Motion Correction Challenge - Data Motion Correction Challenge - Data

The dataset consists of 10 cases from two centres: the University of Utah and University of Auckland. For each case, a single short axis slice time series at rest and at stress is provided. The Utah datasets were acquired using a saturation-recovery radial turboFLASH sequence at rest and during adenosine infusion (140 μg/kg/min), as described in (DiBella et al. 2012). Contrast was 5 cc/s injection of Multihance (Gd-BOPTA) at 0.02 mmol/kg for the rest and 0.03 mmol/kg for the stress. Four of these subjects have known coronary artery disease. The Auckland cases were acquired using a saturation-recovery Cartesian turboFLASH sequence at rest and during adenosine infusion (140 μg/kg/min). Contrast was 0.04 mmol/kg Omniscan (gadodiamide). None of the Auckland cases have overt coronary disease

Expert-drawn contours only at a reference frame, chosen when contrast is present in both ventricles, will be given to the participants. This will create the same starting point for everyone, and will remove any bias due to the initial contours. No limitations are set, except that the method must be fully automated. We encourage groups to apply both rigid and non-rigid analysis.



Ungated imagesReference mask


This BONUS data set is not mandatory to be processed during this challenge, but it is recommended to try applying your motion correction algorithm on this new rapid ungated MR perfusion technique. More information about this technique can be found in [Harrison et al., 2013].

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Organisers Organisers
  1. Edward DiBella
    (University of Utah, USA)
  2. Alistair Young
    (University of Auckland, NZ)
  3. Nils Noorman
    (TU Eindhoven, NL)
  4. Avan Suinesiaputra
    (University of Auckland, NZ)
  5. Devravat Likhite
    (University of Utah, USA)
  6. James Small
    (University of Auckland, NZ)
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