Deep MR to CT Synthesis using Unpaired Data
Deep MR to CT Synthesis using Unpaired Data
About this item
Full title
Deep MR to CT Synthesis using Unpaired Data
Author / Creator
Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
arXiv.org, 2017-08
Language
English
Formats
Publication information
Publisher
Ithaca: Cornell University Library, arXiv.org
Subjects
More information
Scope and Contents
Contents
MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis. Current deep learning methods for MR-to-CT synthesis depend on pairwise aligned MR and CT training images of the same patient. However, misalignment between paired images could lead to errors in synthesized CT images. To overcome this, we propose to train a generative adv...
Alternative Titles
Full title
Deep MR to CT Synthesis using Unpaired Data
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2075718595
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2075718595
Other Identifiers
E-ISSN
2331-8422
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