Improved Reconstruction of 4D-MR Images by Motion Predictions
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).
KeywordsGround Truth Dimensionality Reduction Principle Component Analysis Motion State Motion Prediction
- 5.Tryggestad, E., Flammang, A., Han-Oh, S., Hales, R., Herman, J., McNutt, T., Roland, T., Shea, S.M., Wong, J.: Respiration-based sorting of dynamic MRI to derive representative 4D-MRI for radiotherapy planning. Medical Physics 40(5), 051909 (2013)Google Scholar
- 6.Baumgartner, C.F., Kolbitsch, C., McClelland, J.R., Rueckert, D., King, A.P.: Groupwise simultaneous manifold alignment for high-resolution dynamic MR imaging of respiratory motion. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 232–243. Springer, Heidelberg (2013)CrossRefGoogle Scholar
- 12.Avants, B., Gee, J.C.: Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 23, S139–S150 (2004)Google Scholar
- 13.Hartkens, T., Rueckert, D., Schnabel, J.A., Hawkes, D.J., Hill, D.L.G.: VTK CISG registration toolkit: An open source software package for affine and non-rigid registration of single-and multimodal 3D images. In: Bildverarbeitung für die Medizin 2002, pp. 409–412 (2002)Google Scholar
- 14.Heinrich, M.P., Jenkinson, M., Brady, S.M., Schnabel, J.A.: Globally optimal deformable registration on a minimum spanning tree using dense displacement sampling. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 115–122. Springer, Heidelberg (2012)CrossRefGoogle Scholar