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Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach
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Image of FIG. 1.
FIG. 1.

Flowchart illustrating the incorporation of iterative intensity matching of CT-CBCT voxel values within the Demons algorithm. The light gray parts of the flowchart illustrate nominal multiresolution registration with convergence criterion. The black parts of the flowchart illustrate the tissue-specific intensity match performed in each iteration.

Image of FIG. 2.
FIG. 2.

Illustration of datasets. Each column represents a case ranging from minor anatomical deformation (case 1) to major deformation and tissue change (case 6). The first row illustrates the moving image (preoperative CT) and the second the fixed image (intraoperative CBCT). The third row illustrates the image overlap and field of view for each modality and the fourth row shows the difference image following rigid registration (overlaid on CT).

Image of FIG. 3.
FIG. 3.

CT-CBCT registration in the presence of intensity errors. (a) Intraoperative CBCT image showing the true (undistorted) anatomy of the fixed image. The inset shows a zoomed-in view of the cervical vertebrae. [(b)–(d)] Preoperative CT images following Demons deformable registration to (a) using (b) no intensity correction, (c) a single histogram match, and (d) an iterative intensity match. Note the tissue distortions in (b) and (c) in comparison to (a), whereas (d) appears robust against distortion. (e) NCC calculated within the inset region for the NIM, SHM, and IIM approaches.

Image of FIG. 4.
FIG. 4.

Effect of intensity variation on registration accuracy. (a) TRE and for the NIM, SHM, and IIM approaches with simulated uniform intensity errors. Both SHM and IIM provide robustness against intensity errors, but the latter better resolves distortions that can occur due to intensity mismatches. (b) TRE for the IIM approach with both uniform and spatially parabolic intensity errors showing similar results.

Image of FIG. 5.
FIG. 5.

Evolution of intensity rescale parameters computed at each iteration of the IIM approach. Four tissue classes are labeled. Levels 1–4 refer to the morphological pyramid of Fig. 1, with downsampling factors of 8, 4, 2, and 1.

Image of FIG. 6.
FIG. 6.

Evaluation of rank correlation coefficient per iteration of IIM Demons registration and comparison to B-spline parametrized normalized mutual information based registration. The results show (a) registration convergence behavior starting from (b) rigid registration and (d) final product in comparison to (c) a simple B-spline parametrized normalized mutual information registration. All images show a central sagittal slice of intraoperative CBCT in grayscale with yellow-green differences from the registered preoperative CT overlaid.

Image of FIG. 7.
FIG. 7.

Registration accuracy measured across all anatomical target points and datasets. Results are grouped according to simple deformation (cases 1–3) compared to “complex” deformation and tissue change (cases 4–6) and the final group pools all datasets. Box-and-whisker plots represent the median TRE (horizontal centerline), the 25th and 75th percentiles (box edges), and total range (whiskers) excluding outliers .


Generic image for table

Summary of cases studied and registration performance for CT-CBCT registration using iterative intensity matching; “simple” cases at the top and more complex cases at the bottom.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach