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Medical Image Imputation From Image Collections

Dalca, Adrian V. and Bouman, Katherine L. and Freeman, William T. and Rost, Natalia S. and Sabuncu, Mert R. and Golland, Polina (2019) Medical Image Imputation From Image Collections. IEEE Transactions on Medical Imaging, 38 (2). pp. 504-514. ISSN 0278-0062. doi:10.1109/tmi.2018.2866692.

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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 data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains. In contrast, we aim to enable the application of existing algorithms that were originally developed for high-resolution research scans to significantly undersampled scans. We introduce a generative model that captures a fine-scale anatomical structure across subjects in clinical image collections and derives an algorithm for filling in the missing data in scans with large inter-slice spacing. Our experimental results demonstrate that the resulting method outperforms the state-of-the-art upsampling super-resolution techniques, and promises to facilitate subsequent analysis not previously possible with scans of this quality. Our implementation is freely available at

Item Type:Article
Related URLs:
URLURL TypeDescription
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:© 2019 IEEE. Manuscript received June 3, 2018; accepted July 8, 2018. Date of publication August 22, 2018; date of current version February 1, 2019. This work was supported in part by NIH NINDS under Grant R01NS086905, in part by NIH NICHD under Grant U01HD087211, in part by NIH NIBIB NAC under Grant P41EB015902, in part by NIH under Grant R41AG052246-01, Grant 1K25EB013649-01, and Grant 1R21AG050122-01, in part by NSF IIS under Grant 1447473, in part by Wistron Corporation, in part by SIP, in part by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) through the National Institutes of Health under Grant U01 AG024904, and in part by the Department of Defense ADNI under Award W81XWH-12-2-0012. The work at ADNI was supported in part by the National Institute on Aging, in part by the National Institute of Biomedical Imaging, in part by Bioengineering, and in part by the generous contributions from several agencies listed at Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database ( As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: ADNI Acknowledgement List.pdf
Funding AgencyGrant Number
National Institute on AgingUNSPECIFIED
National Institute of Biomedical Imaging and BioengineeringUNSPECIFIED
Wistron CorporationUNSPECIFIED
NIHU01 AG024904
Department of DefenseW81XWH-12-2-0012
Alzheimer's Disease Neuroimaging InitiativeUNSPECIFIED
Subject Keywords:Imputation, super-resolution, clinical scans, MRI, sparse slices, thick slices, brain scans
Issue or Number:2
Record Number:CaltechAUTHORS:20190404-161219146
Persistent URL:
Official Citation:A. V. Dalca, K. L. Bouman, W. T. Freeman, N. S. Rost, M. R. Sabuncu and P. Golland, "Medical Image Imputation From Image Collections," in IEEE Transactions on Medical Imaging, vol. 38, no. 2, pp. 504-514, Feb. 2019. doi: 10.1109/TMI.2018.2866692
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94481
Deposited By: George Porter
Deposited On:05 Apr 2019 15:39
Last Modified:16 Nov 2021 17:05

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