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        <identifier>oai:researchdata.se:doi-10-23698-aida-megre/0</identifier>
        <datestamp>2020-04-30</datestamp>
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          <dc:identifier>https://doi.org/10.23698/AIDA/MEGRE</dc:identifier>
          <dc:title xml:lang="en">Multi-echo gradient echo (MEGRE)</dc:title>
          <dc:creator>https://orcid.org/0000-0003-2931-5615</dc:creator>
          <dc:subject xml:lang="en">Medical and Health Sciences</dc:subject>
          <dc:subject xml:lang="sv">Medicin och hälsovetenskap</dc:subject>
          <dc:subject xml:lang="en">Radiology and Medical Imaging</dc:subject>
          <dc:subject xml:lang="sv">Radiologi och bildbehandling</dc:subject>
          <dc:subject xml:lang="en">Cancer and Oncology</dc:subject>
          <dc:subject xml:lang="sv">Cancer och onkologi</dc:subject>
          <dc:description xml:lang="en">Multi-echo gradient echo (MEGRE) MRI data with 8 different echo times (2.38-23.6 ms) from 326 + 40 prostate cancer patients with gold fiducial markers inserted into the prostate (train/validation + test dataset). A scientific paper that utilizes this dataset for deep learning has been [published](https://doi.org/10.1088/1361-6560/abb0f9). Underlying description of the technique and its first use has been described in a [previous publication](https://www.ncbi.nlm.nih.gov/pubmed/28803447). The data contains an image volume for each patient, for each echo time. The center of mass from the three inserted prostate gold fiducial markers was manually defined. The ground truth label for this dataset consist of spherical objects with a radius of 1-12 mm, inserted in the center of mass defined locations. Method in the paper uses 9 mm radius.</dc:description>
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          <dc:date>2020-04-30T00:00:00Z</dc:date>
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