Van Nieuwenhove Vincent, Van Eyndhoven Geert, Batenburg K Joost, Buls Nico, Vandemeulebroucke Jef, De Beenhouwer Jan, Sijbers Jan
iMinds-Vision Lab, University of Antwerp, Antwerp (Wilrijk) B-2610, Belgium.
iMinds-Vision Lab, University of Antwerp, Antwerp (Wilrijk) B-2610, Belgium; Centrum Wiskunde & Informatica, Amsterdam NL-1090 GB, The Netherlands; and Mathematical Institute, Leiden University, Leiden NL-2300 RA, The Netherlands.
Med Phys. 2016 Dec;43(12):6429. doi: 10.1118/1.4967263.
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.
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.
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.
The LACO algorithm allows estimating high quality perfusion maps in low dose scanning protocols.
脑灌注X线计算机断层扫描（PCT）是一种用于全脑血流动力学信息无创成像的强大工具。传统的PCT在灌注过程中需要对大脑进行多次成像，因此辐射剂量是一个主要问题。作者提出了一种PCT重建算法，该算法能够在降低剂量的同时保持灌注图的高质量。它依赖于对动脉输入函数（AIF）的准确估计，而这又取决于动脉区域内衰减曲线的质量。
作者提出了局部衰减曲线优化（LACO）框架。它准确地对血管和动脉区域内的衰减曲线进行建模，并直接根据采集到的X线投影数据优化其形状。
LACO算法通过模拟和实际临床实验得到了广泛验证。定量和定性结果表明，我们提出的方法仅从少量X线投影就能准确估计血管和动脉衰减曲线。与基于重建图像估计AIF的传统方法不同，我们的方法直接根据投影数据计算出最佳AIF，从而得到更准确的灌注图。
LACO算法能够在低剂量扫描方案中估计高质量的灌注图。
