Abstract
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).
We acknowledge funding from the EU’s Seventh Framework Program (FP7/2007-2013) under grant agreement no 270186 (FUSIMO) and no 611889 (TRANS-FUSIMO). We thank Prof. S. Kozerke and Dr. J. Schmidt (Institute for Biomedical Engineering, University and ETH, Zurich) for their help with the MRI acquisitions.
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Tanner, C., Boye, D., Samei, G., Székely, G.: Review on 4D models for organ motion compensation. Critical Reviews in Biomedical Engineering 40(2), 135–154 (2012)
McClelland, J.R., Hawkes, D.J., Schaeffter, T., King, A.P.: Respiratory motion models: A review. Medical Image Analysis 17(1), 19–42 (2013)
Von Siebenthal, M., Székely, G., Gamper, U., Boesiger, P., Lomax, A., Cattin, P.: 4D MR imaging of respiratory organ motion and its variability. Phys. Med. Biol. 52, 1547 (2007)
Wachinger, C., Yigitsoy, M., Rijkhorst, E.J., Navab, N.: Manifold learning for image-based breathing gating in ultrasound and MRI. Medical Image Analysis 16(4), 806–818 (2012)
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)
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)
Fransens, R., Strecha, C., Van Gool, L.: Optical flow based super-resolution: A probabilistic approach. Computer Vision and Image Understanding 106(1), 106–115 (2007)
Wu, G., Wang, Q., Lian, J., Shen, D.: Estimating the 4D respiratory lung motion by spatiotemporal registration and building super-resolution image. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 532–539. Springer, Heidelberg (2011)
Hamm, J., Ye, D.H., Verma, R., Davatzikos, C.: Gram: A framework for geodesic registration on anatomical manifolds. Medical Image Analysis 14(5), 633–642 (2010)
Gerber, S., Tasdizen, T., Fletcher, T., Joshi, S., Whitaker, R.: Manifold modeling for brain population analysis. Medical Image Analysis 14(5), 643–653 (2010)
Wu, G., Jia, H., Wang, Q., Shen, D.: Sharpmean: Groupwise registration guided by sharp mean image and tree-based registration. NeuroImage 56(4), 1968–1981 (2011)
Avants, B., Gee, J.C.: Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 23, S139–S150 (2004)
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)
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)
Heinrich, M., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Transactions on Medical Imaging 32(7), 1239–1248 (2013)
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Tanner, C., Samei, G., Székely, G. (2014). Improved Reconstruction of 4D-MR Images by Motion Predictions. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8673. Springer, Cham. https://doi.org/10.1007/978-3-319-10404-1_19
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DOI: https://doi.org/10.1007/978-3-319-10404-1_19
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