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Site-specific deformable imaging registration algorithm selection using patient-based simulated deformations
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10.1118/1.4793723
/content/aapm/journal/medphys/40/4/10.1118/1.4793723
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/40/4/10.1118/1.4793723

Figures

Image of FIG. 1.
FIG. 1.

Validation of computational phantom with a physical phantom, (a) nonradiopaque marker locations before and (b) after plastic insert for bladder filling; (c) CT image of the physical phantom before and (d) after bladder filling; (e) CT image with identified landmarks movement; (f) generated deformed images using ImSimQA, which showed excellent correlation with physical phantom image (d), only slightly different near the edge of bladder and rectum boundary.

Image of FIG. 2.
FIG. 2.

The difference maps between (a) the virtual simulated deformation, (b) free-form deformation by MIM software, and (c) multipass B-Spline by Velocity AI, (d) quantitative analysis result of cumulative registration error to the ground-truth deformation measured from the physical phantom.

Image of FIG. 3.
FIG. 3.

The simulated virtual phantom images for the three clinical cases: (a) prostate, (b) head and neck, (c) cranial spinal irradiation. Each of the images represents only one slice of axial, sagittal, and coronal view of a 3D CT dataset.

Image of FIG. 4.
FIG. 4.

The DVFs from simulated virtual phantom as ground-truth deformation and different image registration algorithms provided by several commercialized software applications. Only one representative slice was shown for each set of image, (a) prostate, (b) head-and-neck, (c) cranial spinal case. VEL-SD: single pass B-spline deformation from VelocityAI; VEL-MD: multipass B-spline deformation from VelocityAI, and MIM: intensity based free-form deformation from MIM software; w/o: without adding simulated noise; w: with adding simulated noise.

Image of FIG. 5.
FIG. 5.

Cumulative error histograms for the (a) prostate, (b) head-and-neck, (c) cranial spinal cases from three commercially available DIR algorithms with (dashed line) and without (solid line) adding simulated CT noise. The overall accuracy was evaluated with all image voxels that resided in the body contour (global analysis), and in a smallest box region that covered tumor target as well as all the identified organs at risk as defined by the physician (regional analysis), respectively.

Image of FIG. 6.
FIG. 6.

The relationship between registration error and magnitude of voxel displacement for the three clinical cases: (a) prostate, (b) head and neck, (c) cranial spinal irradiation. The error-bar showed the standard deviation of voxel displacement. Only the positive error-bar was shown for illustration purpose only.

Tables

Generic image for table
TABLE I.

The average displacements of selected 30 pairs of landmarks that represent the real patient movement for all three cases.

Generic image for table
TABLE II.

Deformation errors of regional voxel-by-voxel analysis.

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/content/aapm/journal/medphys/40/4/10.1118/1.4793723
2013-03-22
2014-04-16
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Site-specific deformable imaging registration algorithm selection using patient-based simulated deformations
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/40/4/10.1118/1.4793723
10.1118/1.4793723
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