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用于横向成像平面中相对弹性模量重建的无创血管移位估计。

Noninvasive vascular displacement estimation for relative elastic modulus reconstruction in transversal imaging planes.

机构信息

Medical UltraSound Imaging Center (MUSIC), Department of Radiology, Radboud University Nijmegen Medical Center, Nijmegen 6500 HB, The Netherlands.

出版信息

Sensors (Basel). 2013 Mar 11;13(3):3341-57. doi: 10.3390/s130303341.

Abstract

Atherosclerotic plaque rupture can initiate stroke or myocardial infarction. Lipid-rich plaques with thin fibrous caps have a higher risk to rupture than fibrotic plaques. Elastic moduli differ for lipid-rich and fibrous tissue and can be reconstructed using tissue displacements estimated from intravascular ultrasound radiofrequency (RF) data acquisitions. This study investigated if modulus reconstruction is possible for noninvasive RF acquisitions of vessels in transverse imaging planes using an iterative 2D cross-correlation based displacement estimation algorithm. Furthermore, since it is known that displacements can be improved by compounding of displacements estimated at various beam steering angles, we compared the performance of the modulus reconstruction with and without compounding. For the comparison, simulated and experimental RF data were generated of various vessel-mimicking phantoms. Reconstruction errors were less than 10%, which seems adequate for distinguishing lipid-rich from fibrous tissue. Compounding outperformed single-angle reconstruction: the interquartile range of the reconstructed moduli for the various homogeneous phantom layers was approximately two times smaller. Additionally, the estimated lateral displacements were a factor of 2-3 better matched to the displacements corresponding to the reconstructed modulus distribution. Thus, noninvasive elastic modulus reconstruction is possible for transverse vessel cross sections using this cross-correlation method and is more accurate with compounding.

摘要

动脉粥样硬化斑块破裂可引发中风或心肌梗死。富含脂质的薄纤维帽斑块比纤维斑块更容易破裂。富含脂质和纤维组织的弹性模量不同,可以使用从血管内超声射频 (RF) 数据采集估计的组织位移来重建。本研究使用基于迭代 2D 互相关的位移估计算法,调查了是否可以对横向成像平面中血管的非侵入性 RF 采集进行模量重建。此外,由于已知通过在不同波束转向角度估计的位移进行复合可以改善位移,我们比较了有和没有复合的模量重建的性能。为了进行比较,模拟和实验 RF 数据是各种血管模拟体模生成的。重建误差小于 10%,这似乎足以区分富含脂质的组织和纤维组织。复合优于单角度重建:各种均匀体模层的重建模量的四分位间距大约小两倍。此外,估计的横向位移与对应于重建的模量分布的位移的匹配程度提高了 2-3 倍。因此,使用这种互相关方法可以对横向血管横截面进行非侵入性弹性模量重建,并且复合可以提高精度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fdbf/3658750/71255ea9c12a/sensors-13-03341f1.jpg

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