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. 2008 Jun 7;53(11):2991-3006.
doi: 10.1088/0031-9155/53/11/016. Epub 2008 May 12.

Motion estimation for cardiac emission tomography by optical flow methods

D R Gilland  1 , B A Mair, J G Parker
Affiliations

Motion estimation for cardiac emission tomography by optical flow methods

D R Gilland et al. Phys Med Biol. .

Abstract

This paper describes a new method for estimating the 3D, non-rigid object motion in a time sequence of images. The method is a generalization of a standard optical flow algorithm that is incorporated into a successive quadratic approximation framework. The method was evaluated for gated cardiac emission tomography using images obtained from a mathematical, 4D phantom and a physical, dynamic phantom. The results showed that the proposed method offers improved motion estimation accuracy relative to the standard optical flow method. Convergence of the proposed algorithm was evidenced with a monotonically decreasing objective function value with iteration. Practical application of the motion estimation method in cardiac emission tomography includes quantitative myocardial motion estimation and 4D, motion-compensated image reconstruction.

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Figures

Figure 1
Figure 1
Example 2D slice images of the 4D NCAT phantom in transaxial and sagittal slice orientations at end-diastole (ED) and end-systole (ES) frames. (a) Noise-free phantom, (b) Set 1 images (without attenuation, scatter and detector response effects) and (c) Set 2 images (with attenuation, scatter and detector response effects).
Figure 2
Figure 2
Transaxial slice of 4D NCAT attenuation distribution for Monte Carlo simulation.
Figure 3
Figure 3
Photograph of beating heart phantom showing myocardial markers. The outer shell of the phantom has been removed.
Figure 4
Figure 4
Reconstructed images of marker activity (top row) and myocardial activity (bottom row) for the physical, beating heart phantom.
Figure 5
Figure 5
One slice of the 4D region-of-interest (left) and the corresponding phantom image (right) used for the PME measurement.
Figure 6
Figure 6
Motion estimation error as a function of the penalty parameter β for the HS method. Left: Set 1 images and right: Set 2 images.
Figure 7
Figure 7
Motion estimation error as a function of the SQ-HS iteration number. Left: noise-free images and right: noisy images. In both plots, the left and right axes refer to Set 1 and Set 2 images, respectively.
Figure 8
Figure 8
Estimated motion vector fields from the Set 1 images superimposed on noise-free phantom images. The results are for a mid-systole frame interval (frame 1 to 2).
Figure 9
Figure 9
Estimated motion vector fields from the Set 2 images superimposed on noise-free images. The results are for a mid-systole frame interval (frame 1 to 2).
Figure 10
Figure 10
Objective function value versus iteration for the HS (left) and SQ-HS (right) methods.
Figure 11
Figure 11
Motion estimation error as a function of β for the physical phantom images. Left: frame 1-2 interval and right: averaged over all frame intervals.
Figure 12
Figure 12
Estimated motion vector fields superimposed on a reconstructed image of the physical phantom data for the frame 1-2 interval. (a) HS method and (b) SQ-HS method.
Figure 13
Figure 13
3D OSEM versus PML using the HS and SQ-HS motion estimates. Top row: frame 1, bottom row: frame 2. Profiles were taken along the middle, horizontal pixel row.
See this image and copyright information in PMC

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