Motion Correction Challenge - Introduction Motion Correction Challenge - Introduction


Dynamic contrast MR myocardial perfusion imaging has evolved into an accurate technique for the diagnosis of coronary artery disease. T1-weighted images are rapidly acquired every heartbeat to track the uptake and washout of a contrast agent (see the image movies). The diagnosis is based on time-series signal intensity data typically from rest and pharmacological stress images. Quantification of myocardial perfusion can be a useful adjunct to visual analysis, and can be valuable in other contexts. To quantify the time-series data, motion-free data is desired. However, at least 40 seconds of data are typically used to obtain regional perfusion values in the myocardium. Breath-holding becomes a major issue, particularly for patients and during pharmacological stress imaging. The problem is then to handle the inter-frame motion artefact caused by respiration, which makes quantitative analyses difficult. More recently, ungated perfusion acquisition, which uses no ECG triggers, has become a possibility (Harrison et al., 2013). In the ungated case, more motion - from both the cardiac cycle and respiration - must be compensated.

Extensive research has focused on correcting motion artefacts from cardiac MR perfusion images. Several methods have been proposed in the last decade, which can be categorised into two groups: rigid and non-rigid registration techniques. As mentioned in a survey paper of cardiac MR perfusion imaging techniques (Gupta et al., 2012), proponents of each group have their own strong points but also limitations. Rigid registration is computationally more efficient, robust to noise and provides better consistency. Non-rigid registration however provides better alignment if there is cardiac motion due to for example through-plane motion, but it is more susceptible to noise and requires more computation. It is also not clear if images with tissue from out of plane should be used, or if instead the time frame should be discarded. However, all of these methods are still limited in clinical acceptance, and this is due in part to the absence of unbiased algorithmic validation framework using a common multi-centre dataset.

This year, STACOM 2014 is organising a challenge for any research group to develop and/or test motion correction algorithms on a common dataset. You can benchmark your results with other peers at the workshop and discuss common problems. We will write a collation study to compare different methods to correct motion artefacts. Particularly, we are interested to test the hypothesis that there is no significant differences in terms of perfusion values from MR images that have been corrected either by non-rigid or rigid methods. Upon the success of this challenge it is planned to extend the dataset into more thorough study for a journal publication.

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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