Evaluation Of Deformable Image Registration Research Articles

Impact of CT scan parameters on deformable image registration accuracy using deformable thorax phantom.

The purpose of this study was to evaluate the deformable image registration (DIR) accuracy using various CT scan parameters with deformable thorax phantom. Our developed deformable thorax phantom (Dephan, Chiyoda Technol Corp, Tokyo, Japan) was used. The phantom consists of a base phantom, an inner phantom, and a motor-derived piston. The base phantom is an acrylic cylinder phantom with a diameter of 180mm, which simulates the chest wall. The inner phantom consists of deformable, 20mm thick disk-shaped sponges with 48 Lucite beads and 48 nylon cross-wires which simulate the vascular and bronchial bifurcations of the lung. Peak-exhale and peak-inhale images of the deformable phantom were acquired using a CT scanner (Aquilion LB, TOSHIBA). To evaluate the impact of CT scan parameters on DIR accuracy, we used the four tube voltages (80, 100, 120, and 135kV) and six reconstruction algorithms (FC11, FC13, FC15, FC41, FC44, and FC52). Intensity-based DIR was performed between the two images using MIM Maestro (MIM software, Cleveland, USA). Fiducial markers (beads and cross-wires) based target registration error (TRE) was used for quantitative evaluation of DIR. In case with different tube voltages, the range of average TRE were 4.44-5.69mm (reconstruction algorithm: FC13). In case with different reconstruction algorithms, the range of average TRE were 4.26-4.59mm (tube voltage: 120kV). The TRE were differed by up to 3.0mm (3.96-6.96mm) depending on the combination of tube voltage and reconstruction algorithm. Our result indicated that CT scan parameters had moderate impact of TRE, especially for reconstruction algorithms for the deformable thorax phantom.

Evaluating the Quality of Patient-Specific Deformable Image Registration in Adaptive Radiotherapy Using a Digitally Enhanced Head and Neck Phantom

Despite the availability of national and international guidelines, an accurate and efficient, patient-specific, deformable image registration (DIR) validation methodology is not yet established, and several groups have found an incompatibility of the various digital phantoms with the commercial systems. To evaluate the quality of the computed tomography (CT) and on-board cone-beam CT (CBCT) DIRs, a novel methodology was developed and tested on 10 head and neck (HN) patients, using CT and CBCT anthropomorphic HN phantom images, digitally reprocessed to include the common organs at risk. Reference DVFs (refDVFs) were generated from the clinical patient CT-CBCT fused images using an independent registration software. The phantom CT images were artificially deformed, using the refDVFs, and registered with the phantom CBCT images, using the clinical registration software, generating a test DVF (testDVF) dataset. The clinical plans were recalculated on the daily patient ‘deformed’ CTs, and the dose maps transferred to the patient-planning CT, using both the refDVF and testDVF. The spatial and dosimetric errors were quantified and the DIR performance evaluated using an established operative tolerance level. The method showed the ability to quantify the DIR spatial errors and assess their dose impact at the voxel level and could be applied to patient-specific DIR evaluation during adaptive HN radiotherapy in routine practice.

Variability in commercially available deformable image registration: A multi‐institution analysis using virtual head and neck phantoms

PurposeThe purpose of this study was to evaluate the performance of three common deformable image registration (DIR) packages across algorithms and institutions.Methods and MaterialsThe Deformable Image Registration Evaluation Project (DIREP) provides ten virtual phantoms derived from computed tomography (CT) datasets of head‐and‐neck cancer patients over a single treatment course. Using the DIREP phantoms, DIR results from 35 institutions were submitted using either Velocity, MIM, or Eclipse. Submitted deformation vector fields (DVFs) were compared to ground‐truth DVFs to calculate target registration error (TRE) for six regions of interest (ROIs). Statistical analysis was performed to determine the variability between each DIR software package and the variability of users within each algorithm.ResultsOverall mean TRE was 2.04 ± 0.35 mm for Velocity, 1.10 ± 0.29 mm for MIM, and 2.35 ± 0.15 mm for Eclipse. The MIM mean TRE was significantly different than both Velocity and Eclipse for all ROIs. Velocity and Eclipse mean TREs were not significantly different except for when evaluating the registration of the cord or mandible. Significant differences between institutions were found for the MIM and Velocity platforms. However, these differences could be explained by variations in Velocity DIR parameters and MIM software versions.ConclusionsAverage TRE was shown to be <3 mm for all three software platforms. However, maximum errors could be larger than 2 cm indicating that care should be exercised when using DIR. While MIM performed statistically better than the other packages, all evaluated algorithms had an average TRE better than the largest voxel dimension. For the phantoms studied here, significant differences between algorithm users were minimal suggesting that the algorithm used may have more impact on DIR accuracy than the particular registration technique employed. A significant difference in TRE was discovered between MIM versions showing that DIR QA should be performed after software upgrades as recommended by TG‐132.

A multivariable study of deformable image registration evaluation metrics in 4DCT of thoracic cancer patients

Deformable image registration (DIR) accuracy is often validated using manually identified landmarks or known deformations generated using digital or physical phantoms. In daily practice, the application of these approaches is limited since they are time-consuming or require additional equipment. An alternative is the use of metrics automatically derived from the registrations, but their interpretation is not straightforward. In this work we aim to determine the suitability of DIR-derived metrics to validate the accuracy of 4 commonly used DIR algorithms. First, we investigated the DIR accuracy using a landmark-based metric (target registration error (TRE)) and a digital phantom-based metric (known deformation recovery error (KDE)). 4DCT scans of 16 thoracic cancer patients along with corresponding pairwise anatomical landmarks (AL) locations were collected from two public databases. Digital phantoms with known deformations were generated by each DIR algorithm to test all other algorithms and compute KDE. TRE and KDE were evaluated at AL. KDE was additionally quantified in coordinates randomly sampled (RS) inside the lungs. Second, we investigated the associations of 5 DIR-derived metrics (distance discordance metric (DDM), inverse consistency error (ICE), transitivity (TE), spatial (SS) and temporal smoothness (TS)) with DIR accuracy through uni- and multivariable linear regression models. TRE values were found higher compared to KDE values and these varied depending on the phantom used. The algorithm with the best accuracy achieved average values of TRE = 1.1 mm and KDE ranging from 0.3 to 0.8 mm. DDM was the best predictor of DIR accuracy, with moderate correlations (R 2 < 0.61). Poor correlations were obtained at AL for algorithms with better accuracy, which improved when evaluated at RS. Only slight correlation improvement was obtained with a multivariable analysis (R 2 < 0.64). DDM can be a useful metric to identify inaccuracies for different DIR algorithms without employing landmarks or digital phantoms.

PO-1629: A quantitative evaluation of deformable image registration based on Halcyon MVCBCT images

PO-1629: A quantitative evaluation of deformable image registration based on Halcyon MVCBCT images

Study of novel deformable image registration in myocardial perfusion single-photon emission computed tomography.

In the present study, deformable image registration (DIR) technology was applied to gated myocardial perfusion single-photon emission computed tomography (G-MPS) reconstructed images in distorting all image phases. We aimed to define a new method of end-diastole compatible image registration and verify the clinical usability for any cardiac volume. Projection images were created using the Monte Carlo simulation. All image phases were shifted to fit the end-diastole phase by applying DIR to images that were reconstructed from projection images. Defect ratios were subsequently evaluated using the simulated images of the anterior wall simulated ischemia. Furthermore, receiver operating characteristic (ROC) analysis was performed for the clinical evaluation of DIR and nongated images. To this end, normal volume and small hearts of 33 patients without coronary artery disease and 55 with single vessel disease (coronary stenosis > 70%) were evaluated. Defect ratio analysis for voxel values of 25-100 were 75.7-21.3 for nongated and 74.7-15.6 for DIR images. For normal cardiac volume, the area under the ROC curve was 0.901 ± 0.088 for nongated and 0.925 ± 0.073 for DIR images (P = 0.078). Finally, for small cardiac volume, the area under the ROC curve was 0.651 ± 0.124 for nongated and 0.815 ± 0.119 for DIR (P < 0.01). In the present study, we developed a new registration technique by applying DIR to G-MPS images. When optimal DIR conditions were applied, the resolution of G-MPS images was improved. Furthermore, the diagnostic ability was improved in cases of small cardiac volume.

A quantitative evaluation of deformable image registration based on MV cone beam CT images: Impact of deformation magnitudes and image modalities

Background and purposeTo evaluate the impact of deformation magnitude and image modality on deformable-image-registration (DIR) accuracy using Halcyon megavoltage cone beam CT images (MVCBCT). Materials and methodsPlanning CT images of an anthropomorphic Head phantom were aligned rigidly with MVCBCT and re-sampled to achieve the same resolution, denoted as pCT. MVCBCT was warped with twenty simulated pre-known virtual deformation fields (Ti, i = 1–20) with increasing deformation magnitudes, yielding warped CBCT (wCBCT). The pCT and MVCBCT were registered to wCBCT respectively (Multi-modality and Uni-modality DIR), generating deformation vector fields Vi and Vi′ (i = 1–20). Vi and Vi′ were compared with Ti respectively to assess the DIR accuracy geometrically. In addition, Vi, Ti, and Vi′ were applied to pCT, generating deformed CT (dCTi), ground-truth CT (Gi) and deformed CT′ (dCTi′) respectively. The Hounsfield Unit (HU) on these virtual CT images were also compared. ResultsThe mean errors of vector displacement increased with the deformation magnitude. For deformation magnitudes between 2.82 mm and 7.71 mm, the errors of uni-modality DIR were 1.16 mm ~ 1.73 mm smaller than that of multi-modality (p = 0.0001, Wilcoxon signed rank test). DIR could reduce the maximum signed and absolute HU deviations from 70.8 HU to 11.4 HU and 208 HU to 46.2 HU respectively. ConclusionsAs deformation magnitude increases, DIR accuracy continues to deteriorate and uni-modality DIR consistently outperformed multi-modality DIR. DIR-based adaptive radiotherapy utilizing the noisy MVCBCT images is only conditionally applicable with caution.

Automatic large quantity landmark pairs detection in 4DCT lung images.

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. 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. 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±0.45mm with 98% of landmark pairs having a TRE smaller than 2mm 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±0.53mm with 97% of landmark pairs having a TRE smaller than 2mm. 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.

EP-2010 A QA method for evaluation of deformable image registration in head and neck adaptive radiotherapy

EP-2010 A QA method for evaluation of deformable image registration in head and neck adaptive radiotherapy

The VAMPIRE challenge: A multi-institutional validation study of CT ventilation imaging.

CT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities. The VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA-SPECT, and 4 sheep imaged with Xenon-CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B-splines, Free-form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel-wise Spearman coefficient , and Dice similarity coefficients evaluated for low function lung ( ) and high function lung ( ). A total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant-submitted DIR motion fields using the in-house software, VESPIR. The and DSC results reveal a high degree of inter-algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross-modality correlations used a biomechanical model-based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27-0.73) for , 0.52 (0.36-0.67) for , and 0.45 (0.28-0.62) for . All other algorithms exhibited at least one negative value, and/or one DSC value less than 0.5. The VAMPIRE Challenge results demonstrate that the cross-modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a "gold standard," highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.

Technical Note: A method for quality assurance of landmark sets for use in evaluation of deformable image registration accuracy of lung parenchyma.

To develop a quality control method to improve the accuracy of corresponding landmark sets used for deformable image registration (DIR) evaluation in the lung parenchyma. An iterative workflow was developed as a method for quality assurance of landmark sets. Starting with the initial landmark set for a given image pair, a landmark-based deformation was applied to one of the images. A difference image and a color overlay were generated using the deformed image and the other image of the pair. Inspection of these generated images at locations of landmarks allowed for the identification of misplaced landmarks. The observer responsible for creating the initial landmark set was tasked with review and revision of points flagged by the quality assurance procedure. Using the updated landmark sets, the process was repeated until all points were acceptable to the reviewer. Eighteen landmark sets, containing a mean (SD) of 170 (31) landmarks, were created using CT images from non-small cell lung cancer patients exhibiting large geometric changes and atelectasis resolution, making landmark specification challenging. Following the quality assurance procedure, the final landmark sets contained a mean (SD) of 165 (25) landmarks, as points too difficult to match were removed and points were added to regions deficient in landmarks. For landmark sets in which changes were made, maximum and mean differences in landmark positions before and after quality assurance ranged between 8.7-81.5mm and 0.3-9.6mm, respectively. An effective method for improving the accuracy of landmark correspondence was presented. This quality assurance approach enables more accurate evaluation of DIR for lung parenchyma in clinical image pairs in the absence of a ground truth deformation and may be applicable to other feature-rich anatomical sites.

Evaluation of deformable image registration in HDR gynecological brachytherapy

Introduction: In brachytherapy, as in external radiotherapy, image-guidance plays an important role. For GYN treatments it is standard to acquire at least CT images and preferably MR images prior to each treatment and to calculate the dose of the day on each set of images. Then, the dose to the target and to the organs at risk (OAR) is calculated with worst case scenario from ICRU recommendation that propose add maximum doses to the volume of 2cc of every organ (D2cc) from every session. Especially for the OAR, it makes the safe conservative assumption that the same part of the OAR always receives the highest dose. The dose calculated this way often limits the prescription dose or the target coverage. In this work, we propose to investigate an alternative method to assess the cumulative dose for an HDR GYN treatment course; the use of image deformation software to track the deformations of the OAR from one fraction to the next. Materials and Methods: Thirty patients with locally advanced cervical cancer who had been treated by external beam radiotherapy (EBRT) and three to four fractions of HDR brachytherapy (HDR-BT) were introduced to this study. Intensity-based free form deformation algorithm has been used as the primary DIR techniques using MIM software. In the dose accumulationn stage, total doses of rectum and bladder from all brachytherapy fractions have been integrated by deformable image registration techniques. Results: The DICE, jaccard, MDA and HD indexes of deformable registration for bladder and rectum were (0.94±0.03), (0.89±0.06), (0.83±0.3), (9.85±3.8) and (0.86±0.05), (0.78±0.07), (1.46±0.7), (14.4±0.1) respectively compared to rigid registration for bladder and rectum that were (0.74±0.07), (0.61±0.07), (4.2±2.3), (18.73±8.6) and (0.59±0.13), (0.43±0.11), (5.01±1.7), (23.4±1.1) respectively. Dose accumulation of D2cc values were (5.1±2,3)% and (2.7±1.1)% lower for the bladder and rectum respectively compared to adding D2cc of each fractions. Conclusion: Using deformable image registration, based on image intensity, allows to accumulate the dose from different brachytherapy fractions. The results from our study indicate that it is feasible to utilize deformable image registration for dose accumulation.

P262] Verification of Deformable Image Registration (DIR) program for clinical application in head and neck cancer

Purpose Deformable image registration (DIR) program has played an importance role in radiotherapy, for example, image registration from multi-modality and adaptive radiotherapy (ART). Velocity™ v3.2.1 is a commercial software for multipurpose such as DIR, automatic segmentation and PET contouring. The reliability of DIR should be evaluated before clinical used. Nevertheless, most of verification program for DIR are commercial software. This study aims to verify the DIR program in case of there is no commercial verification software. Methods I’mRT phantom™ with 3 inserted fiducial markers and Anthropomorphic Rando phantom™ (head and neck part) with 12 inserted fiducial markers were used for deformable registration evaluation. For evaluation of DIR, the translation (lateral, longitudinal and vertical directions), rotation, pitch and yaw rotation of I’mRT™ and Rando phantom™ were performed for calculation of target registration error (TRE). Eclipse™ treatment planning v13.6 was used for TRE by measuring the center of line profile of fiducial markers among translated phantoms. Nine cases of head and neck cancer CT images were selected for evaluation the TRE and DVH changes of tumor and critical organs. Results Target registration error of rigid phantom was 0.39 ± 0.40 mm, maximum value was 1.0 mm. For anatomical phantom, TRE was 0.93 ± 1.01 mm with maximum value of 5.5 mm. The highest value was found at the border bone region. For clinical CT images, TRE was 2.4 ± 1.76 mm and maximum difference were found at uvula and epiglottis region. For inverse consistency method, the result showed good agreement (percentage differences were less than 3%) of DVH report between initial CT and deformed CT images. Conclusions Velocity™ can be used for image registration in head and neck region. For more reliability, further investigation will be performed in clinical data to achieve consistency evaluation.

An automated, quantitative, and case-specific evaluation of deformable image registration in computed tomography images

A prerequisite for adaptive dose-tracking in radiotherapy is the assessment of the deformable image registration (DIR) quality. In this work, various metrics that quantify DIR uncertainties are investigated using realistic deformation fields of 26 head and neck and 12 lung cancer patients. Metrics related to the physiologically feasibility (the Jacobian determinant, harmonic energy (HE), and octahedral shear strain (OSS)) and numerically robustness of the deformation (the inverse consistency error (ICE), transitivity error (TE), and distance discordance metric (DDM)) were investigated. The deformable registrations were performed using a B-spline transformation model. The DIR error metrics were log-transformed and correlated (Pearson) against the log-transformed ground-truth error on a voxel level. Correlations of r ⩾ 0.5 were found for the DDM and HE. Given a DIR tolerance threshold of 2.0 mm and a negative predictive value of 0.90, the DDM and HE thresholds were 0.49 mm and 0.014, respectively. In conclusion, the log-transformed DDM and HE can be used to identify voxels at risk for large DIR errors with a large negative predictive value. The HE and/or DDM can therefore be used to perform automated quality assurance of each CT-based DIR for head and neck and lung cancer patients.

Evaluation of Deformable Image Registration and Dose Accumulation Using Histogram Matching Algorithm between kVCT and MVCT with Helical Tomotherapy

Purpose: To evaluate the accuracy of deformable image registration (DIR) between the planning kVCT (pCT) and the daily MVCT combined with the histogram matching (HM) algorithm, and evaluate the deformable dose accumulation using a suggested method for adaptive radiotherapy with Helical Tomotharapy (HT). Methods: For five prostate cancer patients (76 Gy/38 Fr) treated with HT in our institution, seven MVCT series (a total of 35 series) acquired weekly were investigated. First, to minimize the effect of different HU values between pCT and MVCT, this image-processing method adjusts HU values between pCT and MVCT images by using image cumulative histograms of HU values, generating an HM-MVCT. Then, the DIR of the pCT to the HM-MVCT was performed, generating a deformed pCT. Finally, deformable dose accumulation was performed toward the pCT image. Results: The accuracy of DIR was significantly improved by using the HM algorithm, compared with non-HM method for several structures (p < 0.05). The mean dice similarity coefficient of the non-HM method was 0.75 ± 0.05, 0.83 ± 0.06, and 0.90 ± 0.04 for the CTV, rectum, and bladder, respectively, while that of the HM method was 0.81 ± 0.06, 0.81 ± 0.04, and 0.92 ± 0.06, respectively. For the deformable dose accumulation, some difference was observed between the two methods, particularly for the small calculated regions, such as rectum V60 and V70. Conclusion: Adapting the HM method can improve the accuracy of DIR. Furthermore, dose calculation using the deformed pCT using HM methods can be an effective tool for adaptive radiotherapy.

44. Deformable image registration for dose accumulation: Principle and evaluation

Most Treatment Planning Systems for adaptive radiotherapy today propose Deformable Image Registration (DIR) tools which enable contour propagation or dose accumulation between images acquired over the course of treatment. While the results of DIR for contour propagation can be easily assessed and eventually manually corrected, the evaluation of DIR for dose accumulation relies on a knowledge of anatomical point correspondences and is therefore much more challenging. This talk will review the methodological principle of DIR for dose accumulation and current evaluation methods. Accumulating dose in a tissue element contained in a voxel of the reference image requires to determine the location of that element in the considered longitudinal images. The role of DIR is to automatically establish those spatial correspondences usually in the form of displacement vector fields (DVF). The first natural way to evaluate DIR results is to deform the longitudinal image onto the reference image based on the DVF and to measure the resulting similarity between the images. However, as demonstrated in previous studies, popular DIR algorithms for contour propagation can lead to high similarity between the deformed and reference images and critically wrong anatomical point correspondences. For this reason, image similarity measures or visual assessment with image fusion tools should be considered in clinical practice only after the local accuracy (in millimeters) has been quantified for a significant number of cases. Among DIR validation methods for dose accumulation, the most reliable one consists of manually identifying pairs of landmarks in the images and to relate the misalignment of those landmark pairs after DIR to the spatial resolution of the dose distributions. This approach can be easily implemented for some anatomical localizations but identifying correspondences on the surface of smooth organs or in large homogeneous tissues is not always feasible. In that case, DIR validation will have to verify that the estimated DVF respect physical considerations such as a reasonable range of local volume variations measured with the Jacobian determinant of the DVF. DIR algorithms can also be tested with images derived from numerical or physical phantoms which provide a ground truth deformation. Phantoms have demonstrated that traditional DIR algorithms could fail to estimate complex deformations such as a sliding motion between organs and justified the need to develop anatomical site-specific models. Finally, a limited number of studies have addressed DIR of longitudinal images presenting inconsistent content (i.e. in case of tumor growth or erosion) and a special attention should be paid in those situations. In parallel of efforts to develop more accurate DIR methods, it is important today to define a commissioning strategy for available DIR solutions to be used in clinical trials involving dose accumulation. In that sense, a Task Group of the American Association of Medical Physicists recently reported its recommendations.

Evaluation of Deformable Image Registration (DIR) Methods for Dose Accumulation in Nasopharyngeal Cancer Patients during Radiotherapy.

IntroductionDeformable image registration (DIR) is used to modify structures according to anatomical changes for observing the dosimetric effect. In this study, megavoltage computed tomography (MVCT) images were used to generate cumulative doses for nasopharyngeal cancer (NPC) patients by various DIR methods. The performance of the multiple DIR methods was analysed, and the impact of dose accumulation was assessed.Patients and methodsThe study consisted of five NPC patients treated with a helical tomotherapy unit. The weekly MVCT images at the 1st, 6th, 11th, 16th, 21st, 26th, and 31st fractions were used to assess the dose accumulation by the four DIR methods. The cumulative dose deviations from the initial treatment plan were analysed, and correlations of these variations with the anatomic changes and DIR methods were explored.ResultsThe target dose received a slightly different result from the initial plan at the end of the treatment. The organ dose differences increased as the treatment progressed to 6.8% (range: 2.2 to 10.9%), 15.2% (range: -1.7 to 36.3%), and 6.4% (range: -1.6 to 13.2%) for the right parotid, the left parotid, and the spinal cord, respectively. The mean uncertainty values to estimate the accumulated doses for all the DIR methods were 0.21 ± 0.11 Gy (target dose), 1.99 ± 0.76 Gy (right parotid), 1.19 ± 0.24 Gy (left parotid), and 0.41 ± 0.04 Gy (spinal cord).ConclusionsAccuracy of the DIR methods affects the estimation of dose accumulation on both the target dose and the organ dose. The DIR methods provide an adequate dose estimation technique for observation as a result of inter-fractional anatomic changes and are beneficial for adaptive treatment strategies.

Construction of a biomechanical head and neck motion model as a guide to evaluation of deformable image registration

The use of deformable image registration methods in the context of adaptive radiotherapy leads to uncertainties in the simulation of the administered dose distributions during the treatment course. Evaluation of these methods is a prerequisite to decide if a plan adaptation will improve the individual treatment. Current approaches using manual references limit the validity of evaluation, especially for low-contrast regions. In particular, for the head and neck region, the highly flexible anatomy and low soft tissue contrast in control images pose a challenge to image registration and its evaluation. Biomechanical models promise to overcome this issue by providing anthropomorphic motion modelling of the patient. We introduce a novel biomechanical motion model for the generation and sampling of different postures of the head and neck anatomy. Motion propagation behaviour of the individual bones is defined by an underlying kinematic model. This model interconnects the bones by joints and thus is capable of providing a wide range of motion. Triggered by the motion of the individual bones, soft tissue deformation is described by an extended heterogeneous tissue model based on the chainmail approach. This extension, for the first time, allows the propagation of decaying rotations within soft tissue without the necessity for explicit tissue segmentation. Overall motion simulation and sampling of deformed CT scans including a basic noise model is achieved within 30 s. The proposed biomechanical motion model for the head and neck site generates displacement vector fields on a voxel basis, approximating arbitrary anthropomorphic postures of the patient. It was developed with the intention of providing input data for the evaluation of deformable image registration.

Validating Dose Uncertainty Estimates Produced by AUTODIRECT: An Automated Program to Evaluate Deformable Image Registration Accuracy.

Deformable image registration is a powerful tool for mapping information, such as radiation therapy dose calculations, from one computed tomography image to another. However, deformable image registration is susceptible to mapping errors. Recently, an automated deformable image registration evaluation of confidence tool was proposed to predict voxel-specific deformable image registration dose mapping errors on a patient-by-patient basis. The purpose of this work is to conduct an extensive analysis of automated deformable image registration evaluation of confidence tool to show its effectiveness in estimating dose mapping errors. The proposed format of automated deformable image registration evaluation of confidence tool utilizes 4 simulated patient deformations (3 B-spline-based deformations and 1 rigid transformation) to predict the uncertainty in a deformable image registration algorithm’s performance. This workflow is validated for 2 DIR algorithms (B-spline multipass from Velocity and Plastimatch) with 1 physical and 11 virtual phantoms, which have known ground-truth deformations, and with 3 pairs of real patient lung images, which have several hundred identified landmarks. The true dose mapping error distributions closely followed the Student t distributions predicted by automated deformable image registration evaluation of confidence tool for the validation tests: on average, the automated deformable image registration evaluation of confidence tool–produced confidence levels of 50%, 68%, and 95% contained 48.8%, 66.3%, and 93.8% and 50.1%, 67.6%, and 93.8% of the actual errors from Velocity and Plastimatch, respectively. Despite the sparsity of landmark points, the observed error distribution from the 3 lung patient data sets also followed the expected error distribution. The dose error distributions from automated deformable image registration evaluation of confidence tool also demonstrate good resemblance to the true dose error distributions. Automated deformable image registration evaluation of confidence tool was also found to produce accurate confidence intervals for the dose–volume histograms of the deformed dose.

Evaluation of deformable image registration between external beam radiotherapy and HDR brachytherapy for cervical cancer with a 3D-printed deformable pelvis phantom

In this study, we developed a 3D-printed deformable pelvis phantom for evaluating spatial DIR accuracy. We then evaluated the spatial DIR accuracies of various DIR settings for cervical cancer. A deformable female pelvis phantom was created based on patient CT data using 3D printing. To create the deformable uterus phantom, we first 3D printed both a model of uterus and a model of the internal cavities of the vagina and uterus. We then made a mold using the 3D printed uterus phantom. Finally, urethane was poured into the mold with the model of the internal cavities in place, creating the deformable uterus phantom with a cavity into which an applicator could be inserted. To create the deformable bladder phantom, we first 3D printed models of the bladder and of the same bladder scaled down by 2mm. We then made a mold using the larger bladder model. Finally, silicone was poured into the mold with the smaller bladder model in place to create the deformable bladder phantom with a wall thickness of 2mm. To emulate the anatomical bladder, water was poured into the created bladder. We acquired phantom image without applicator for EBRT. Then, we inserted the applicator into the phantom to simulate BT. In this situation, we scanned the phantom again to obtain the phantom image for BT. We performed DIR using the two phantom images in two cases: Case A, with full bladder (170ml) in both EBRT and BT images; and Case B with full bladder in the BT image and half-full bladder (100ml) in the EBRT image. DIR was evaluated using Dice similarity coefficients (DSCs) and 31 landmarks for the uterus and 25 landmarks for the bladder. A hybrid intensity and structure DIR algorithm implemented in RayStation with four DIR settings was evaluated. On visual inspection, reasonable agreement in shape of the uterus between the phantom and patient CT images was observed for both EBRT and BT, although some regional disagreements in shape of the bladder and rectum were apparent. The created phantom could reproduce the actual patient's uterus deformation by the applicator. For both Case A and B, large variation was seen in landmark error among the four DIR parameters. In addition, although DSCs were comparable, moderate differences in landmark error existed between the two different DIR parameters selected from the four DIR parameters (i.e., DSC=0.96, landmark error=13.2±5.7 mm vs. DSC=0.97, landmark error=9.7±4.0mm). This result suggests that landmark error evaluation might thus be more effective than DSC for evaluating DIR accuracy. Our developed phantom enabled the evaluation of spatial DIR accuracy for the female pelvic region for the first time. Although the DSCs are high, the spatial errors can still be significant and our developed phantom facilitates their quantification. Our results showed that optimization is needed to identify suitable DIR settings. For determining suitable DIR settings, our method of evaluating spatial DIR accuracy using the 3D-printed phantom may prove helpful.