Original Demons Algorithm Research Articles

Tissue-specific deformable image registration using a spatial-contextual filter.

Intensity-based deformable registration with spatial-invariant regularization generally fails when distinct motion exists across different types of tissues. The purpose of this work was to develop and validate a new regularization approach for deformable image registration that is tissue-specific and able to handle motion discontinuities. Our approach was built upon a Demons registration framework, and used the image context supplementing the original spatial constraint to regularize displacement vector fields in iterative image registration process. The new regularization was implemented as a spatial-contextual filter, which favors the motion vectors within the same tissue type but penalizes the motion vectors from different tissues. This approach was validated using five public lung cancer patients, each with 300 landmark pairs identified by a thoracic radiation oncologist. The mean and standard deviation of the landmark registration errors were 1.3 ± 0.8 mm, compared with those of 2.3 ± 2.9 mm using the original Demons algorithm. Particularly, for the case with the largest initial landmark displacement of 15 ± 9 mm, the modified Demons algorithm had a registration error of 1.3 ± 1.1 mm, while the original Demons algorithm had a registration error of 3.6 ± 5.9 mm. We also qualitatively evaluated the modified Demons algorithm using two difficult cases in our routine clinic: one lung case with large sliding motion and one head and neck case with large anatomical changes in air cavity. Visual evaluation on the deformed image created by the deformable image registration showed that the modified Demons algorithm achieved reasonable registration accuracy, but the original Demons algorithm produced distinct registration errors.

VLSI-Assisted Nonrigid Registration Using Modified Demons Algorithm

Increasing demand of high-speed portable modules for multimedia applications has motivated the development of hardware-based solutions for image processing applications. Most of the nonrigid image registration algorithms are found to be unsuitable for hardware implementation because of their nonlinearity and computationally intensive nature. In this paper, an algorithm for nonrigid image registration based on Demons approximation is proposed. The algorithm has been simulated in MATLAB and results show a 15% improvement in peak-signal-to-noise-ratio with a 17% reduction in registration time for $256 \times 256$ image over the original Demons algorithm. The proposed algorithm is synthesized in Virtex6-xc6vlx760-2-ff1760 and maximum synthesized frequency is found to be 174 MHz. The proposed architecture provides the low cost, high-speed solution for the registration process, which is also helpful for making a portable system.

WE‐D‐9A‐02: Automated Landmark‐Guided CT to Cone‐Beam CT Deformable Image Registration

Purpose:The anatomical changes that occur between the simulation CT and daily cone‐beam CT (CBCT) are investigated using an automated landmark‐guided deformable image registration (LDIR) algorithm with simultaneous intensity correction. LDIR was designed to be accurate in the presence of tissue intensity mismatch and heavy noise contamination.Method:An auto‐landmark generation algorithm was used in conjunction with a local small volume (LSV) gradient matching search engine to map corresponding landmarks between the CBCT and planning CT. The LSVs offsets were used to perform an initial deformation, generate landmarks, and correct local intensity mismatch. The landmarks act as stabilizing controlpoints in the Demons objective function. The accuracy of the LDIR algorithm was evaluated on one synthetic case with ground truth and data of ten head and neck cancer patients. The deformation vector field (DVF) accuracy was accessed using a synthetic case. The Root mean square error of the 3D canny edge (RMSECE), mutual information (MI), and feature similarity index metric (FSIM) were used to access the accuracy of LDIR on the patient data. The quality of the corresponding deformed contours was verified by an attending physician.Results:The resulting 90 percentile DVF error for the synthetic case was within 5.63mm for the original demons algorithm, 2.84mm for intensity correction alone, 2.45mm using controlpoints without intensity correction, and 1.48 mm for the LDIR algorithm. For the five patients the mean RMSECE of the original CT, Demons deformed CT, intensity corrected Demons CT, control‐point stabilized deformed CT, and LDIR CT was 0.24, 0.26, 0.20, 0.20, and 0.16 respectively.Conclusion:LDIR is accurate in the presence of multimodal intensity mismatch and CBCT noise contamination. Since LDIR is GPU based it can be implemented with minimal additional strain on clinical resources.This project has been supported by a CPRIT individual investigator award RP11032.

Application of three-dimensional medical image registration algorithm in image-guided radiotherapy

To acquire an accurate patient positioning in image-guided radiotherapy, an improved Demons deformable registration method was developed. The FDK algorithm was adopted to reconstruct Cone Beam CT( CBCT) and the reconstruction result was visualized by a volume rendering method with Visualization ToolKit( VTK). Based on the Insight segmentation and registration ToolKit( ITK), the Demons algorithm was completed incorporating the gradient information of fixed image and floating image by the concept of symmetric gradient, and a new formula of Demons force was demonstrated.Registrion experiments were carried out using medical images both from single modality and multi-modality. The results show that the improved Demons algorithm achieves a faster convergence speed and a higher precision compared with the original demons algorithm, which indicates that the Demons algorithm based on symmetric gradient is more suitable for the registration of CBCT reconstruction image and CT plan image in image-guided radiotherapy.

A contour-guided deformable image registration algorithm for adaptive radiotherapy

In adaptive radiotherapy, deformable image registration is often conducted between the planning CT and treatment CT (or cone beam CT) to generate a deformation vector field (DVF) for dose accumulation and contour propagation. The auto-propagated contours on the treatment CT may contain relatively large errors, especially in low-contrast regions. A clinician's inspection and editing of the propagated contours are frequently needed. The edited contours are able to meet the clinical requirement for adaptive therapy; however, the DVF is still inaccurate and inconsistent with the edited contours. The purpose of this work is to develop a contour-guided deformable image registration (CG-DIR) algorithm to improve the accuracy and consistency of the DVF for adaptive radiotherapy. Incorporation of the edited contours into the registration algorithm is realized by regularizing the objective function of the original demons algorithm with a term of intensity matching between the delineated structures set pairs. The CG-DIR algorithm is implemented on computer graphics processing units (GPUs) by following the original GPU-based demons algorithm computation framework (Gu et al 2010 Phys Med Biol. 55 207–219). The performance of CG-DIR is evaluated on five clinical head-and-neck and one pelvic cancer patient data. It is found that compared with the original demons, CG-DIR improves the accuracy and consistency of the DVF, while retaining similar high computational efficiency.

CT to cone-beam CT deformable registration with simultaneous intensity correction

Computed tomography (CT) to cone-beam CT (CBCT) deformable image registration (DIR) is a crucial step in adaptive radiation therapy. Current intensity-based registration algorithms, such as demons, may fail in the context of CT–CBCT DIR because of inconsistent intensities between the two modalities. In this paper, we propose a variant of demons, called deformation with intensity simultaneously corrected (DISC), to deal with CT–CBCT DIR. DISC distinguishes itself from the original demons algorithm by performing an adaptive intensity correction step on the CBCT image at every iteration step of the demons registration. Specifically, the intensity correction of a voxel in CBCT is achieved by matching the first and the second moments of the voxel intensities inside a patch around the voxel with those on the CT image. It is expected that such a strategy can remove artifacts in the CBCT image, as well as ensuring the intensity consistency between the two modalities. DISC is implemented on computer graphics processing units in compute unified device architecture (CUDA) programming environment. The performance of DISC is evaluated on a simulated patient case and six clinical head-and-neck cancer patient data. It is found that DISC is robust against the CBCT artifacts and intensity inconsistency and significantly improves the registration accuracy when compared with the original demons.

Respiratory motion correction with an improved demons algorithm for PET images

Respiratory motion is a major factor that affects the quality of PET images of the thoracic area. The diaphragm moves about 15–20 mm due to respiratory motion, which substantially degrades the effective spatial resolution of PET. In this paper, a gated acquisition method is used to correct the motion effects. In this method, an improved demons algorithm is proposed to align the gated images. The experimental results show that the quality of PET images is significantly improved when using our improved method and the proposed method has a faster convergence rate than the original demons algorithm.

WE-E-213CD-04: CT to Cone-Beam CT Deformable Registration With Simultaneous Intensity Correction.

To develop and validate a robust CT to cone-beam (CBCT) deformable image registration algorithm that can handle CBCT artifacts and intensity inconsistency, and thus can yield accurate registration results. We propose a new algorithm called Deformation with Intensity Simultaneously Corrected (DISC). DISC distinguishes itself from the original demons by performing an intensity correction procedure on the CBCT image at every iteration step of demons registration. Specifically, the intensity correction of a voxel in CBCT is achieved by matching the first and the second moments of the image intensities inside a patch around this voxel with those on the CT image. It is expected that such a strategy can remove artifacts in the CBCT image, as well as ensuring the intensity consistency between the two modalities and hence facilitating the registration process. DISC is implemented on computer graphics processing units (GPUs) using the compute unified device architecture (CUDA) programming environment. The performance of DISC has been qualitatively and quantitatively evaluated on a simulated patient case and six head-and- neck cancer patient data. Visual inspection shows that original demons distorts the tissues after registration, especially in regions which are heavily degraded by artifacts. DISC, on the other hand, can effectively register CT and CBCT image even in regions contaminated by severe artifacts. The intensity corrected CBCT that extracted from the last iteration of DISC is artifact-free and has similar histogram distribution with the deformed CT. We have developed a robust CT to CBCT deformable image registration method that properly deals with the CBCT artifacts and intensity inconsistency, and thus yields accurate registration results. This work is supported in part by the University of California Lab Fees Research Program, the Master Research Agreement from Varian Medical Systems, Inc., and the grants from the National Natural Science Foundation of China (No.30970866).

SU‐E‐J‐100: A Contour‐Guided Deformable Image Registration Algorithm for Adaptive Radiotherapy

Purpose: To develop a contour‐guided deformable image registration algorithm to improve the accuracy of deformation image registration in adaptive radiotherapy, especially for improving the accuracy of low contrast structures deformation. Methods: We developed an efficient algorithm to accurately deform low‐contrast structures in CT images. The new DIR algorithm is based on our original developed Demons algorithm by adding constraints defined by physician drawn contour pairs on images. By iteratively increasing the intensity of ROI enclosed by contour pairs, the optimization problem defined in Demons is ensured to converge to a desirable solution. Results: The new algorithm was validated on both a numerical phantom experiment and a clinical patient case. For the digital phantom experiment, a moving image is defined as two intersected spheres embedded in a cube and the contrast between spheres and cube is low; and a target image is a low‐contrast sphere in a cubic. For the clinical case, two CT images scanned for a head and neck cancer patient in one month apart are treated as a pair of moving and target images. The left parotids with anatomical changes are defined as structures of interest (SOIs). Qualitatively, with visual inspection, the proposed algorithm yields satisfactory results in both numerical and clinical testing cases. Quantitatively, root mean squares error (RMS) and normalized correlation coefficient (NCC) were calculated between the deformed and target SOI. Compared to the original Demons algorithm, the contour‐guided Demons algorithm results the decrease of RMS and the increase of NCC for both cases. Conclusions: The proposed contour‐guided demons algorithm permits us to take advantage of the structures defined by physicians to obtain more accurate image deformations. It provides a useful clinical tool for future interactive adaptive radiotherapy.

Object‐constrained meshless deformable algorithm for high speed 3D nonrigid registration between CT and CBCT

High-speed nonrigid registration between the planning CT and the treatment CBCT data is critical for real time image guided radiotherapy (IGRT) to improve the dose distribution and to reduce the toxicity to adjacent organs. The authors propose a new fully automatic 3D registration framework that integrates object-based global and seed constraints with the grayscale-based "demons" algorithm. Clinical objects were segmented on the planning CT images and were utilized as meshless deformable models during the nonrigid registration process. The meshless models reinforced a global constraint in addition to the grayscale difference between CT and CBCT in order to maintain the shape and the volume of geometrically complex 3D objects during the registration. To expedite the registration process, the framework was stratified into hierarchies, and the authors used a frequency domain formulation to diffuse the displacement between the reference and the target in each hierarchy. Also during the registration of pelvis images, they replaced the air region inside the rectum with estimated pixel values from the surrounding rectal wall and introduced an additional seed constraint to robustly track and match the seeds implanted into the prostate. The proposed registration framework and algorithm were evaluated on 15 real prostate cancer patients. For each patient, prostate gland, seminal vesicle, bladder, and rectum were first segmented by a radiation oncologist on planning CT images for radiotherapy planning purpose. The same radiation oncologist also manually delineated the tumor volumes and critical anatomical structures in the corresponding CBCT images acquired at treatment. These delineated structures on the CBCT were only used as the ground truth for the quantitative validation, while structures on the planning CT were used both as the input to the registration method and the ground truth in validation. By registering the planning CT to the CBCT, a displacement map was generated. Segmented volumes in the CT images deformed using the displacement field were compared against the manual segmentations in the CBCT images to quantitatively measure the convergence of the shape and the volume. Other image features were also used to evaluate the overall performance of the registration. The algorithm was able to complete the segmentation and registration process within 1 min, and the superimposed clinical objects achieved a volumetric similarity measure of over 90% between the reference and the registered data. Validation results also showed that the proposed registration could accurately trace the deformation inside the target volume with average errors of less than 1 mm. The method had a solid performance in registering the simulated images with up to 20 Hounsfield unit white noise added. Also, the side by side comparison with the original demons algorithm demonstrated its improved registration performance over the local pixel-based registration approaches. Given the strength and efficiency of the algorithm, the proposed method has significant clinical potential to accelerate and to improve the CBCT delineation and targets tracking in online IGRT applications.