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A neural network based 3D/3D image registration quality evaluator for the head-and-neck patient setup in the absence of a ground truth
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10.1118/1.3502756
/content/aapm/journal/medphys/37/11/10.1118/1.3502756
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/37/11/10.1118/1.3502756

Figures

Image of FIG. 1.
FIG. 1.

The axial and the sagittal views of the FBCT and the CBCT of a head-and-neck patient. The FBCT and the CBCT images are shown in the top and the bottom panels, respectively. The ROI is indicated by the white boxes and the isocenter as the circled cross hairs located in the ROI.

Image of FIG. 2.
FIG. 2.

Definitions of the distinctiveness of the optimum and the mirror symmetry features.

Image of FIG. 3.
FIG. 3.

Schematic plot of the structure of the neural network used as the registration quality evaluator.

Image of FIG. 4.
FIG. 4.

Scatter plots of all the data over two features: DO(1) and DO(5). The networks were constructed using (a) mutual information and the whole image content; (b) mutual information and bony landmarks; (c) mean-squared difference and the whole image content; and (d) mean-squared difference and bony landmarks.

Image of FIG. 5.
FIG. 5.

Same as Fig. 4, except that the data were plotted over two other features: MS(1) and MS(5).

Image of FIG. 6.
FIG. 6.

Cost function (i.e., the negated mutual information computed using the whole image content) profiles probed along the x, y, and z axes from the selected point-of-solutions. The profiles centered on the successful solutions from two different patients are shown in (a) and (b). The profiles centered on the local minimum solutions are shown in (c) and (d).

Image of FIG. 7.
FIG. 7.

An example of the misregistration caused by local minimum trapping. The target FBCT images were shown on the left panels and the registered CBCT images on the right. The top panels give the axial views and the sagittal views show on the bottom.

Tables

Generic image for table
TABLE I.

Range of random disturbances to the initial transformation parameters and the registration success rates when generating training and test data. Abbreviations: information; difference.

Generic image for table
TABLE II.

Definitions of the criteria that were used to evaluate the performance of a classifier. Abbreviations: positive; positive; negative; negative; predictive value; predictive value.

Generic image for table
TABLE III.

Classifier performance when the mutual information and the whole image content were used. Abbreviations: See Table II.

Generic image for table
TABLE IV.

Classifier performance when the mutual information and bony landmarks were used. Abbreviations: See Table II.

Generic image for table
TABLE V.

Classifier performance when the mean-squared difference and the whole image content were used. Abbreviations: See Table II.

Generic image for table
TABLE VI.

Classifier performance when the mean-squared difference and bony landmarks were used. Abbreviations: See Table II.

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/content/aapm/journal/medphys/37/11/10.1118/1.3502756
2010-10-14
2014-04-21
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: A neural network based 3D/3D image registration quality evaluator for the head-and-neck patient setup in the absence of a ground truth
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/37/11/10.1118/1.3502756
10.1118/1.3502756
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