Qu Hongying, Gao Yongan, Li Meiling, Zhai Shuo, Zhang Miao, Lu Jie
Department of radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China.
Front Neurol. 2021 Feb 4;11:621202. doi: 10.3389/fneur.2020.621202. eCollection 2020.
Atherosclerotic disease of the internal carotid artery (ICA) is a common reason for ischemic stroke. Computed tomography angiography (CTA) is a common tool for evaluation of internal carotid artery (ICA) stenosis. However, blooming artifacts caused by calcified plaques might lead to overestimation of the stenosis grade. Furthermore, the intracranial ICA is more vulnerable to calcification than other ICA segments. The proposed technique, dual-energy computed tomography (DECT) with a modified three-material decomposition algorithm may facilitate the removal of calcified plaques and thus increase diagnostic accuracy. The objective of the study is to assess the accuracy of the modified three-material decomposition algorithm for grading intracranial ICA stenosis after calcified plaque removal, with digital subtraction angiography (DSA) used as a reference standard. In total, 41 patients underwent DECT angiography and DSA. The three-material decomposition DECT algorithm for calcium removal was applied. We evaluated 64 instances of calcified stenosis using conventional CTA, the previous non-modified calcium removal DECT technique, the modified DECT algorithm, and DSA. The correlation coefficient ( ) between the results generated by the modified algorithm and DSA was also calculated. The virtual non-calcium images (VNCa) produced by the previous non-modified calcium removal algorithm were named VNCa 1, and those produced by the modified algorithm were named VNCa 2. The assigned degree of stenosis of VNCa 1 (mean stenosis: 39.33 ± 19.76%) differed significantly from that of conventional CTA images (mean stenosis: 59.03 ± 25.96%; = 0.001), DSA (13.19 ± 17.12%, < 0.001). VNCa 1 also significantly differed from VNCa 2 (mean stenosis: 15.35 ± 18.70%, < 0.001). In addition, there was a significant difference between the degree of stenosis of VNCa 2 and conventional CTA images ( < 0.001). No significant differences were observed between VNCa 2 and DSA ( = 0.076). The correlation coefficient ( ) between the stenosis degree of the VNCa 2 and DSA images was 0.991. The proposed DECT with a modified three-material decomposition algorithm for calcium removal has high sensitivity for the detection of relevant stenoses, and its results were more strongly correlated with DSA than with those of conventional CTA or the previous non-modified algorithm. Further, it overcomes CTA's previous problem of overestimating the degree of stenosis because of blooming artifacts caused by calcified plaques. It is useful to account for calcified plaques while evaluating carotid stenosis.
颈内动脉(ICA)粥样硬化疾病是缺血性中风的常见原因。计算机断层扫描血管造影(CTA)是评估颈内动脉(ICA)狭窄的常用工具。然而,钙化斑块引起的 blooming 伪影可能导致对狭窄程度的高估。此外,颅内ICA比ICA的其他节段更容易发生钙化。所提出的技术,即采用改良三物质分解算法的双能计算机断层扫描(DECT),可能有助于去除钙化斑块,从而提高诊断准确性。本研究的目的是评估改良三物质分解算法在去除钙化斑块后对颅内ICA狭窄分级的准确性,并将数字减影血管造影(DSA)用作参考标准。共有41例患者接受了DECT血管造影和DSA检查。应用了用于去除钙的三物质分解DECT算法。我们使用传统CTA、先前未改良的去钙DECT技术、改良DECT算法和DSA评估了64例钙化狭窄病例。还计算了改良算法与DSA结果之间的相关系数()。先前未改良的去钙算法产生的虚拟无钙图像(VNCa)命名为VNCa 1,改良算法产生的命名为VNCa 2。VNCa 1的指定狭窄程度(平均狭窄:39.33±19.76%)与传统CTA图像(平均狭窄:59.03±25.96%;=0.001)、DSA(13.19±17.12%,<0.001)有显著差异。VNCa 1与VNCa 2也有显著差异(平均狭窄:15.35±18.70%,<0.001)。此外,VNCa 2与传统CTA图像的狭窄程度有显著差异(<0.001)。VNCa 2与DSA之间未观察到显著差异(=0.076)。VNCa 2与DSA图像的狭窄程度之间的相关系数()为0.991。所提出的采用改良三物质分解算法去除钙的DECT对相关狭窄的检测具有高灵敏度,其结果与DSA的相关性比与传统CTA或先前未改良算法的结果更强。此外,它克服了CTA先前因钙化斑块引起的blooming伪影而高估狭窄程度的问题。在评估颈动脉狭窄时考虑钙化斑块是有用的。