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Bi-modal Non-rigid Registration of Brain MRI Data Based on Deconvolution of Joint Statistics

机译:基于联合统计反卷积的脑MRI数据双峰非刚性配准

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摘要

Images of different contrasts in MRI can contain complementary information and can highlight different tissue types. Such datasets often need to be co-registered for any further processing. A novel and effective non-rigid registration method based on the restoration of the joint statistics of pairs of such images is proposed. The registration is performed with the deconvolution of the joint statistics and then with the enforcement of the deconvolved statistics back to the spatial domain to form a preliminary registration. The spatial transformation is also regularized with Gaussian spatial smoothing. The registration method has been compared to B-Splines and validated with a simulated Shepp-Logan phantom, with the BrainWeb phantom, and with real datasets. Improved results have been obtained for both accuracy as well as efficiency.
机译:MRI中不同对比度的图像可以包含补充信息,并且可以突出显示不同的组织类型。此类数据集通常需要共同注册才能进行进一步的处理。提出了一种新的有效的基于非刚性图像对的联合统计的配准方法。通过联合统计数据的反卷积执行注册,然后执行反卷积的统计数据返回空间域以形成初步注册。空间变换也可以通过高斯空间平滑进行正则化。该配准方法已与B样条进行了比较,并通过模拟的Shepp-Logan幻像,BrainWeb幻像以及真实数据集进行了验证。在准确性和效率上都获得了改进的结果。

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