Abstract
Human brain templates are a basis for comparison of brain features across individuals. They should ideally capture anatomical details at both coarse and fine scales to facilitate comparison at varying granularity. Brain template construction typically involves spatial normalization and image fusion. While significant efforts have been dedicated to improving brain templates with sophisticated spatial normalization algorithms, image fusion is typically carried out using intensity-based averaging, causing blurring of anatomical structures. Here, we present an image fusion method that exploits cortical surfaces as guidance to help preserve details in brain templates. Our method encodes cortical boundary information given by a cortical surface mesh in a signed distance function (SDF) map. We use the SDF map to help determine localized contributions of the individual images, especially at cortical boundaries, in image fusion. Experimental results demonstrate that our method significantly improves the preservation of fine gyral and sulcal details, resulting in detailed brain templates with good surface-volume agreement.
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Notes
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An image closest in appearance to all other images.
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Acknowledgments
This work was supported in part by the United States National Institutes of Health (NIH) grants EB008374 and EB006733.
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Ahmad, S., Wu, Y., Yap, PT. (2021). Surface-Guided Image Fusion for Preserving Cortical Details in Human Brain Templates. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_37
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