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各向异性多尺度流体配准:在乳腺磁共振成像中的评估

Anisotropic multi-scale fluid registration: evaluation in magnetic resonance breast imaging.

作者信息

Crum W R, Tanner C, Hawkes D J

机构信息

Centre for Medical Image Computing (CMIC), University College London, London, WC1E 6BT, UK.

出版信息

Phys Med Biol. 2005 Nov 7;50(21):5153-74. doi: 10.1088/0031-9155/50/21/014. Epub 2005 Oct 19.

DOI:10.1088/0031-9155/50/21/014
PMID:16237247
Abstract

Registration using models of compressible viscous fluids has not found the general application of some other techniques (e.g., free-form-deformation (FFD)) despite its ability to model large diffeomorphic deformations. We report on a multi-resolution fluid registration algorithm which improves on previous work by (a) directly solving the Navier-Stokes equation at the resolution of the images, (b) accommodating image sampling anisotropy using semi-coarsening and implicit smoothing in a full multi-grid (FMG) solver and (c) exploiting the inherent multi-resolution nature of FMG to implement a multi-scale approach. Evaluation is on five magnetic resonance (MR) breast images subject to six biomechanical deformation fields over 11 multi-resolution schemes. Quantitative assessment is by tissue overlaps and target registration errors and by registering using the known correspondences rather than image features to validate the fluid model. Context is given by comparison with a validated FFD algorithm and by application to images of volunteers subjected to large applied deformation. The results show that fluid registration of 3D breast MR images to sub-voxel accuracy is possible in minutes on a 1.6 GHz Linux-based Athlon processor with coarse solutions obtainable in a few tens of seconds. Accuracy and computation time are comparable to FFD techniques validated for this application.

摘要

尽管基于可压缩粘性流体模型的配准能够对大的微分同胚变形进行建模,但它尚未像其他一些技术(例如自由形式变形(FFD))那样得到广泛应用。我们报告了一种多分辨率流体配准算法,该算法在先前工作的基础上进行了改进,具体如下:(a)在图像分辨率下直接求解纳维-斯托克斯方程;(b)在全多网格(FMG)求解器中使用半粗化和隐式平滑来适应图像采样各向异性;(c)利用FMG固有的多分辨率特性来实现多尺度方法。我们在11种多分辨率方案下,对5幅磁共振(MR)乳腺图像施加6种生物力学变形场进行了评估。定量评估通过组织重叠和目标配准误差进行,并且通过使用已知对应关系而非图像特征进行配准来验证流体模型。通过与经过验证的FFD算法进行比较以及将其应用于经受大的施加变形的志愿者图像来给出相关背景信息。结果表明,在基于1.6 GHz Linux的速龙处理器上,几分钟内就可以将3D乳腺MR图像进行流体配准,达到亚体素精度,并且在几十秒内即可获得粗略解。其精度和计算时间与针对该应用验证的FFD技术相当。

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