Improved Reconstruction of 4D-MR Images by Motion Predictions

  • Christine Tanner
  • Golnoosh Samei
  • Gábor Székely
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


The reconstruction of 4D images from 2D navigator and data slices requires sufficient observations per motion state to avoid blurred images and motion artifacts between slices. Especially images from rare motion states, like deep inhalations during free-breathing, suffer from too few observations.

To address this problem, we propose to actively generate more suitable images instead of only selecting from the available images. The method is based on learning the relationship between navigator and data-slice motion by linear regression after dimensionality reduction. This can then be used to predict new data slices for a given navigator by warping existing data slices by their predicted displacement field. The method was evaluated for 4D-MRIs of the liver under free-breathing, where sliding boundaries pose an additional challenge for image registration.

Leave-one-out tests for five short sequences of ten volunteers showed that the proposed prediction method improved on average the residual mean (95%) motion between the ground truth and predicted data slice from 0.9mm (1.9mm) to 0.8mm (1.6mm) in comparison to the best selection method. The approach was particularly suited for unusual motion states, where the mean error was reduced by 40% (2.2mm vs. 1.3mm).


Ground Truth Dimensionality Reduction Principle Component Analysis Motion State Motion Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Christine Tanner
    • 1
  • Golnoosh Samei
    • 1
  • Gábor Székely
    • 1
  1. 1.Computer Vision LaboratoryETH ZurichZurichSwitzerland

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