Abstract

The quantitative analysis of the blood flow in the brain tissue is one of the important problems in neurosurgery. It arises when diagnosing acute ischemic stroke. This problem can be solved using computed tomography (CT) perfusion imaging. There are various methods for extracting quantitative characteristics of cerebral blood flow from CT perfusion data, which differ in degrees of their noise resistance. More noise-resistant methods enable the reduction in radiation doses when conducting the examination of the patient. Hence, the development of noise-resistant methods is an important problem. This paper presents an algorithm for evaluating the quantitative characteristics of cerebral blood flow, based on the regularization using the projection onto a set of monotonic functions while minimizing the functional of total generalized variation (TGV). The proposed approach is tested on synthetic and real-world data. It yields better results than the singular value decomposition (SVD) method with Tikhonov regularization and methods of total variation (TV) minimization and TGV minimization.

中文翻译：

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基于反卷积的CT脑灌注的投影方法

摘要

脑组织中血流的定量分析是神经外科中的重要问题之一。诊断急性缺血性中风时会出现这种情况。使用计算机断层扫描（CT）灌注成像可以解决此问题。有多种方法可从CT灌注数据中提取脑血流的定量特征，这些方法的抗噪声程度不同。更多的抗噪声方法可以在进行患者检查时减少辐射剂量。因此，开发抗噪声方法是一个重要的问题。本文提出了一种用于评估脑血流定量特征的算法，该算法基于投影到一组单调函数的正则化同时最小化总广义变异（TGV）的函数。所提出的方法已在综合和真实数据上进行了测试。它比使用Tikhonov正则化的奇异值分解（SVD）方法以及总变差（TV）最小化和TGV最小化的方法产生更好的结果。