Abstract
Advances in neuroimaging have yielded extensive variety in the scale and type of data available. Effective integration of such data promises deeper understanding of anatomy and disease–with consequences for both diagnosis and treatment. Often catered to particular datatypes or scales, current computational tools and mathematical frameworks remain inadequate for simultaneously registering these multiple modes of “images” and statistically analyzing the ensuing menagerie of data. Here, we present (1) a registration algorithm using a “scattering transform” to align high and low resolution images and (2) a varifold-based modeling framework to compute 3D spatial statistics of multiscale data. We use our methods to quantify microscopic tau pathology across macroscopic 3D regions of the medial temporal lobe to address a major challenge in the diagnosis of Alzheimer’s Disease–the reliance on invasive methods to detect microscopic pathology.
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Acknowledgements
This work was supported by the NIH (T32 GM 136577 (KS), U19 AG 033655 (MM), and R01 EB 020062 (MM)), the Kavli Neuroscience Discovery Institute (MM, DT), and the Karen Toffler Charitable Trust (DT). MM owns a founder share of Anatomy Works with the arrangement being managed by Johns Hopkins University in accordance with its conflict of interest policies. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We thank Juan Troncoso, Susumu Mori, and Atsushi Saito at the Johns Hopkins Brain Resource Center for preparation of tissue samples. We thank Menno Witter at the Kavli Institute for Systems Neuroscience and Norwegian University of Science and Technology for his input with MTL segmentations.
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Stouffer, K.M. et al. (2021). From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data. In: Syeda-Mahmood, T., et al. Multimodal Learning for Clinical Decision Support. ML-CDS 2021. Lecture Notes in Computer Science(), vol 13050. Springer, Cham. https://doi.org/10.1007/978-3-030-89847-2_1
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