Inferring PET from MRI with pix2pix

Merel M. Jung, Bram van den Berg, Eric Postma, Willem Huijbers

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Abstract

    Medical image-to-image translation, using conditional Gen-erative Adversarial Networks (cGANs), could be beneficial for clinicaldecisions when additional diagnostics scans are requested. The recentlyproposed pix2pix architecture provides an effective image-to-image trans-lation method to study such medical use of cGANs. This study addressesthe question to what extent pix2pix can translate a magnetic resonanceimaging (MRI) scan of a patient into an estimate of a positron emissiontomography (PET) scan of the same patient. We perform two image-to-image translation experiments using paired MRI and PET brain scansof Alzheimer’s disease patients and healthy controls. In experiment 1, wetrain using data sliced in one dimension (the axial plane). In experiment2, we train using augmented data sliced in all three dimensions (axial,sagittal and coronal). After training, the synthetically generated PETscans are compared to the actual ones. The results suggest that PETscans can be sufficiently and reliably estimated from MRI, with similarresults using axial and augmented training. We conclude that image-to-image translation is a promising and potentially cost-saving method formaking informed use of expensive diagnostic technology
    Original languageEnglish
    Title of host publicationInferring PET from MRI with pix2pix
    Publication statusPublished - 2018
    EventBenelux Conference on Artificial Intelligence - Den Bosch, Netherlands
    Duration: 8 Nov 20189 Nov 2018
    Conference number: 30
    https://bnaic2018.nl/

    Conference

    ConferenceBenelux Conference on Artificial Intelligence
    Abbreviated titleBNAIC2018
    Country/TerritoryNetherlands
    CityDen Bosch
    Period8/11/189/11/18
    Internet address

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