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Publication Detail
Bayesian image quality transfer
  • Publication Type:
  • Authors:
    Tanno R, Ghosh A, Grussu F, Kaden E, Criminisi A, Alexander DC
  • Publisher:
    Springer International Publishing
  • Publication date:
  • Pagination:
    265, 273
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    9901 LNCS
  • ISBN-13:
  • Status:
  • Name of conference:
    MICCAI 2016. 19th International Conference
  • Conference place:
    Athens, Greece
  • Conference start date:
  • Conference finish date:
  • Print ISSN:
© Springer International Publishing AG 2016.Image quality transfer (IQT) aims to enhance clinical images of relatively low quality by learning and propagating high-quality structural information from expensive or rare data sets. However,the original framework gives no indication of confidence in its output,which is a significant barrier to adoption in clinical practice and downstream processing. In this article,we present a general Bayesian extension of IQT which enables efficient and accurate quantification of uncertainty,providing users with an essential prediction of the accuracy of enhanced images. We demonstrate the efficacy of the uncertainty quantification through super-resolution of diffusion tensor images of healthy and pathological brains. In addition,the new method displays improved performance over the original IQT and standard interpolation techniques in both reconstruction accuracy and robustness to anomalies in input images.
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