
Proceedings Paper
Representation of deformable motion for compression of dynamic cardiac image dataFormat | Member Price | Non-Member Price |
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Paper Abstract
We present a new approach for efficient estimation and storage of tissue deformation in dynamic medical image
data like 3-D+t computed tomography reconstructions of human heart acquisitions. Tissue deformation between
two points in time can be described by means of a displacement vector field indicating for each voxel of a slice,
from which position in the previous slice at a fixed position in the third dimension it has moved to this position.
Our deformation model represents the motion in a compact manner using a down-sampled potential function of
the displacement vector field. This function is obtained by a Gauss-Newton minimization of the estimation error
image, i. e., the difference between the current and the deformed previous slice. For lossless or lossy compression
of volume slices, the potential function and the error image can afterwards be coded separately. By assuming
deformations instead of translational motion, a subsequent coding algorithm using this method will achieve
better compression ratios for medical volume data than with conventional block-based motion compensation
known from video coding. Due to the smooth prediction without block artifacts, particularly whole-image
transforms like wavelet decomposition as well as intra-slice prediction methods can benefit from this approach.
We show that with discrete cosine as well as with Karhunen-Lo`eve transform the method can achieve a better
energy compaction of the error image than block-based motion compensation while reaching approximately the
same prediction error energy.
Paper Details
Date Published: 14 February 2012
PDF: 10 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140E (14 February 2012); doi: 10.1117/12.911276
Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)
PDF: 10 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140E (14 February 2012); doi: 10.1117/12.911276
Show Author Affiliations
Andreas Weinlich, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Siemens AG (Germany)
Peter Amon, Siemens AG (Germany)
Siemens AG (Germany)
Peter Amon, Siemens AG (Germany)
Andreas Hutter, Siemens AG (Germany)
André Kaup, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
André Kaup, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)
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