International Journal on Magnetic Particle Imaging IJMPI
Vol. 5 No. 1-2 (2019): Int J Mag Part Imag
https://doi.org/10.18416/IJMPI.2019.1908001
Temporal Polyrigid Registration for Patch-based MPI Reconstruction of Moving Objects
Main Article Content
Copyright (c) 2019 Jan Ehrhardt, Mandy Ahlborg, Hristina Uzunova, Thorsten M. Buzug, Heinz Handels
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
In Magnetic Particle Imaging, the size of the field of view can be increased with static focus fields resulting in imaging patches. Patches are acquired successively and combined during or after image reconstruction. However, the occurrence of motion may result in artifacts in the reconstructed images. In this contribution, a temporal polyrigid registration is proposed to combine reconstructed MPI patches by predicting a possible object motion. The experiments use different two-dimensional simulated MPI acquisition scenarios. It is shown that our approach reduces motion artifacts in dependence of the used patch overlaps successfully.
Int. J. Mag. Part. Imag. 5(1-2), 2019, Article ID: 1908001, DOI: 10.18416/IJMPI.2019.1908001
Article Details
References
[1] B. Gleich and J. Weizenecker. Tomographic imaging using the nonlinear response of magnetic particles. Nature, 435(7046):1214–1217, 2005, doi:10.1038/nature03808.
[2] A. C. Bakenecker, M. Ahlborg, C. Debbeler, C. Kaethner, T. M. Buzug, and K. Lüdtke-Buzug.Magnetic particle imaging in vascular medicine. Innovative Surgical Sciences, 3(3):179–192, 2018, doi:10.1515/iss-2018-2026.
[3] E. U. Saritas, P. W. Goodwill, G. Z. Zhang, and S. M. Conolly. Magnetostimulation Limits in Magnetic Particle Imaging. IEEE Transactions on Medical Imaging, 32(9):1600–1610, 2013, doi:10.1109/TMI.2013.2260764.
[4] J. Rahmer, B. Gleich, C. Bontus, I. Schmale, J. D. Schmidt, J. Kanzenbach, O. Woywode, J. Weizenecker, and J. Borgert, Results on Rapid 3D Magnetic Particle Imaging with a Large Field of View, in International Society for Magnetic Resonance in Medicine 19, 629, 2011.
[5] P. Vogel, T. Kampf, M. A. Rückert, and V. C. Behr. Flexible and Dynamic Patch Reconstruction for Traveling Wave Magnetic Particle Imaging. International Journal on Magnetic Particle Imaging, 2(2), 2016, doi:10.18416/IJMPI.2016.1611001.
[6] M. Ahlborg, C. Kaethner, T. Knopp, P. Szwargulski, and T. M. Buzug. Using data redundancy gained by patch overlaps to reduce truncation artifacts in magnetic particle imaging. Physics in Medicine and Biology, 61(12):4583–4598, 2016, doi:10.1088/0031-9155/61/12/4583.
[7] C. Jung, J. Salamon, P. Szwargulski, N. Gdaniec, M.Hofmann, M. G. Kaul, G. Adam, S. J. Kemp, M. Ferguson, A. P. Khandhar, K. M. Krishnan, T. Knopp, and H. Ittrich, Using a Long Circulating Blood Pool Tracer to Perform Multi-patch MPI for Whole Body Imaging of a Mice, in Radiological Society of North America, 2016.
[8] R. Szeliski. Image Alignment and Stitching: A Tutorial. Foundations and Trends (R) in Computer Graphics and Vision, 2(1):1–104, 2006, doi:10.1561/0600000009.
[9] T. Vercauteren, A. Perchant, G.Malandain, X. Pennec, and N. Ayache. Robust mosaicing with correction of motion distortions and tissue deformations for in vivo fibered microscopy. Medical Image Analysis, 10(5):673–692, 2006, doi:10.1016/j.media.2006.06.006.
[10] T. Pan, Helical 4D CT and Comparison with Cine 4D CT, in 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy, J. Ehrhardt and C. Lorenz, Eds., 2013, 25–41. doi:10.1007/978-3-642-36441-9_2.
[11] N. Gdaniec, M. Schluter, M. Moddel, M. G. Kaul, K. M. Krishnan, A. Schlaefer, and T. Knopp. Detection and Compensation of Periodic Motion in Magnetic Particle Imaging. IEEE Transactions on Medical Imaging, 36(7):1511–1521, 2017, doi:10.1109/TMI.2017.2666740.
[12] N. Gdaniec, P. Szwargulski, M.Möddel, M. Boberg, and T. Knopp, Multi–patch magnetic particle imaging of a phantom with periodic motion, in International Workshop on Magnetic Particle Imaging, 27–28, 2019.
[13] J. Ehrhardt, R.Werner, D. Säring, T. Frenzel,W. Lu, D. Low, and H. Handels. An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing. Medical Physics, 34(2):711–721, 2007, doi:10.1118/1.2431245.
[14] M. Usman, D. Atkinson, F. Odille, C. Kolbitsch, G. Vaillant, T. Schaeffter, P. G. Batchelor, and C. Prieto. Motion corrected compressed sensing for free-breathing dynamic cardiac MRI. Magnetic Resonance in Medicine, 70(2):504–516, 2013, doi:10.1002/mrm.24463.
[15] C. Forman, D. Piccini, R. Grimm, J. Hutter, J. Hornegger, and M. O. Zenge. Reduction of respiratory motion artifacts for free breathing whole-heart coronary MRA by weighted iterative reconstruction. Magnetic Resonance in Medicine, 73(5):1885–1895, 2015, doi:10.1002/mrm.25321.
[16] V. Arsigny, O. Commowick, N. Ayache, and X. Pennec. A Fast and Log-Euclidean Polyaffine Framework for Locally Linear Registration. Journal of Mathematical Imaging and Vision, 33(2):222–238, 2009, doi:10.1007/s10851-008-0135-9.
[17] C. Seiler, X. Pennec, and M. Reyes, Simultaneous Multiscale Polyaffine Registration by Incorporating Deformation Statistics, in Medical Image Computing and Computer-Assisted Intervention, 130–137, 2012. doi:10.1007/978-3-642-33418-4_17.
[18] P. Cachier, E. Bardinet, D. Dormont, X. Pennec, and N. Ayache. Iconic feature based nonrigid registration: the PASHA algorithm. Computer Vision and Image Understanding, 89(2-3):272–298, 2003, doi:10.1016/S1077-3142(03)00002-X.
[19] J. Ehrhardt, M. Ahlborg, H. Uzunova, T. M. Buzug, and H. Handels, Temporal polyrigid registration for patch-based MPI reconstruction of moving objects, in International Workshop on Magnetic Particle Imaging, 55–56, 2018.
[20] K. McLeod, C. Seiler, M. Sermesant, and X. Pennec, A Near-Incompressible Poly-affine Motion Model for Cardiac Function Analysis, in Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges, 2013, 288–297. doi:10.1007/978-3-642-36961-2_33.
[21] C. Seiler, X. Pennec, and M. Reyes. Capturing the multiscale anatomical shape variability with polyaffine transformation trees. Medical Image Analysis, 16(7):1371–1384, 2012, doi:10.1016/j.media.2012.05.011.
[22] V. Arsigny, X. Pennec, and N. Ayache. Polyrigid and polyaffine transformations: A novel geometrical tool to deal with nonrigid deformations – Application to the registration of histological slices. Medical Image Analysis, 9(6):507–523, 2005, doi:10.1016/j.media.2005.04.001.
[23] L. Le Folgoc, H. Delingette, A. Criminisi, and N. Ayache. Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements. Medical Image Analysis, 36:79–97, 2017, doi:10.1016/j.media.2016.09.008.
[24] J. Ehrhardt and C. Lorenz, Eds., 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy, ser. Biological and Medical Physics, Biomedical Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, doi:10.1007/978-3-642-36441-9.
[2] A. C. Bakenecker, M. Ahlborg, C. Debbeler, C. Kaethner, T. M. Buzug, and K. Lüdtke-Buzug.Magnetic particle imaging in vascular medicine. Innovative Surgical Sciences, 3(3):179–192, 2018, doi:10.1515/iss-2018-2026.
[3] E. U. Saritas, P. W. Goodwill, G. Z. Zhang, and S. M. Conolly. Magnetostimulation Limits in Magnetic Particle Imaging. IEEE Transactions on Medical Imaging, 32(9):1600–1610, 2013, doi:10.1109/TMI.2013.2260764.
[4] J. Rahmer, B. Gleich, C. Bontus, I. Schmale, J. D. Schmidt, J. Kanzenbach, O. Woywode, J. Weizenecker, and J. Borgert, Results on Rapid 3D Magnetic Particle Imaging with a Large Field of View, in International Society for Magnetic Resonance in Medicine 19, 629, 2011.
[5] P. Vogel, T. Kampf, M. A. Rückert, and V. C. Behr. Flexible and Dynamic Patch Reconstruction for Traveling Wave Magnetic Particle Imaging. International Journal on Magnetic Particle Imaging, 2(2), 2016, doi:10.18416/IJMPI.2016.1611001.
[6] M. Ahlborg, C. Kaethner, T. Knopp, P. Szwargulski, and T. M. Buzug. Using data redundancy gained by patch overlaps to reduce truncation artifacts in magnetic particle imaging. Physics in Medicine and Biology, 61(12):4583–4598, 2016, doi:10.1088/0031-9155/61/12/4583.
[7] C. Jung, J. Salamon, P. Szwargulski, N. Gdaniec, M.Hofmann, M. G. Kaul, G. Adam, S. J. Kemp, M. Ferguson, A. P. Khandhar, K. M. Krishnan, T. Knopp, and H. Ittrich, Using a Long Circulating Blood Pool Tracer to Perform Multi-patch MPI for Whole Body Imaging of a Mice, in Radiological Society of North America, 2016.
[8] R. Szeliski. Image Alignment and Stitching: A Tutorial. Foundations and Trends (R) in Computer Graphics and Vision, 2(1):1–104, 2006, doi:10.1561/0600000009.
[9] T. Vercauteren, A. Perchant, G.Malandain, X. Pennec, and N. Ayache. Robust mosaicing with correction of motion distortions and tissue deformations for in vivo fibered microscopy. Medical Image Analysis, 10(5):673–692, 2006, doi:10.1016/j.media.2006.06.006.
[10] T. Pan, Helical 4D CT and Comparison with Cine 4D CT, in 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy, J. Ehrhardt and C. Lorenz, Eds., 2013, 25–41. doi:10.1007/978-3-642-36441-9_2.
[11] N. Gdaniec, M. Schluter, M. Moddel, M. G. Kaul, K. M. Krishnan, A. Schlaefer, and T. Knopp. Detection and Compensation of Periodic Motion in Magnetic Particle Imaging. IEEE Transactions on Medical Imaging, 36(7):1511–1521, 2017, doi:10.1109/TMI.2017.2666740.
[12] N. Gdaniec, P. Szwargulski, M.Möddel, M. Boberg, and T. Knopp, Multi–patch magnetic particle imaging of a phantom with periodic motion, in International Workshop on Magnetic Particle Imaging, 27–28, 2019.
[13] J. Ehrhardt, R.Werner, D. Säring, T. Frenzel,W. Lu, D. Low, and H. Handels. An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing. Medical Physics, 34(2):711–721, 2007, doi:10.1118/1.2431245.
[14] M. Usman, D. Atkinson, F. Odille, C. Kolbitsch, G. Vaillant, T. Schaeffter, P. G. Batchelor, and C. Prieto. Motion corrected compressed sensing for free-breathing dynamic cardiac MRI. Magnetic Resonance in Medicine, 70(2):504–516, 2013, doi:10.1002/mrm.24463.
[15] C. Forman, D. Piccini, R. Grimm, J. Hutter, J. Hornegger, and M. O. Zenge. Reduction of respiratory motion artifacts for free breathing whole-heart coronary MRA by weighted iterative reconstruction. Magnetic Resonance in Medicine, 73(5):1885–1895, 2015, doi:10.1002/mrm.25321.
[16] V. Arsigny, O. Commowick, N. Ayache, and X. Pennec. A Fast and Log-Euclidean Polyaffine Framework for Locally Linear Registration. Journal of Mathematical Imaging and Vision, 33(2):222–238, 2009, doi:10.1007/s10851-008-0135-9.
[17] C. Seiler, X. Pennec, and M. Reyes, Simultaneous Multiscale Polyaffine Registration by Incorporating Deformation Statistics, in Medical Image Computing and Computer-Assisted Intervention, 130–137, 2012. doi:10.1007/978-3-642-33418-4_17.
[18] P. Cachier, E. Bardinet, D. Dormont, X. Pennec, and N. Ayache. Iconic feature based nonrigid registration: the PASHA algorithm. Computer Vision and Image Understanding, 89(2-3):272–298, 2003, doi:10.1016/S1077-3142(03)00002-X.
[19] J. Ehrhardt, M. Ahlborg, H. Uzunova, T. M. Buzug, and H. Handels, Temporal polyrigid registration for patch-based MPI reconstruction of moving objects, in International Workshop on Magnetic Particle Imaging, 55–56, 2018.
[20] K. McLeod, C. Seiler, M. Sermesant, and X. Pennec, A Near-Incompressible Poly-affine Motion Model for Cardiac Function Analysis, in Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges, 2013, 288–297. doi:10.1007/978-3-642-36961-2_33.
[21] C. Seiler, X. Pennec, and M. Reyes. Capturing the multiscale anatomical shape variability with polyaffine transformation trees. Medical Image Analysis, 16(7):1371–1384, 2012, doi:10.1016/j.media.2012.05.011.
[22] V. Arsigny, X. Pennec, and N. Ayache. Polyrigid and polyaffine transformations: A novel geometrical tool to deal with nonrigid deformations – Application to the registration of histological slices. Medical Image Analysis, 9(6):507–523, 2005, doi:10.1016/j.media.2005.04.001.
[23] L. Le Folgoc, H. Delingette, A. Criminisi, and N. Ayache. Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements. Medical Image Analysis, 36:79–97, 2017, doi:10.1016/j.media.2016.09.008.
[24] J. Ehrhardt and C. Lorenz, Eds., 4D Modeling and Estimation of Respiratory Motion for Radiation Therapy, ser. Biological and Medical Physics, Biomedical Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, doi:10.1007/978-3-642-36441-9.