The goal of this project is to investigate quantitatively the performance of different deformable image registration algorithms (DIR) with helical (HCT), axial (ACT), and cone‐beam CT (CBCT). The variations in the CT‐number values and lengths of well‐known targets moving with controlled motion were evaluated. Four DIR algorithms: Demons, Fast‐Demons, Horn‐Schunck and Lucas‐Kanade were used to register intramodality CT images of a mobile phantom scanned with different imaging techniques. The phantom had three water‐equivalent targets inserted in a low‐density foam with different lengths (10–40 mm) and moved with adjustable motion amplitudes (0–20 mm) and frequencies (0–0.5 Hz). The variations in the CT‐number level, volumes and shapes of these targets were measured from the spread‐out of the CT‐number distributions. In CBCT, most of the DIR algorithms were able to produce the actual lengths of the mobile targets; however, the CT‐number values obtained from the DIR algorithms deviated from the actual CT‐number of the targets. In HCT, the DIR algorithms were successful in deforming the images of the mobile targets to the images of the stationary targets producing the CT‐number values and lengths of the targets for motion amplitudes <20 mm. Similarly in ACT, all DIR algorithms produced the actual CT‐number values and lengths of the stationary targets for low‐motion amplitudes <15 mm. The optical flow‐based DIR algorithms such as the Horn‐Schunck and Lucas‐Kanade performed better than the Demons and Fast‐Demons that are based on attraction forces particularly at large motion amplitudes. In conclusion, most of the DIR algorithms did not reproduce well the CT‐number values and lengths of the targets in images that have artifacts induced by large motion amplitudes. The deviations in the CT‐number values and variations in the volume of the mobile targets in the deformed CT images produced by the different DIR algorithms need to be considered carefully in the treatment planning for accurate dose calculation dose coverage of the tumor, and sparing of critical structures.
Purpose: To investigate correlation of displacement vector fields (DVF) calculated by deformable image registration algorithms with motion parameters in helical axial and cone‐beam CT images with motion artifacts. Methods: A mobile thorax phantom with well‐known targets with different sizes that were made from water‐equivalent material and inserted in foam to simulate lung lesions. The thorax phantom was imaged with helical, axial and cone‐beam CT. The phantom was moved with a cyclic motion with different motion amplitudes and frequencies along the superior‐inferior direction. Different deformable image registration algorithms including demons, fast demons, Horn‐Shunck and iterative‐optical‐flow from the DIRART software were used to deform CT images for the phantom with different motion patterns. The CT images of the mobile phantom were deformed to CT images of the stationary phantom. Results: The values of displacement vectors calculated by deformable image registration algorithm correlated strongly with motion amplitude where large displacement vectors were calculated for CT images with large motion amplitudes. For example, the maximal displacement vectors were nearly equal to the motion amplitudes (5mm, 10mm or 20mm) at interfaces between the mobile targets lung tissue, while the minimal displacement vectors were nearly equal to negative the motion amplitudes. The maximal and minimal displacement vectors matched with edges of the blurred targets along the Z‐axis (motion‐direction), while DVF's were small in the other directions. This indicates that the blurred edges by phantom motion were shifted largely to match with the actual target edge. These shifts were nearly equal to the motion amplitude. Conclusions: The DVF from deformable‐image registration algorithms correlated well with motion amplitude of well‐defined mobile targets. This can be used to extract motion parameters such as amplitude. However, as motion amplitudes increased, image artifacts increased significantly and that limited image quality and poor correlation between the motion amplitude and DVF was obtained.
Purpose: To evaluate quantitatively the performance of different deformable image registration algorithms using a mobile thorax phantom with well‐defined targets and controlled motion‐patterns imaged with helical, axial and cone‐beam CT. Materials and Methods: A thorax phantom was scanned using helical, axial and cone‐beam CT imaging while it is moving and stationary. The phantom system was moved using cyclic motion with adjustable motion amplitude (0–40 mm) and frequency (0.125–0.5Hz). Several water equivalent targets (small, medium and large) with known sizes were inserted in the middle of low‐density foam. The CT images of the mobile phantom were registered with the images of the stationary phantom with different deformable image registration algorithms using the DIRART software. The performance of different deformable image registration algorithms were evaluated in terms of reducing image artifact and reproducing the shape, size and CT‐number level of well‐defined targets. Results: The image artifacts induced by phantom motion which included deformation and spread‐out of the targets increased with motion amplitude. The deformable image registration improved image‐quality from artifacts induced by motion in the deformed CT images. The CT‐number profiles showed that the deformable image registration algorithms were able to reproduce the shape and size of the mobile targets where the spread‐out artifacts were eliminated. However, the CT‐numbers deviated from the corresponding values for the stationary targets. The discrepancy between the CT‐number level from the deformed CT images and that from the stationary CT images increased with motion amplitude. Conclusion: The performance of the deformable image registration depends on several parameters that include: motion artifacts, deformable image registration algorithm, and imaging modes. The deformable image registration algorithms reproduced size and shape of mobile targets with different motion‐patterns. However, they did not reproduce the CT‐number value which deviated strongly from the actual value of the stationary CT images with increasing motion amplitude.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.