1887
banner image
No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
Improving best-phase image quality in cardiac CT by motion correction with MAM optimization
Rent:
Rent this article for
USD
10.1118/1.4789486
/content/aapm/journal/medphys/40/3/10.1118/1.4789486
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/40/3/10.1118/1.4789486

Figures

Image of FIG. 1.
FIG. 1.

Flow chart of the proposed algorithm for motion estimation and compensation by MAM-optimization.

Image of FIG. 2.
FIG. 2.

Example for the entropy MAM and the positivity MAM computed in a region of interest of a coronary artery CT image at different reconstruction phases, see the two images on the left side. Both MAMs have low values at reconstruction phases with good image quality and little motion artifacts (see, the example, image at 70% with a well-depicted cross section of a coronary artery). They have high values at reconstruction phases with significant motion artifacts (see, the example, image at 79% with a blurred coronary artery).

Image of FIG. 3.
FIG. 3.

Results of the simulation experiment row-wise for different heart phases. The left-column shows the ground truth template image. A missing result due to insufficient data for motion estimation is indicated by X. All results images were extracted from the full image at the position of the maximum NCC. Window width/center: 800/200 HU.

Image of FIG. 4.
FIG. 4.

Results of the numerical simulation experiment for the NCC quality measure. Higher numbers in (a) indicate better quality. (a) Image quality of static and motion compensated reconstruction algorithms. (b) Relative quality improvement to the best-phase in image data used for motion estimation. (c) Ground truth motion profile and registration result in the static images.

Image of FIG. 5.
FIG. 5.

Details of the simulated CT data acquisition for a heart rate of 70 bpm and a rotation time of 300 ms. The minimum data range needed for image reconstruction at the isocenter can be acquired in 150 ms.

Image of FIG. 6.
FIG. 6.

Example for the complexity of motion artifacts. The same constant linear motion can lead to a large variety in the degree of motion artifacts depending on the motion direction and start angle of the reconstruction.

Image of FIG. 7.
FIG. 7.

Results of the numerical simulation experiment for the RMSD quality measure. Lower numbers in (a) indicate better quality. (a) Image quality of static and motion compensated reconstruction algorithms. (b) Relative quality improvement to the best-phase in image data used for motion estimation. (c) Ground truth motion profile and registration result in the static images.

Image of FIG. 8.
FIG. 8.

Convergence analysis of the MAM entropy optimization algorithm for the results of the numerical simulation at different heart phases.

Image of FIG. 9.
FIG. 9.

Results for clinical case 1. See Table I for details on the acquisition and reconstruction.

Image of FIG. 10.
FIG. 10.

Results for clinical case 2. See Table I for details on the acquisition and reconstruction.

Image of FIG. 11.
FIG. 11.

Results for clinical case 3. See Table I for details on the acquisition and reconstruction.

Image of FIG. 12.
FIG. 12.

Results for clinical case 4. See Table I for details on the acquisition and reconstruction.

Tables

Generic image for table
TABLE I.

Overview of the studied clinical cases.

Loading

Article metrics loading...

/content/aapm/journal/medphys/40/3/10.1118/1.4789486
2013-02-08
2014-04-17
Loading

Full text loading...

This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Improving best-phase image quality in cardiac CT by motion correction with MAM optimization
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/40/3/10.1118/1.4789486
10.1118/1.4789486
SEARCH_EXPAND_ITEM