Time separation technique: Accurate solution for 4D C-Arm-CT perfusion imaging using a temporal decomposition model

The issue of perfusion imaging using a temporal decomposition model is to enable the reconstruction of undersampled measurements acquired with a slowly rotating x-ray-based imaging system, for example, a C-arm-based cone beam computed tomography (CB-CT). The aim of this work is to integrate prior knowledge into the dynamic CT task in order to reduce the required number of views and the computational effort as well as to save dose. The prior knowledge comprises of a mathematical model and clinical perfusion data.

In case of model-based perfusion imaging via superposition of specified orthogonal temporal basis functions, a priori knowledge is incorporated into the reconstructions. Instead of estimating the dynamic attenuation of each voxel by a weighting sum, the modeling approach is done as a preprocessing step in the projection space. This point of view provides a method that decomposes the temporal and spatial domain of dynamic CT data. The resulting projection set consists of spatial information that can be treated as individual static CT tasks. Consequently, the high-dimensional model-based CT system can be completely transformed, allowing for the use of an arbitrary reconstruction algorithm.

For CT, reconstructions of preprocessed dynamic in silico data are illustrated and evaluated by means of conventional clinical parameters for stroke diagnostics. The time separation technique presented here, provides the expected accuracy of model-based CT perfusion imaging. Consequently, the model-based handled 4D task can be solved approximately as fast as the corresponding static 3D task.

For C-arm-based CB-CT, the algorithm presented here provides a solution for resorting to model-based perfusion reconstruction without its connected high computational cost. Thus, this algorithm is potentially able to have recourse to the benefit from model-based perfusion imaging for practical application. This study is a proof of concept.