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Population Based Image Imputation
Name
nihms934456.pdf
Description
Accepted version
Size
800.47 KB
Format
Adobe PDF
Checksum (MD5)
ad3bc8f68897f43fb9416b7cc4bef1da
Author(s)
Dalca, Adrian V.
Bouman, Katherine L.
Freeman, William T.
Rost, Natalia S.
Sabuncu, Mert R.
Golland, Polina
Date Issued
2017
Publisher
Springer Nature
Citation
Dalca, Adrian V., Bouman, Katherine L., Freeman, William T., Rost, Natalia S., Sabuncu, Mert R. et al. 2017. "Population Based Image Imputation."
Version
Author's final manuscript
Abstract
© Springer International Publishing AG 2017. 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1007/978-3-319-59050-9_52
