A mode based de-convolution algorithm in CT cerebral tumor perfusion image

Ping Zhang, Liqun Gao, Zhaohua Cui, Xiaoyou Shan
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Abstract

With the advent of fast, multi-slice CT scanners, CT perfusion technique has significant implications in the diagnosis and treatment of cerebral tumor in the past 2 decades. The fundamental bases for dual chamber model which is selected to fit the tumor tissue are both the transport by blood flow of intravenously iodinated constrast material to tissue and the exchange by diffusion of these contrast between the intravascular space and the extravascular interstitial space. In this paper, a digital de-convolution algorithm is introduced and realized to compute the tissue residue function R(t), and the following parameters: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT) and the tissue permeability coefficient (TPS). They can provide important reference before and after therapy. This algorithm is tested by CT cerebral tumors medical image data.
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基于模式的CT脑肿瘤灌注图像去卷积算法
随着快速多层CT扫描仪的出现,CT灌注技术在近20年来对脑肿瘤的诊断和治疗具有重要意义。选择适合肿瘤组织的双腔模型的基本依据是静脉内碘化造影剂向组织的血流转运以及这些造影剂在血管内间隙和血管外间隙间的弥散交换。本文引入并实现了一种计算组织残差函数R(t)、脑血流量(CBF)、脑血容量(CBV)、平均传递时间(MTT)和组织渗透系数(TPS)的数字反卷积算法。可为治疗前后提供重要参考。用CT脑肿瘤医学图像数据对该算法进行了验证。
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