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Population Based Image Imputation

Dalca, Adrian V. and Bouman, Katherine L. and Freeman, William T. and Rost, Natalia S. and Sabuncu, Mert R. and Golland, Polina (2017) Population Based Image Imputation. In: Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science. No.10265. Springer , Cham, Switzerland, pp. 659-671. ISBN 9783319590493.

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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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription ReadCube access
Dalca, Adrian V.0000-0002-8422-0136
Bouman, Katherine L.0000-0003-0077-4367
Freeman, William T.0000-0002-2231-7995
Sabuncu, Mert R.0000-0002-7068-719X
Additional Information:© Springer International Publishing AG 2017. We acknowledge the following funding sources: NIH NINDS R01NS086905, NIH NICHD U01HD087211, NIH NIBIB NAC P41EB015902, NIH R41AG052246-01, 1K25EB013649-01, 1R21AG050122-01, and Wistron Corporation.
Funding AgencyGrant Number
Wistron CorporationUNSPECIFIED
Series Name:Lecture Notes in Computer Science
Issue or Number:10265
Record Number:CaltechAUTHORS:20190405-160617876
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94521
Deposited By: George Porter
Deposited On:05 Apr 2019 23:16
Last Modified:16 Nov 2021 17:05

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