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Inf Process Med Imaging. 2017 Jun;10265:659-671. doi: 10.1007/978-3-319-59050-9_52. Epub 2017 May 23.

Population Based Image Imputation.

Author information

1
Computer Science and Artificial Intelligence Lab, MIT, Cambridge, USA.
2
Martinos Center for Biomedical Imaging, Massachusetts General Hospital, HMS, Charlestown, MA, USA.
3
Google Research, Cambridge, MA, USA.
4
Department of Neurology, Massachusetts General Hospital, HMS, Boston, USA.
5
School of Electrical and Computer Engineering, Cornell, Ithaca, USA.

Abstract

We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large databases of clinical images contain a wealth of information, medical acquisition constraints result in sparse scans that miss much of the anatomy. These characteristics often render computational analysis impractical as standard processing algorithms tend to fail when applied to such images. Highly specialized or application-specific algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains. In contrast, our goal is to enable application of existing algorithms that were originally developed for high resolution research scans to significantly undersampled scans. We introduce a model that captures fine-scale anatomical similarity across subjects in clinical image collections and use it to fill in the missing data in scans with large slice spacing. Our experimental results demonstrate that the proposed method outperforms current upsampling methods and promises to facilitate subsequent analysis not previously possible with scans of this quality.

PMID:
29379264
PMCID:
PMC5786165
DOI:
10.1007/978-3-319-59050-9_52
[Indexed for MEDLINE]
Free PMC Article

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