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一种用于术中超声图像与术前磁共振图像非刚性配准的混合方法。

A hybrid method for non-rigid registration of intra-operative ultrasound images with pre-operative MR images.

作者信息

Farnia P, Ahmadian A, Shabanian T, Serej N D, Alirezaie J

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:5562-5. doi: 10.1109/EMBC.2014.6944887.

Abstract

In recent years intra-operative ultrasound images have been used for many procedures in neurosurgery. The registration of intra-operative ultrasound images with preoperative magnetic resonance images is still a challenging problem. In this study a new hybrid method based on residual complexity is proposed for this problem. A new two stages method based on the matching echogenic structures such as sulci is achieved by optimizing the residual complexity (RC) value with quantized coefficients between the ultrasound image and the probabilistic map of MR image. The proposed method is a compromise between feature-based and intensity-based approaches. The evaluation is performed on both a brain phantom and patient data set. The results of the phantom data set confirmed that the proposed method outperforms the accuracy of conventional RC by 39%. Also the mean of fiducial registration errors reached to 1.45, 1.94 mm when the method was applied on phantom and clinical data set, respectively. This hybrid method based on RC enables non-rigid multimodal image registration in a computational time compatible with clinical use as well as being accurate.

摘要

近年来,术中超声图像已被用于神经外科的许多手术中。术中超声图像与术前磁共振图像的配准仍然是一个具有挑战性的问题。在本研究中,针对该问题提出了一种基于残差复杂度的新型混合方法。通过利用超声图像与磁共振图像概率图之间的量化系数优化残差复杂度(RC)值,实现了一种基于匹配脑沟等回声结构的两阶段新方法。所提出的方法是基于特征和基于强度的方法之间的一种折衷。在脑模型和患者数据集上都进行了评估。模型数据集的结果证实,所提出的方法比传统RC的精度提高了39%。当该方法应用于模型和临床数据集时,基准配准误差的平均值分别达到1.45、1.94毫米。这种基于RC的混合方法能够在与临床应用兼容的计算时间内实现非刚性多模态图像配准,并且精度较高。

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