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Large area detector computed tomography systems with fastrotating gantries enable volumetric dynamic cardiac perfusion studies. Prospectively ECG-triggered acquisitions limit the data acquisition to a predefined cardiac phase and thereby reduce X-ray dose andlimit motion artifacts. Even in the case of highly accurate prospective triggering and stable heart rate, spatial misalignment of the cardiac volumes acquired and reconstructed per cardiac cycle may occurdue to small motion pattern variations from cycle to cycle. These misalignments reduce the accuracy of the quantitative analysis of myocardial perfusion parameters on a per voxel basis. An image based solution to this problem is elastic 3D image registration of dynamic volume sequences with variable contrast, as it is introduced in thiscontribution. After circular cone-beam CT reconstruction of cardiacvolumes covering large areas of the myocardial tissue, the completeseries is aligned with respect to a chosen reference volume. The results of the quantitative perfusion analysis are compared on pig datausing the non-registered versus the registered data set. The reduced spatial misalignment leads to an improved characterization of myocardial perfusion confirming the potential of this method. Conclusions - In conclusion, an elastic image registration-based method was proposed to improve the characterization of CT-based estimates of myocardial perfusion. The technique’s performance, that was visually and quantitatively assessed on three pig data sets, confirmed its potential. The proposed method may also be applied to other perfusion studies being limited by inconsistent motion states.

Purpose: X-ray based tomographic blood perfusion imaging requires recovery of contrast time-attenuation-curves from dynamic projection data. When using slowly rotating imaging systems this task is challenging due to non-simultaneous projection acquisition. A dynamic reconstruction method is proposed that aims at compensating the lack of simultaneously acquired information by incorporating prior knowledge about the expected temporal contrast dynamics. Methods: A decomposition model using temporal basis functions to approximate time-attenuation-curves is integrated into an iterative tomographicre construction method. The computationally efficient implementationof the proposed approach makes use of standard forward- and back projections as well as scalar products in image space. The critical issue of projection noise propagation is tackled by application of regularization which is realizedby early stopping of iteration cycles and by proper selection of smooth temporal basis functions. The performance of the proposed dynamic reconstruction approach is evaluated in a simulation study concerning various aspects: noise propagation and regularization, specification of temporal model, and type of acquisition mode. Results: The evaluation based on dynamic phantom data indicates that tomographic recovery of contrast time-attenuation-curves in tissue can be achieved with an average range of accuracy of ca. 2% (with respect todynamic peak attenuation) under ideal noise-free conditions. The relative estimation error for arterial time-attenuation-curves is in the range of 8%, which is due to faster contrast dynamics in the artery. In general, performance depends on the level of acquired information contained in the projection data which is mainly influenced by the type of rotational acquisition mode; restrictions in angular range and speed can lead to limited accuracy. The analysis of propagated projection noise in a statistical Bias-Variance framework reveals relative noise levels in estimated time-attenuation-curves of 3-4% intissue regions and below 1% in vessels when using optimized settingsfor regularization. Here, the effect of noise suppression depends oninterrelation between the model. Conclusions: For usage with slowly rotating imaging systems the presented model-based iterative dynamic reconstruction method is capable of recovering contrast time-attenuation-curves related to tissue perfusion. The proposed regularization framework is an effective means to limit the impact of projection noise which is a factor dominating estimation accuracy in tissue regions.