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Proceedings Paper

CT and MR image fusion for CSF leak diagnosis
Author(s): Yangqiu Hu; David R. Haynor; Kenneth R. Maravilla
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Paper Abstract

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.

Paper Details

Date Published: 17 March 2008
PDF: 7 pages
Proc. SPIE 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling, 69181P (17 March 2008); doi: 10.1117/12.771057
Show Author Affiliations
Yangqiu Hu, Univ. of Washington (United States)
Clario Medical Imaging Inc. (United States)
David R. Haynor, Univ. of Washington (United States)
Kenneth R. Maravilla, Univ. of Washington (United States)


Published in SPIE Proceedings Vol. 6918:
Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling
Michael I. Miga; Kevin Robert Cleary, Editor(s)

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