Local attenuation curve optimization framework for high quality perfusion maps in low-dose cerebral perfusion CT

Purpose: Cerebral perfusion x-ray computed tomography (PCT) is a powerful tool for noninvasive imaging of hemodynamic information throughout the brain. Conventional PCT requires the brain to be imaged multiple times during the perfusion process, and hence radiation dose is a major concern. The authors propose a PCT reconstruction algorithm that allows for lowering the dose while maintaining a high quality of the perfusion maps. It relies on an accurate estimation of the arterial input function (AIF), which in turn depends on the quality of the attenuation curves in the arterial region. Methods: The authors propose the local attenuation curve optimization (LACO) framework. It accurately models the attenuation curves inside the vessel and arterial regions and optimizes its shape directly based on the acquired x-ray projection data. Results: The LACO algorithm is extensively validated with simulation and real clinical experiments. Quantitative and qualitative results show that our proposed approach accurately estimates the vessel and arterial attenuation curves from only few x-ray projections. In contrast to conventional approaches, where the AIF is estimated based on the reconstructed images, our method computes an optimal AIF directly based on the projection data, resulting in far more accurate perfusion maps. Conclusions: The LACO algorithm allows estimating high quality perfusion maps in low dose scanning protocols.

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