-up stroke patients

“…Several studies have demonstrated that ischemic core volume estimations varied significantly between different software packages, and the ischemic core volume on RAPID was most closely correlated with the final infarct volume (20)(21)(22)(23)(24). The DAWN and DEFUSE 3 trials selected patients based on ischemic core volumes estimated with RAPID software (iSchemaView, Menlo Park, CA, USA) (25,26).…”

Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging

“…In (Kulvait et al, 2022) only the first coefficient, which corresponds to a constant basis, is used for brain segmentation. When using the analytical basis functions, this is also the case for the liver segmentation.…”

“…A simplified approach would be to reconstruct every rotation separately, the straightforward approach, which can result in over or underestimation of perfusion parameters (Haseljić et al, 2021). Recent publications have shown that model-based reconstruction and time separation technique (TST) could deal with poor temporal resolution (Montes and Lauritsch, 2009;Neukirchen et al, 2010;Manhart et al, 2013;Bannasch et al, 2018;Kulvait et al, 2022;Haseljić et al, 2021Haseljić et al, , 2022 and provide highly accurate liver perfusion maps.…”

Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique

“…Instead of treating all projections of one rotation to be acquired at the same time point (i.e. straightforward reconstruction), the TST models the temporal behaviour of every voxel as a linear combination of basis functions that are mutually orthonormal [2], [3]. The liver segmentation was the subject of research in many works [4]- [6].…”