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CT and MR Image Fusion for CSF Leak Diagnosis

机译:CT和MR图像融合用于CSF漏诊

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The diagnosis of CSF leak using MR images alone is difficult due to the inherently poor bony information on MR images. While CT images show bones exquisitely, they lack the soft tissue contrast that is important for detecting CSF leak. For these reasons, CT cisternography has been the preferred modality for CSF leak diagnosis despite its invasiveness. We propose a method to fuse the CT and MR images to combine the complementary information from each modality, which we believe will help with the diagnosis and surgical planning for patients with CSF leak, and potentially reduce/replace the use of CT cisternography. In the first step, the user identifies three roughly corresponding points on both the CT and MR images. A GUI was designed that allows the user to quickly navigate through the images by reslicing the volumes interactively. After finding the CT and MR slices at approximately the same anatomical position, the user places three markers to represent the same spatial location. In the second step, a generalized Procrustes transform is used to compute an initial transformation that aligns the CT and MR, which is then optimized using mutual information maximization. The CT is registered with the MR using the optimal transformation found, and the bony masks determined from thresholding CT intensity are blended with MR images. Initial results suggest that CT/MR fusion images are superior to unprocessed CT and MR images in diagnosing CSF leak, and a formal clinical evaluation is being planned to assess the efficacy of fusion images.
机译:仅由于MR图像固有的较差的骨信息,很难仅使用MR图像来诊断CSF泄漏。尽管CT图像可以很好地显示骨骼,但它们缺乏对检测CSF泄漏很重要的软组织对比度。由于这些原因,尽管CT CT造影具有侵入性,但它已成为CSF漏诊的首选方式。我们提出了一种融合CT和MR图像的方法,以结合来自每种模态的补充信息,我们相信这将有助于CSF漏诊患者的诊断和手术计划,并有可能减少/替代CT造影。第一步,用户在CT和MR图像上识别三个大致对应的点。设计了一个GUI,允许用户通过交互地分割卷来快速浏览图像。在大致相同的解剖位置找到CT和MR切片后,用户放置三个标记以表示相同的空间位置。在第二步中,使用广义Procrustes变换来计算使CT和MR对齐的初始变换,然后使用互信息最大化对其进行优化。使用找到的最佳变换将CT与MR配准,然后将从阈值CT强度确定的骨质遮罩与MR图像混合。初步结果表明,CT / MR融合图像在诊断CSF泄漏方面优于未处理的CT和MR图像,并且计划进行正式的临床评估以评估融合图像的疗效。

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