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Novel image registration quality evaluator (RQE) with an implementation for automated patient positioning in cranial radiation therapy
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10.1118/1.2736783
/content/aapm/journal/medphys/34/6/10.1118/1.2736783
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/34/6/10.1118/1.2736783

Figures

Image of FIG. 1.
FIG. 1.

Flow chart for the RQE construction.

Image of FIG. 2.
FIG. 2.

(a) Original reference portal EPID image used for the intramodal RQE construction; (b) original target portal EPID image used for both the intramodal and the intermodal RQE constructions; (c) region of interest (ROI) image for image (a); (d) reference MVDRR used for the intermodal RQE construction; (e) conventional (i.e., kilovoltage energy based) DRR used for fiducial marker localization; (f) ROI image for image (d).

Image of FIG. 3.
FIG. 3.

ROC curves calculated from the training datasets for different similarity measures: (a) the intramodal RQE; (b) the intermodal RQE. In both plots, MI and NMI curves overlap each other completely within the scale of the display resolution.

Image of FIG. 4.
FIG. 4.

Areas under ROC curves (AUC) calculated from the training datasets for different similarity measures: (a) the intramodal RQE; (b) the intermodal RQE.

Image of FIG. 5.
FIG. 5.

Cost as a function of cutoff value (negative MI) using the training datasets: (a) the intramodal RQE; (b) the intermodal RQE.

Image of FIG. 6.
FIG. 6.

Selection of cutoff value for RQEs: (a) the intramodal RQE; (b) the intermodal RQE. The cutoff point is calculated by optimizing the cost function for the respective training dataset. The registration count is the frequency of occurrence of a registration result for a given mutual information value.

Image of FIG. 7.
FIG. 7.

Performance of the RQE with the presence of out-of-plane deviations (AP view): (a) Sensitivity; (b) Specificity; (c) NPV; and (d) PPV.

Image of FIG. 8.
FIG. 8.

Performance of the RQE with the presence of out-of-plane deviations (lateral view): (a) Sensitivity; (b) Specificity; (c) NPV; and (d) PPV.

Tables

Generic image for table
TABLE I.

Performance of intramodal RQE. Test dataset 1 consists of registrations based on EPID images with actual shifts (number of ). Test dataset 2 consists of registrations using EPID images with computer-generated shifts (number of ). Test dataset 3 is identical to test dataset 2 except for the addition of Gaussian noise ( of image pixel intensity of x-ray beam passing through a of solid water) to the target EPID image (number of ).

Generic image for table
TABLE II.

Performance of the intermodal RQE. Test dataset 1 consists of registrations based on DRR-EPID images with actual shifts (number of ). Test dataset 2 consists of registrations between target EPID images with computer-generated shifts and a reference DRR (number of ). Test dataset 3 is identical to test dataset 2 except for the addition of Gaussian noise ( of image pixel intensity of x-ray beam passing through a of solid water) to the target EPID images (number of ).

Generic image for table
TABLE III.

Performance of the intermodal RQE using clinic data. Each patient image set consists of a reference DRR and portal images acquired weekly using a video camera-based EPID during of treatment. For each portal image, 50 registrations with different initial transform parameters were carried out in aligning the portal image with its reference DRR to generate test data for their corresponding RQEs.

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/content/aapm/journal/medphys/34/6/10.1118/1.2736783
2007-05-18
2014-04-17
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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Novel image registration quality evaluator (RQE) with an implementation for automated patient positioning in cranial radiation therapy
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/34/6/10.1118/1.2736783
10.1118/1.2736783
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