Efficient dense non-rigid registration using the free-form deformation framework
Efficient dense non-rigid registration using the free-form deformation framework
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发表时间：
2012-02
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通讯作者：
M. Modat
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作者：
M. Modat
医学图像配准包括查找两个或多个图像之间的空间对应关系。它是一种强大的工具，常用于各种医学图像处理任务。尽管医学图像配准在过去二十年中一直是一个活跃的研究主题，但该领域的重大挑战仍有待解决。本论文通过扩展自由形式变形（FFD）配准框架来解决其中的一些挑战，该框架是使用最广泛和最完善的非刚性配准算法之一。由于非刚性变换的高自由度，医学图像配准是一项计算成本高昂的任务。在这项工作中，FFD 算法已被重构，以实现快速处理，同时保持结果的准确性。此外，还采用并行计算范例来提供近乎实时的图像配准功能。已进行进一步的修改以提高对组织不均匀性等伪影的配准鲁棒性。通过使用基于正则化的生物力学模型，提高了生成变形场的合理性。此外，还开发了该算法的微分同胚扩展。本论文中提出的工作已通过对诊断为痴呆症的患者或接受脑切除患者的脑磁共振成像进行了广泛验证。它还应用于肺部X射线计算机断层扫描和小动物成像。除了本论文之外，还开发了一个开源包 NiftyReg，以向医学成像社区发布所展示的工作。
Medical image registration consists of finding spatial correspondences between two images or more. It is a powerful tool which is commonly used in various medical image processing tasks. Even though medical image registration has been an active topic of research for the last two decades, significant challenges in the field remain to be solved. This thesis addresses some of these challenges through extensions to the Free-Form Deformation (FFD) registration framework, which is one of the most widely used and well-established non-rigid registration algorithm. Medical image registration is a computationally expensive task because of the high degrees of freedom of the non-rigid transformations. In this work, the FFD algorithm has been re-factored to enable fast processing, while maintaining the accuracy of the results. In addition, parallel computing paradigms have been employed to provide near real-time image registration capabilities. Further modifications have been performed to improve the registration robustness to artifacts such as tissues non-uniformity. The plausibility of the generated deformation field has been improved through the use of bio-mechanical models based regularization. Additionally, diffeomorphic extensions to the algorithm were also developed. The work presented in this thesis has been extensively validated using brain magnetic resonance imaging of patients diagnosed with dementia or patients undergoing brain resection. It has also been applied to lung X-ray computed tomography and imaging of small animals. Alongside with this thesis an open-source package, NiftyReg, has been developed to release the presented work to the medical imaging community.