Evaluation of deformable image registration and a motion model in CT images with limited features

Surrogate-driven respiratory motion model for projection-resolved motion estimation and motion compensated cone-beam CT reconstruction from unsorted projection data

Objective.As the most common solution to motion artefact for cone-beam CT (CBCT) in radiotherapy, 4DCBCT suffers from long acquisition time and phase sorting error. This issue could be addressed if the motion at each projection could be known, which is a severely ill-posed problem. This study aims to obtain the motion at each time point and motion-free image simultaneously from unsorted projection data of a standard 3DCBCT scan.Approach.Respiration surrogate signals were extracted by the Intensity Analysis method. A general framework was then deployed to fit a surrogate-driven motion model that characterized the relation between the motion and surrogate signals at each time point. Motion model fitting and motion compensated reconstruction were alternatively and iteratively performed. Stochastic subset gradient based method was used to significantly reduce the computation time. The performance of our method was comprehensively evaluated through digital phantom simulation and also validated on clinical scans from six patients.Results.For digital phantom experiments, motion models fitted with ground-truth or extracted surrogate signals both achieved a much lower motion estimation error and higher image quality, compared with non motion-compensated results.For the public SPARE Challenge datasets, more clear lung tissues and less blurry diaphragm could be seen in the motion compensated reconstruction, comparable to the benchmark 4DCBCT images but with a higher temporal resolution. Similar results were observed for two real clinical 3DCBCT scans.Significance.The motion compensated reconstructions and motion models produced by our method will have direct clinical benefit by providing more accurate estimates of the delivered dose and ultimately facilitating more accurate radiotherapy treatments for lung cancer patients.

Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view

Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue. Due to technological, methodological and clinical evolutions, tools for adaptive RT (ART) are becoming increasingly sophisticated. The aim of this paper is to give an overview of the key steps of an H&N ART workflow and tools from the point of view of a group of French-speaking medical physicists and physicians (from GORTEC). Focuses are made on image registration, segmentation, estimation of the delivered dose of the day, workflow and quality assurance for an implementation of H&N offline and online ART. Practical recommendations are given to assist physicians and medical physicists in a clinical workflow.

Performance Evaluation of Deformable Image Registration Algorithms Using Computed Tomography of Multiple Lung Metastases

Purpose: Various deformable image registration (DIR) methods have been used to evaluate organ deformations in 4-dimensional computed tomography (4D CT) images scanned during the respiratory motions of a patient. This study assesses the performance of 10 DIR algorithms using 4D CT images of 5 patients with fiducial markers (FMs) implanted during the postoperative radiosurgery of multiple lung metastases. Methods: To evaluate DIR algorithms, 4D CT images of 5 patients were used, and ground-truths of FMs and tumors were generated by physicians based on their medical expertise. The positions of FMs and tumors in each 4D CT phase image were determined using 10 DIR algorithms, and the deformed results were compared with ground-truth data. Results: The target registration errors (TREs) between the FM positions estimated by optical flow algorithms and the ground-truth ranged from 1.82 ± 1.05 to 1.98 ± 1.17 mm, which is within the uncertainty of the ground-truth position. Two algorithm groups, namely, optical flow and demons, were used to estimate tumor positions with TREs ranging from 1.29 ± 1.21 to 1.78 ± 1.75 mm. With respect to the deformed position for tumors, for the 2 DIR algorithm groups, the maximum differences of the deformed positions for gross tumor volume tracking were approximately 4.55 to 7.55 times higher than the mean differences. Errors caused by the aforementioned difference in the Hounsfield unit values were also observed. Conclusions: We quantitatively evaluated 10 DIR algorithms using 4D CT images of 5 patients and compared the results with ground-truth data. The optical flow algorithms showed reasonable FM-tracking results in patient 4D CT images. The iterative optical flow method delivered the best performance in this study. With respect to the tumor volume, the optical flow and demons algorithms delivered the best performance.

Multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI): Development and initial evaluation in liver tumor patients

PURPOSE

To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients.

METHODS AND MATERIALS

The MC-4D-MRI technique integrates multi-parametric MRI fusion, 4D-MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image pre-processing, fusion algorithm, adaptation, and fused multi-contrast MRI as output. Four-dimensional deformation vector fields (4D-DVF) were generated from an original T2/T1-w 4D-MRI by deforming end-of-inhalation (EOI) to nine other phase volumes via DIR. The 4D-DVF were applied to multi-contrast MRI to generate a spectrum of 4D-MRI in different image contrasts. The MC-4D-MRI technique was evaluated in five liver tumor patients on tumor contrast-to-noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D-DIR introduced errors in tumor motion range, centroid location, extent, and volume.

RESULTS

MC-4D-MRI consisting of 4D-MRIs in native image contrasts (T1-w, T2-w, and T2/T1-w) and synthetic image contrasts, such as tumor-enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6 ± 1.8 in the T2/T1-w MRI, to -4.4 ± 2.4, 6.6 ± 3.0, and 9.6 ± 3.9 in the T1-w, T2-w, and TEC MRI, respectively. Patient ITV inter-observer mean Dice similarity coefficient (mDSC) increased from 0.65 ± 0.10 in the original T2/T1-w 4D-MRI, to 0.76 ± 0.14, 0.77 ± 0.12, and 0.86 ± 0.05 in the T1-w, T2-w, and TEC 4D-MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D-MRIs and original T2/T1-w 4D-MRI were 1.2 ± 1.3, 0.3 ± 0.7, and 0.5 ± 0.5 mm, respectively. Patient tumor displacement phase-averaged absolute differences between the three 4D-MRIs and the original 4D-MRI were 0.72 ± 0.33, 0.62 ± 0.54, and 0.74 ± 0.43 mm in the superior-inferior (SI) direction, and 0.59 ± 0.36, 0.51 ± 0.30, and 0.50 ± 0.24 mm in the anterior-posterior (AP) direction, respectively. In the digital phantoms, phase-averaged absolute tumor centroid shift caused by the 4D-DIR were at or below 0.5 mm in SI, AP, and left-right (LR) directions.

CONCLUSION

We developed an MC-4D-MRI technique capable of expanding single image contrast 4D-MRI along a new dimension of image contrast. Initial evaluations in liver tumor patients showed enhancements in image contrast variety, tumor contrast, and ITV contouring consistencies using MC-4D-MRI. The technique might offer new perspectives on the image contrast of MRI and 4D-MRI in MR-guided radiotherapy.

Automatic large quantity landmark pairs detection in 4DCT lung images

PURPOSE

To automatically and precisely detect a large quantity of landmark pairs between two lung computed tomography (CT) images to support evaluation of deformable image registration (DIR). We expect that the generated landmark pairs will significantly augment the current lung CT benchmark datasets in both quantity and positional accuracy.

METHODS

A large number of landmark pairs were detected within the lung between the end-exhalation (EE) and end-inhalation (EI) phases of the lung four-dimensional computed tomography (4DCT) datasets. Thousands of landmarks were detected by applying the Harris-Stephens corner detection algorithm on the probability maps of the lung vasculature tree. A parametric image registration method (pTVreg) was used to establish initial landmark correspondence by registering the images at EE and EI phases. A multi-stream pseudo-siamese (MSPS) network was then developed to further improve the landmark pair positional accuracy by directly predicting three-dimensional (3D) shifts to optimally align the landmarks in EE to their counterparts in EI. Positional accuracies of the detected landmark pairs were evaluated using both digital phantoms and publicly available landmark pairs.

RESULTS

Dense sets of landmark pairs were detected for 10 4DCT lung datasets, with an average of 1886 landmark pairs per case. The mean and standard deviation of target registration error (TRE) were 0.47 ± 0.45 mm with 98% of landmark pairs having a TRE smaller than 2 mm for 10 digital phantom cases. Tests using 300 manually labeled landmark pairs in 10 lung 4DCT benchmark datasets (DIRLAB) produced TRE results of 0.73 ± 0.53 mm with 97% of landmark pairs having a TRE smaller than 2 mm.

CONCLUSION

A new method was developed to automatically and precisely detect a large quantity of landmark pairs between lung CT image pairs. The detected landmark pairs could be used as benchmark datasets for more accurate and informative quantitative evaluation of DIR algorithms.

A robust deformable image registration enhancement method based on radial basis function

Background

To develop and evaluate a robust deformable image registration (DIR) enhancement method based on radial basis function (RBF) expansion.

Methods

To improve DIR accuracy using sparsely available measured displacements, it is crucial to estimate the motion correlation between the voxels. In the proposed method, we chose to derive this correlation from the initial displacement vector fields (DVFs), and represent it in the form of RBF expansion coefficients of the voxels. The method consists of three steps: (I) convert an initial DVF to a coefficient matrix comprising expansion coefficients of the Wendland's RBF; (II) modify the coefficient matrix under the guidance of sparely distributed landmarks to generate the post-enhancement coefficient matrix; and (III) convert the post-enhancement coefficient matrix to the post-enhancement DVF. The method was tested on five DIR algorithms using a digital phantom. 3D registration errors were calculated for comparisons between the pre-/post-enhancement DVFs and the ground-truth DVFs. Effects of the number and locations of landmarks on DIR enhancement were evaluated.

Results

After applying the DIR enhancement method, the 3D registration errors per voxel (unit: mm) were reduced from pre-enhancement to post-enhancement by 1.3 (2.4 to 1.1, 54.2%), 0.0 (0.9 to 0.9, 0.0%), 6.1 (8.2 to 2.1, 74.4%), 3.2 (4.7 to 1.5, 68.1%), and 1.7 (2.9 to 1.2, 58.6%) for the five tested DIR algorithms respectively. The average DIR error reduction was 2.5±2.3 mm (percentage error reduction: 51.1%±29.1%). 3D registration errors decreased inverse-exponentially as the number of landmarks increased, and were insensitive to the landmarks' locations in relation to the down-sampling DVF grids.

Conclusions

We demonstrated the feasibility of a robust RBF-based method for enhancing DIR accuracy using sparsely distributed landmarks. This method has been shown robust and effective in reducing DVF errors using different numbers and distributions of landmarks for various DIR algorithms.

A hybrid proton and hyperpolarized gas tagging MRI technique for lung respiratory motion imaging: a feasibility study

The aim of this work was to develop a novel hybrid 3D hyperpolarized (HP) gas tagging MRI (t-MRI) technique and to evaluate it for lung respiratory motion measurement with comparison to deformable image registrations (DIR) methods. Three healthy subjects underwent a hybrid MRI which combines 3D HP gas t-MRI with a low resolution (Low-R, 4.5 mm isotropic voxels) 3D proton MRI (p-MRI), plus a high resolution (High-R, 2.5 mm isotropic voxels) 3D p-MRI, during breath-holds at the end-of-inhalation (EOI) and the end-of-exhalation (EOE). Displacement vector field (DVF) of the lung motion was determined from the t-MRI images by tracking tagging grids and from the High-R p-MRI using three DIR methods (B-spline based method implemented by Velocity, Free Form Deformation by MIM, and B-spline by an open source software Elastix: denoted as A, B, and C, respectively), labeled as tDVF and dDVF, respectively. The tDVF from the HP gas t-MRI was used as ground-truth reference to evaluate performance of the three DIR methods. Differences in both magnitude and angle between the tDVF and dDVFs were analyzed. The mean lung motion of the three subjects was 37.3 mm, 8.9 mm and 12.9 mm, respectively. Relatively large discrepancies were observed between the tDVF and the dDVFs as compared to previously reported DIR errors. The mean  ±  standard deviation (SD) DVF magnitude difference was 8.3  ±  5.6 mm, 9.2  ±  4.5 mm, and 9.3  ±  6.1 mm, and the mean  ±  SD DVF angular difference was 29.1  ±  12.1°, 50.1  ±  28.6°, and 39.0  ±  6.3°, for the DIR Methods A, B, and C, respectively. These preliminary results showed that the hybrid HP gas t-MRI technique revealed different lung motion patterns as compared to the DIR methods. It may provide unique perspectives in developing and evaluating DIR of the lungs. Novelty and Significance We designed a MRI protocol that includes a novel hybrid MRI technique (3D HP gas t-MRI with a low resolution 3D p-MRI) plus a high resolution 3D p-MRI. We tested the novel hybrid MRI technique on three healthy subjects for measuring regional lung respiratory motion with comparison to deformable image registrations (DIR) methods, and observed relatively large discrepancies in lung motion between HP gas t-MRI and DIR methods.

Use of Elastic Registration in Pulmonary MRI for the Assessment of Pulmonary Fibrosis in Patients with Systemic Sclerosis

Background Current imaging methods are not sensitive to changes in pulmonary function resulting from fibrosis. MRI with ultrashort echo time can be used to image the lung parenchyma and lung motion. Purpose To evaluate elastic registration of inspiratory-to-expiratory lung MRI for the assessment of pulmonary fibrosis in study participants with systemic sclerosis (SSc). Materials and Methods This prospective study was performed from September 2017 to March 2018 and recruited healthy volunteers and participants with SSc and high-resolution CT (within the previous 3 months) of the chest for lung MRI. Two breath-hold, coronal, three-dimensional, ultrashort-echo-time, gradient-echo sequences of the lungs were acquired after full inspiration and expiration with a 3.0-T unit. Images were registered from inspiration to expiration by using an elastic registration algorithm. Jacobian determinants were calculated from deformation fields and represented on color maps. Similarity between areas with marked shrinkage and logarithm of Jacobian determinants less than -0.15 were compared between healthy volunteers and study participants with SSc. Receiver operating characteristic curve analysis was performed to determine the best Dice similarity coefficient threshold for diagnosis of fibrosis. Results Sixteen participants with SSc (seven with pulmonary fibrosis at high-resolution CT) and 11 healthy volunteers were evaluated. Areas of marked shrinkage during expiration with logarithm of Jacobian determinants less than -0.15 were found in the posterior lung bases of healthy volunteers and in participants with SSc without fibrosis, but not in participants with fibrosis. The sensitivity and specificity of MRI for presence of fibrosis at high-resolution CT were 86% and 75%, respectively (area under the curve, 0.81; P = .04) by using a threshold of 0.36 for Dice similarity coefficient. Conclusion Elastic registration of inspiratory-to-expiratory MRI shows less lung base respiratory deformation in study participants with systemic sclerosis-related pulmonary fibrosis compared with participants without fibrosis. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Biederer in this issue.

ELPHA: Dynamically deformable liver phantom for real-time motion-adaptive radiotherapy treatments

PURPOSE

Real-time motion-adaptive radiotherapy of intrahepatic tumors needs to account for motion and deformations of the liver and the target location within. Phantoms representative of anatomical deformations are required to investigate and improve dynamic treatments. A deformable phantom capable of testing motion detection and motion mitigation techniques is presented here.

METHODS

The dynamically dEformable Liver PHAntom (ELPHA) was designed to fulfill three main constraints: First, a reproducibly deformable anatomy is required. Second, the phantom should provide multimodality imaging contrast for motion detection. Third, a time-resolved dosimetry system to measure temporal effects should be provided. An artificial liver with vasculature was casted from soft silicone mixtures. The silicones allow for deformation and radiographic image contrast, while added cellulose provides ultrasonic contrast. An actuator was used for compressing the liver in the inferior direction according to a prescribed respiratory motion trace. Electromagnetic (EM) transponders integrated in ELPHA help provide ground truth motion traces. They were used to quantify the motion reproducibility of the phantom and to validate motion detection based on ultrasound imaging. A two-dimensional ultrasound probe was used to follow the position of the vessels with a template-matching algorithm. This detected vessel motion was compared to the EM transponder signal by calculating the root-mean-square error (RMSE). ELPHA was then used to investigate the dose deposition of dynamic treatment deliveries. Two dosimetry systems, radio-chromic film and plastic scintillation dosimeters (PSD), were integrated in ELPHA. The PSD allow for time-resolved measurement of the delivered dose, which was compared to a time-resolved dose of the treatment planning system. Film and PSD were used to investigate dose delivery to the deforming phantom without motion compensation and with treatment-couch tracking for motion compensation.

RESULTS

ELPHA showed densities of 66 and 45 HU in the liver and the surrounding tissues. A high motion reproducibility with a submillimeter RMSE (<0.32 mm) was measured. The motion of the vasculature detected with ultrasound agreed well with the EM transponder position (RMSE < 1 mm). A time-resolved dosimetry system with a 1 Hz time resolution was achieved with the PSD. The agreement of the planned and measured dose to the PSD decreased with increasing motion amplitude: A dosimetric RMSE of 1.2, 2.1, and 2.7 cGy/s was measured for motion amplitudes of 8, 16, and 24 mm, respectively. With couch tracking as motion compensation, these values decreased to 1.1, 1.4, and 1.4 cGy/s. This is closer to the static situation with 0.7 cGy/s. Film measurements showed that couch tracking was able to compensate for motion with a mean target dose within 5% of the static situation (-5% to +1%), which was higher than in the uncompensated cases (-41% to -1%).

CONCLUSIONS

ELPHA is a deformable liver phantom with high motion reproducibility. It was demonstrated to be suitable for the verification of motion detection and motion mitigation modalities. Based on the multimodality image contrast, a high accuracy of ultrasound based motion detection was shown. With the time-resolved dosimetry system, ELPHA is suitable for performance assessment of real-time motion-adaptive radiotherapy, as was shown exemplary with couch tracking.

An initial investigation of hyperpolarized gas tagging magnetic resonance imaging in evaluating deformable image registration-based lung ventilation

BACKGROUND

Deformable image registration (DIR)-based lung ventilation mapping is attractive due to its simplicity, and also challenging due to its susceptibility to errors and uncertainties. In this study, we explored the use of 3D Hyperpolarized (HP) gas tagging MRI to evaluate DIR-based lung ventilation.

METHOD AND MATERIAL

Three healthy volunteers included in this study underwent both 3D HP gas tagging MRI (t-MRI) and 3D proton MRI (p-MRI) using balanced steady-state free precession pulse sequence at end of inhalation and end of exhalation. We first obtained the reference displacement vector fields (DVFs) from the t-MRIs by tracking the motion of each tagging grid between the exhalation and the inhalation phases. Then, we determined DIR-based DVFs from the p-MRIs by registering the images at the two phases with two commercial DIR algorithms. Lung ventilations were calculated from both the reference DVFs and the DIR-based DVFs using the Jacobian method and then compared using cross correlation and mutual information.

RESULTS

The DIR-based lung ventilations calculated using p-MRI varied considerably from the reference lung ventilations based on t-MRI among all three subjects. The lung ventilations generated using Velocity AI were preferable for the better spatial homogeneity and accuracy compared to the ones using MIM, with higher average cross correlation (0.328 vs 0.262) and larger average mutual information (0.528 vs 0.323).

CONCLUSION

We demonstrated that different DIR algorithms resulted in different lung ventilation maps due to underlining differences in the DVFs. HP gas tagging MRI provides a unique platform for evaluating DIR-based lung ventilation.

Non-rigid point cloud registration based lung motion estimation using tangent-plane distance

Accurate estimation of motion field in respiration-correlated 4DCT images, is a precondition for the analysis of patient-specific breathing dynamics and subsequent image-supported treatment planning. However, the lung motion estimation often suffers from the sliding motion. In this paper, a novel lung motion method based on the non-rigid registration of point clouds is proposed, and the tangent-plane distance is used to represent the distance term, which describes the difference between two point clouds. Local affine transformation model is used to express the non-rigid deformation of the lung motion. The final objective function is expressed in the Frobenius norm formation, and matrix optimization scheme is carried out to find out the optimal transformation parameters that minimize the objective function. A key advantage of our proposed method is that it alleviates the requirement that the source point cloud and the reference point cloud should be in one-to-one corresponding relationship, and the requirement is difficult to be satisfied in practical application. Furthermore, the proposed method takes the sliding motion of the lung into consideration and improves the registration accuracy by reducing the constraint of the motion along the tangent direction. Non-rigid registration experiments are carried out to validate the performance of the proposed method using popi-model data. The results demonstrate that the proposed method outperforms the traditional method with about 20% accuracy increase.

Evaluation of automatic contour propagation in T2-weighted 4DMRI for normal-tissue motion assessment using internal organ-at-risk volume (IRV)

Purpose

The purpose of this study was to evaluate the quality of automatically propagated contours of organs at risk (OARs) based on respiratory‐correlated navigator‐triggered four‐dimensional magnetic resonance imaging (RC‐4DMRI) for calculation of internal organ‐at‐risk volume (IRV) to account for intra‐fractional OAR motion.

Methods and Materials

T2‐weighted RC‐4DMRI images were of 10 volunteers acquired and reconstructed using an internal navigator‐echo surrogate and concurrent external bellows under an IRB‐approved protocol. Four major OARs (lungs, heart, liver, and stomach) were delineated in the 10‐phase 4DMRI. Two manual‐contour sets were delineated by two clinical personnel and two automatic‐contour sets were propagated using free‐form deformable image registration. The OAR volume variation within the 10‐phase cycle was assessed and the IRV was calculated as the union of all OAR contours. The OAR contour similarity between the navigator‐triggered and bellows‐rebinned 4DMRI was compared. A total of 2400 contours were compared to the most probable ground truth with a 95% confidence level (S95) in similarity, sensitivity, and specificity using the simultaneous truth and performance level estimation (STAPLE) algorithm.

Results

Visual inspection of automatically propagated contours finds that approximately 5–10% require manual correction. The similarity, sensitivity, and specificity between manual and automatic contours are indistinguishable (P > 0.05). The Jaccard similarity indexes are 0.92 ± 0.02 (lungs), 0.89 ± 0.03 (heart), 0.92 ± 0.02 (liver), and 0.83 ± 0.04 (stomach). Volume variations within the breathing cycle are small for the heart (2.6 ± 1.5%), liver (1.2 ± 0.6%), and stomach (2.6 ± 0.8%), whereas the IRV is much larger than the OAR volume by: 20.3 ± 8.6% (heart), 24.0 ± 8.6% (liver), and 47.6 ± 20.2% (stomach). The Jaccard index is higher in navigator‐triggered than bellows‐rebinned 4DMRI by 4% (P < 0.05), due to the higher image quality of navigator‐based 4DMRI.

Conclusion

Automatic and manual OAR contours from Navigator‐triggered 4DMRI are not statistically distinguishable. The navigator‐triggered 4DMRI image provides higher contour quality than bellows‐rebinned 4DMRI. The IRVs are 20–50% larger than OAR volumes and should be considered in dose estimation.

Adaptive radiotherapy for NSCLC patients: utilizing the principle of energy conservation to evaluate dose mapping operations

Tumor regression during the course of fractionated radiotherapy confounds the ability to accurately estimate the total dose delivered to tumor targets. Here we present a new criterion to improve the accuracy of image intensity-based dose mapping operations for adaptive radiotherapy for patients with non-small cell lung cancer (NSCLC). Six NSCLC patients were retrospectively investigated in this study. An image intensity-based B-spline registration algorithm was used for deformable image registration (DIR) of weekly CBCT images to a reference image. The resultant displacement vector fields were employed to map the doses calculated on weekly images to the reference image. The concept of energy conservation was introduced as a criterion to evaluate the accuracy of the dose mapping operations. A finite element method (FEM)-based mechanical model was implemented to improve the performance of the B-Spline-based registration algorithm in regions involving tumor regression. For the six patients, deformed tumor volumes changed by 21.2  ±  15.0% and 4.1  ±  3.7% on average for the B-Spline and the FEM-based registrations performed from fraction 1 to fraction 21, respectively. The energy deposited in the gross tumor volume (GTV) was 0.66 Joules (J) per fraction on average. The energy derived from the fractional dose reconstructed by the B-spline and FEM-based DIR algorithms in the deformed GTV's was 0.51 J and 0.64 J, respectively. Based on landmark comparisons for the 6 patients, mean error for the FEM-based DIR algorithm was 2.5  ±  1.9 mm. The cross-correlation coefficient between the landmark-measured displacement error and the loss of radiation energy was  -0.16 for the FEM-based algorithm. To avoid uncertainties in measuring distorted landmarks, the B-Spline-based registrations were compared to the FEM registrations, and their displacement differences equal 4.2  ±  4.7 mm on average. The displacement differences were correlated to their relative loss of radiation energy with a cross-correlation coefficient equal to 0.68. Based on the principle of energy conservation, the FEM-based mechanical model has a better performance than the B-Spline-based DIR algorithm. It is recommended that the principle of energy conservation be incorporated into a comprehensive QA protocol for adaptive radiotherapy.

A Novel Respiratory Motion Perturbation Model Adaptable to Patient Breathing Irregularities

PURPOSE

To develop a physical, adaptive motion perturbation model to predict tumor motion using feedback from dynamic measurement of breathing conditions to compensate for breathing irregularities.

METHODS AND MATERIALS

A novel respiratory motion perturbation (RMP) model was developed to predict tumor motion variations caused by breathing irregularities. This model contained 2 terms: the initial tumor motion trajectory, measured from 4-dimensional computed tomography (4DCT) images, and motion perturbation, calculated from breathing variations in tidal volume (TV) and breathing pattern (BP). The motion perturbation was derived from the patient-specific anatomy, tumor-specific location, and time-dependent breathing variations. Ten patients were studied, and 2 amplitude-binned 4DCT images for each patient were acquired within 2 weeks. The motion trajectories of 40 corresponding bifurcation points in both 4DCT images of each patient were obtained using deformable image registration. An in-house 4D data processing toolbox was developed to calculate the TV and BP as functions of the breathing phase. The motion was predicted from the simulation 4DCT scan to the treatment 4DCT scan, and vice versa, resulting in 800 predictions. For comparison, noncorrected motion differences and the predictions from a published 5-dimensional model were used.

RESULTS

The average motion range in the superoinferior direction was 9.4 ± 4.4 mm, the average ΔTV ranged from 10 to 248 mm³ (-26% to 61%), and the ΔBP ranged from 0 to 0.2 (-71% to 333%) between the 2 4DCT scans. The mean noncorrected motion difference was 2.0 ± 2.8 mm between 2 4DCT motion trajectories. After applying the RMP model, the mean motion difference was reduced significantly to 1.2 ± 1.8 mm (P=.0018), a 40% improvement, similar to the 1.2 ± 1.8 mm (P=.72) predicted with the 5-dimensional model.

CONCLUSIONS

A novel physical RMP model was developed with an average accuracy of 1.2 ± 1.8 mm for interfraction motion prediction, similar to that of a published lung motion model. This physical RMP was analytically derived and is able to adapt to breathing irregularities. Further improvement of this RMP model is under investigation.

Development of a deformable dosimetric phantom to verify dose accumulation algorithms for adaptive radiotherapy

Adaptive radiotherapy may improve treatment outcomes for lung cancer patients. Because of the lack of an effective tool for quality assurance, this therapeutic modality is not yet accepted in clinic. The purpose of this study is to develop a deformable physical phantom for validation of dose accumulation algorithms in regions with heterogeneous mass. A three-dimensional (3D) deformable phantom was developed containing a tissue-equivalent tumor and heterogeneous sponge inserts. Thermoluminescent dosimeters (TLDs) were placed at multiple locations in the phantom each time before dose measurement. Doses were measured with the phantom in both the static and deformed cases. The deformation of the phantom was actuated by a motor driven piston. 4D computed tomography images were acquired to calculate 3D doses at each phase using Pinnacle and EGSnrc/DOSXYZnrc. These images were registered using two registration software packages: VelocityAI and Elastix. With the resultant displacement vector fields (DVFs), the calculated 3D doses were accumulated using a mass-and energy congruent mapping method and compared to those measured by the TLDs at four typical locations. In the static case, TLD measurements agreed with all the algorithms by 1.8% at the center of the tumor volume and by 4.0% in the penumbra. In the deformable case, the phantom's deformation was reproduced within 1.1 mm. For the 3D dose calculated by Pinnacle, the total dose accumulated with the Elastix DVF agreed well to the TLD measurements with their differences <2.5% at four measured locations. When the VelocityAI DVF was used, their difference increased up to 11.8%. For the 3D dose calculated by EGSnrc/DOSXYZnrc, the total doses accumulated with the two DVFs were within 5.7% of the TLD measurements which are slightly over the rate of 5% for clinical acceptance. The detector-embedded deformable phantom allows radiation dose to be measured in a dynamic environment, similar to deforming lung tissues, supporting the validation of dose mapping and accumulation operations in regions with heterogeneous mass, and dose distributions.

Phase correlation applied to the 3D registration of CT and CBCT image volumes

PURPOSE

In this study, a 3D phase correlation algorithm was investigated to test feasibility for use in determining the anatomical changes that occur throughout a patient's radiotherapy treatment. The algorithm determines the transformations between two image volumes through analysis in the Fourier domain and has not previously been used in radiotherapy for 3D registration of CT and CBCT volumes.

METHODS

Various known transformations were applied to a patient's prostate CT image volume to create 12 different test cases. The mean absolute error and standard deviation were determined by evaluating the difference between the known contours and those calculated from the registration process on a point-by-point basis. Similar evaluations were performed on images with increasing levels of noise added. The improvement in structure overlap offered by the algorithm in registering clinical CBCT to CT images was evaluated using the Dice Similarity Coefficient (DSC).

RESULTS

A mean error of 2.35 (σ = 1.54) mm was calculated for the 12 deformations applied. When increasing levels of noise were introduced to the images, the mean errors were observed to rise up to a maximum increase of 1.77 mm. For CBCT to CT registration, maximum improvements in the DSC of 0.09 and 0.46 were observed for the bladder and rectum, respectively.

CONCLUSIONS

The Fourier-based 3D phase correlation registration algorithm investigated displayed promising results in CT to CT and CT to CBCT registration, offers potential in terms of efficiency and robustness to noise, and is suitable for use in radiotherapy for monitoring patient anatomy throughout treatment.

Quantitative analysis of respiration-related movement for abdominal artery in multiphase hepatic CT

Objectives

Respiration-induced motion in the liver causes potential errors on the measurement of contrast medium in abdominal artery from multiphase hepatic CT scans. In this study, we investigated the use of hepatic CT images to quantitatively estimate the abdominal artery motion due to respiration by optical flow method.

Materials and Methods

A total of 132 consecutive patients were included in our patient cohort. We apply the optical flow method to compute the motion of the abdominal artery due to respiration.

Results

The minimum and maximum displacements of the abdominal artery motion were 0.02 and 30.87 mm by manual delineation, 0.03 and 40.75 mm calculated by optical flow method, respectively. Both high consistency and correlation between the present method and the physicians’ manual delineations were acquired with the regression equation of movement, y = 0.81x+0.25, r = 0.95, p<0.001.

Conclusion

We estimated the motion of abdominal artery due to respiration using the optical flow method in multiphase hepatic CT scans and the motion estimations were validated with the visualization of physicians. The quantitative analysis of respiration-related movement of abdominal artery could be used for motion correction in the measurement of contrast medium passing though abdominal artery in multiphase CT liver scans.

Making the right software choice for clinically used equipment in radiation oncology

The customer of a new system for clinical use in radiation oncology must consider many options in order to find the optimal combination of software tools. Many commercial systems are available and each system has a large number of technical features. However an appraisal of the technical capabilities, especially the options for clinical implementations, is hardly assessable at first view.

The intention of this article was to generate an assessment of the necessary functionalities for high precision radiotherapy and their integration in ROKIS (Radiation oncology clinic information system) for future customers, especially with regard to clinical applicability. Therefore we analysed the clinically required software functionalities and divided them into three categories: minimal, enhanced and optimal requirements for high conformal radiation treatment.

Bidirectional elastic image registration using B-spline affine transformation

A registration scheme termed as B-spline affine transformation (BSAT) is presented in this study to elastically align two images. We define an affine transformation instead of the traditional translation at each control point. Mathematically, BSAT is a generalized form of the affine transformation and the traditional B-spline transformation (BST). In order to improve the performance of the iterative closest point (ICP) method in registering two homologous shapes but with large deformation, a bidirectional instead of the traditional unidirectional objective/cost function is proposed. In implementation, the objective function is formulated as a sparse linear equation problem, and a sub-division strategy is used to achieve a reasonable efficiency in registration. The performance of the developed scheme was assessed using both two-dimensional (2D) synthesized dataset and three-dimensional (3D) volumetric computed tomography (CT) data. Our experiments showed that the proposed B-spline affine model could obtain reasonable registration accuracy.

Using patient-specific phantoms to evaluate deformable image registration algorithms for adaptive radiation therapy

The quality of adaptive treatment planning depends on the accuracy of its underlying deformable image registration (DIR). The purpose of this study is to evaluate the performance of two DIR algorithms, B‐spline‐based deformable multipass (DMP) and deformable demons (Demons), implemented in a commercial software package. Evaluations were conducted using both computational and physical deformable phantoms. Based on a finite element method (FEM), a total of 11 computational models were developed from a set of CT images acquired from four lung and one prostate cancer patients. FEM generated displacement vector fields (DVF) were used to construct the lung and prostate image phantoms. Based on a fast‐Fourier transform technique, image noise power spectrum was incorporated into the prostate image phantoms to create simulated CBCT images. The FEM‐DVF served as a gold standard for verification of the two registration algorithms performed on these phantoms. The registration algorithms were also evaluated at the homologous points quantified in the CT images of a physical lung phantom. The results indicated that the mean errors of the DMP algorithm were in the range of 1.0~3.1mm for the computational phantoms and 1.9 mm for the physical lung phantom. For the computational prostate phantoms, the corresponding mean error was 1.0–1.9 mm in the prostate, 1.9–2.4 mm in the rectum, and 1.8–2.1 mm over the entire patient body. Sinusoidal errors induced by B‐spline interpolations were observed in all the displacement profiles of the DMP registrations. Regions of large displacements were observed to have more registration errors. Patient‐specific FEM models have been developed to evaluate the DIR algorithms implemented in the commercial software package. It has been found that the accuracy of these algorithms is patient‐dependent and related to various factors including tissue deformation magnitudes and image intensity gradients across the regions of interest. This may suggest that DIR algorithms need to be verified for each registration instance when implementing adaptive radiation therapy.

PACS numbers: 87.10.Kn, 87.55.km, 87.55.Qr, 87.57.nj

Estimation of the uncertainty of elastic image registration with the demons algorithm

The accuracy of elastic image registration is limited. We propose an approach to detect voxels where registration based on the demons algorithm is likely to perform inaccurately, compared to other locations of the same image. The approach is based on the assumption that the local reproducibility of the registration can be regarded as a measure of uncertainty of the image registration. The reproducibility is determined as the standard deviation of the displacement vector components obtained from multiple registrations. These registrations differ in predefined initial deformations. The proposed approach was tested with artificially deformed lung images, where the ground truth on the deformation is known. In voxels where the result of the registration was less reproducible, the registration turned out to have larger average registration errors as compared to locations of the same image, where the registration was more reproducible. The proposed method can show a clinician in which area of the image the elastic registration with the demons algorithm cannot be expected to be accurate.