Deformable Image Registration Software Research Articles

Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy.

We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties. Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One-tailed Wilcoxon-Mann-Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications. For the registrations performed with the MIM-CC, MIM-MM, MIM-II, and CM-FEM algorithms, their average MDAs are 0.62±0.27, 2.39±1.30, 3.07±2.42, 1.04±0.72mm, and average DSCs are 0.94±0.03, 0.80±0.12, 0.77±0.15, 0.90±0.11, respectively. The p-values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD-JDs and greater than 0.87 for MDA and DSC. Among the three MIM DIR algorithms, MIM-CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.

Precise dosimetric comparison between GAMOS and the collapsed cone convolution algorithm of 4D DOSE accumulated in lung SBRT treatments

BackgroundIt is widely accepted that Monte Carlo dose calculations offers a higher precision that the commercially available dose calculation algorithms. This advantage may be especially relevant for lung Stereotactic Body Radiation Therapy (SBRT), as this is a precise technique applied to an area of big inhomogeneity. PurposeWe conducted a comparative study to reveal the differences between the doses calculated using the Collapsed Cone Convolution algorithm and the GAMOS/Geant4 Monte Carlo calculation for lung cancer patients treated with Stereotactic Body Radiation Therapy on an Elekta Versa HD linac. MethodsFor this study a set of ten patient treatments carried out at the Clínica Universidad de Navarra was selected. Theanalysis is based on the comparison of several dosimetric quantities for the Gross Tumor Volume (GTV) and several OrgansAt Risk (OARs), and also a gamma index calculation with distance-to-agreement set to 2 mm and dose difference to 3%, as recommended by ICRU to assess clinical impact. In order to guarantee a small uncertainty in the Monte Carlo calculation of the dosimetric quantities, we studied in detail the validity of different methods that may be used to determine this uncertainty.To compensate for lung movements, a 4D-Cone-beam Computed Tomography (CBCT) was acquired before treatment, whichallowed us to identify eight respiratory phases using a temporal binning. Using commercial MIM software®, we performed a deformable image registration between the eight CT respiration phases to construct the 4D doses. The same procedure was applied for the Treatment Planning System (TPS) dose files and for the Monte Carlo dose files. ResultsThe differences between the two algorithms reveal the known weaknesses of the Collapsed Cone Convolution (CCC) algorithm for the calculation of lateral doses and in regions of large density change. The comparison between the two algorithms for individual phase doses shows differences up to 5% of the GTV D95 or 3–4 Gy in some OARs, which may have a clinical impact. Nevertheless these differences are reduced for the 4D dose in most quantities under study. ConclusionsComparing the dose calculated with a Collapsed Cone Convolution algorithm with GAMOS/Geant4 for ten patients and eight respiratory phases, we found some differences that could have a clinical impact. When combining the eight temporal phases into a 4D dose using the MIM Deformable Image Registration software, the differences diminished substantially.Our statistical analysis concludes that dose uncertainty in the voxels with a maximum dose below a given percentage guarantees uncertainty in the dosimetric quantities below that figure.

Dosimetric impact of deformable image registration using radiophotoluminescent glass dosimeters with a physical geometric phantom.

To study the dosimetry impact of deformable image registration (DIR) using radiophotoluminescent glass dosimeter (RPLD) and custom developed phantom with various inserts. The phantom was developed to facilitate simultaneous evaluation of geometric and dosimetric accuracy of DIR. Four computed tomography (CT) images of the phantom were acquired with four different configurations. Four volumetric modulated arc therapy (VMAT) plans were computed for different phantom. Two different patterns were applied to combination of four phantom configurations. RPLD dose measurement was combined between corresponding two phantom configurations. DIR-based dose accumulation was calculated between corresponding two CT images with two commercial DIR software and various DIR parameter settings, and an open source software. Accumulated dose calculated using DIR was then compared with measured dose using RPLD. The mean±standard deviation (SD) of dose difference was 2.71±0.23% (range, 2.22%-3.01%) for tumor-proxy and 3.74±0.79% (range, 1.56%-4.83%) for rectum-proxy. The mean±SD of target registration error (TRE) was 1.66±1.36mm (range, 0.03-4.43mm) for tumor-proxy and 6.87±5.49mm (range, 0.54-17.47mm) for rectum-proxy. These results suggested that DIR accuracy had wide range among DIR parameter setting. The dose difference observed in our study was 3% for tumor-proxy and within 5% for rectum-proxy. The custom developed physical phantom with inserts showed potential for accurate evaluation of DIR-based dose accumulation. The prospect of simultaneous evaluation of geometric and dosimetric DIR accuracy in a single phantom may be useful for validation of DIR for clinical use.

Use of Contrast Media During CT-guided Thermal Ablation of Colorectal Liver Metastasis for Procedure Planning is Associated with Improved Immediate Outcomes.

The aim of this study was to analyze the impact of using intra-procedural pre-ablation contrast-enhanced CT prior to percutaneous thermal ablation (pre-ablation CECT) of colorectal liver metastases (CLM) on local outcomes. This retrospective analysis of a prospectively collected liver ablation registry included 144 consecutive patients (median age 57years IQR [49, 65], 60% men) who underwent 173 CT-guided ablation sessions for 250 CLM between October 2015 and March 2020. In addition to oncologic outcomes, technical success was retrospectively evaluated using a biomechanical deformable image registration software for 3D-minimal ablative margin (3D-MAM) quantification. Bayesian regression was used to estimate effects of pre-ablation CECT on residual unablated tumor, 3D-MAM, and local tumor progression-free survival (LTPFS). Pre-ablation CECT was acquired in 71/173 (41%) sessions. Residual unablated tumor was present in one (0.9%) versus nine tumors (6.6%) ablated with versus without using pre-ablation CECT, respectively (p = 0.024). Pre-ablation CECT use decreased the odds of residual disease on first follow-up by 78% (CI95% [5, 86]) and incomplete ablation (3D-MAM ≤ 0mm) by 58% (CI95% [13, 122]). The odds ratio for residual unablated tumor for larger CLM was lower when pre-ablation CECT was used (odds ratio 1.0 with pre-ablation CECT vs. 2.52 without). Pre-ablation CECT use was not associated with improvements on LTPFS. Pre-ablation CECT is associated with improved immediate outcomes by significantly reducing the incidence of residual unablated tumor and by mitigating the risk of incomplete ablation for larger CLM. We recommend performing baseline intra-procedural pre-ablation CECT as a standard imaging protocol. Level 3 (retrospective cohort study).

Clinical use, challenges, and barriers to implementation of deformable image registration in radiotherapy - the need for guidance and QA tools.

Objective:The aim of this study was to evaluate the current status of the clinical use of deformable image registration (DIR) in radiotherapy and to gain an understanding of the challenges faced by centres in clinical implementation of DIR, including commissioning and quality assurance (QA), and to determine the barriers faced. The goal was to inform whether additional guidance and QA tools were needed.Methods:A survey focussed on clinical use, metrics used, how centres would like to use DIR in the future and challenges faced, was designed and sent to 71 radiotherapy centres in the UK. Data were gathered specifically on which centres we using DIR clinically, which applications were being used, what commissioning and QA tests were performed, and what barriers were preventing the integration of DIR into the clinical workflow. Centres that did not use DIR clinically were encouraged to fill in the survey and were asked if they have any future plans and in what timescale.Results:51 out of 71 (70%) radiotherapy centres responded. 47 centres reported access to a commercial software that could perform DIR. 20 centres already used DIR clinically, and 22 centres had plans to implement an application of DIR within 3 years of the survey. The most common clinical application of DIR was to propagate contours from one scan to another (19 centres). In each of the applications, the types of commissioning and QA tests performed varied depending on the type of application and between centres. Some of the key barriers were determining when a DIR was satisfactory including which metrics to use, and lack of resources.Conclusion:The survey results highlighted that there is a need for additional guidelines, training, better tools for commissioning DIR software and for the QA of registration results, which should include developing or recommending which quantitative metrics to use.Advances in knowledge:This survey has given a useful picture of the clinical use and lack of use of DIR in UK radiotherapy centres. The survey provided useful insight into how centres commission and QA DIR applications, especially the variability among centres. It was also possible to highlight key barriers to implementation and determine factors that may help overcome this which include the need for additional guidance specific to different applications, better tools and metrics.

Evaluation of a deformable image registration software for use in treatment planning of previously irradiated patients

Due to advances in oncology therapies, patients are increasingly presenting for treatment of new lesions. These new lesions often reside close to areas of previous irradiation which poses significant challenges for treatment planning. To address the challenges of re-irradiation, a software package that can be used to perform rigid and deformable registrations of images and contours, and propagate treatment dose across multiple image sets and modalities was acquired, Velocity (Varian Medical Systems).

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.

An international survey on the clinical use of rigid and deformable image registration in radiotherapy.

ObjectivesRigid image registration (RIR) and deformable image registration (DIR) are widely used in radiotherapy. This project aims to capture current international approaches to image registration.MethodsA survey was designed to identify variations in use, resources, implementation, and decision‐making criteria for clinical image registration. This was distributed to radiotherapy centers internationally in 2018.ResultsThere were 57 responses internationally, from the Americas (46%), Australia/New Zealand (32%), Europe (12%), and Asia (10%). Rigid image registration and DIR were used clinically for computed tomography (CT)‐CT registration (96% and 51%, respectively), followed by CT‐PET (81% and 47%), CT‐CBCT (84% and 19%), CT‐MR (93% and 19%), MR‐MR (49% and 5%), and CT‐US (9% and 0%). Respondent centers performed DIR using dedicated software (75%) and treatment planning systems (29%), with 84% having some form of DIR software. Centers have clinically implemented DIR for atlas‐based segmentation (47%), multi‐modality treatment planning (65%), and dose deformation (63%). The clinical use of DIR for multi‐modality treatment planning and accounting for retreatments was considered to have the highest benefit‐to‐risk ratio (69% and 67%, respectively).ConclusionsThis survey data provides useful insights on where, when, and how image registration has been implemented in radiotherapy centers around the world. DIR is mainly in clinical use for CT‐CT (51%) and CT‐PET (47%) for the head and neck (43–57% over all use cases) region. The highest benefit‐risk ratio for clinical use of DIR was for multi‐modality treatment planning and accounting for retreatments, which also had higher clinical use than for adaptive radiotherapy and atlas‐based segmentation.

Development of a dynamic deformable thorax phantom for the quality management of deformable image registration

The purpose of this study was to develop a novel dynamic deformable thorax phantom for deformable image registration (DIR) quality assurance (QA) and to verify as a tool for commissioning and DIR QA. 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. The inner phantom consists of deformable, 20mm thick disk-shaped sponges. To evaluate the physical characteristics of the phantom, we evaluated its image quality and deformation. DIR accuracies were evaluated using the three types of commercially DIR software (MIM, RayStation, and Velocity AI) to test the feasibility of this phantom. We used different DIR parameters to test the impact of parameters on DIR accuracy in various phantom settings. To evaluate DIR accuracy, a target registration error (TRE) was calculated using the anatomical landmark points. The three locations (i.e., distal, middle, and proximal positions) had different displacement amounts. This result indicated that the inner phantom was not moved but deformed. In cases with different phantom settings and marker settings, the ranges of the average TRE were 0.63-15.60mm (MIM). In cases with different DIR parameters settings, the ranges of the average TRE were as follows: 0.73-7.10mm (MIM), 8.25-8.66mm (RayStation), and 8.26-8.43mm (Velocity). These results suggest that our phantom could evaluate the detailed DIR behaviors with TRE. Therefore, this is indicative of the potential usefulness of our phantom in DIR commissioning and QA.

Analysis of dose using CBCT and synthetic CT during head and neck radiotherapy: A single centre feasibility study.

The study aimed to assess the suitability of deformable image registration (DIR) software to generate synthetic CT (sCT) scans for dose verification during radiotherapy to the head and neck. Planning and synthetic CT dose volume histograms were compared to evaluate dosimetric changes during the treatment course. Eligible patients had locally advanced (stage III, IVa and IVb) oropharyngeal cancer treated with primary radiotherapy. Weekly CBCT images were acquired post treatment at fractions 1, 6, 11, 16, 21 and 26 over a 30 fraction treatment course. Each CBCT was deformed with the planning CT to generate a sCT which was used to calculate the dose at that point in the treatment. A repeat planning CT2 was acquired at fraction 16 and deformed with the fraction 16 CBCT to compare differences between the calculations mid-treatment. 20 patients were evaluated generating 138 synthetic CT sets. The single fraction mean dose to PTV_HR between the synthetic and planning CT did not vary, although dose to 95% of PTV_HR was smaller at week 6 compared to planning (difference 2.0%, 95% CI (0.8 to 3.1), p = 0.0). There was no statistically significant difference in PRV_brainstem or PRV_spinal cord maximum dose, although greater variation using the sCT calculations was reported. The mean dose to structures based on the fraction 16 sCT and CT2 scans were similar. Synthetic CT provides comparable dose calculations to those of a repeat planning CT; however the limitations of DIR must be understood before it is applied within the clinical setting.

Database of Radiotherapy Plan Image for Deformable Image Registration

Guidelines require commissioning for deformable image registration (DIR) software before clinical use. The accuracy of DIR software depends upon data used. If common datasets for the DIR commissioning are available, the DIR results using the common datasets would be useful as an accuracy benchmark. Thus, the DIR-database (DIR-DB) was developed for DIR accuracy check and was open to access, which included radiotherapy plan data. This study was approved by Institutional Review Board (IRB). The DIR-DB recorded radiotherapy plans which had been finished on June 2017 and which at least two radiotherapy plans were built for a case in a treatment course. Cone-beam computed tomography (CBCT) images for patient setup were also collected and recorded in the DIR-DB, if it is available. All recorded data were anonymized and were allowed to access by users in Japan with the IRB approval. The accuracy metrics of DIR; Hausdorff distance, mean distance to agreement, Dice similarity coefficient, Jaccard were put up on the DIR-DB web site. The number of recorded cases were 11 cases for head and neck, 16 cases for thorax, 7 cases for abdomen, 8 cases for pelvis and 6 cases for prostate treated with brachytherapy. The number of case for CBCT was 17 cases. It was meaningful for DIR accuracy check in Japan that the DIR-DB and DIR results using the data in the database were released.

Improvement Prediction on Contour Deformation Accuracy Using Deformable Image Registration Results Compared to Rigid Image Registration Results

The aim of this study was to analyze improvement prediction on contour deformation accuracy using deformable image registration (DIR) results compared to rigid image registration (RIR) results. Radiotherapy plans for 31 cases (seven head and neck cases, 10 chest cases, six abdomen cases and eight female pelvis cases) from the privately open database for DIR were used. These cases used at least two radiotherapy plans, and registration was performed using two plans, not only for one case but also for different cases. The DIR and RIR were performed using the DIR software MIM Maestro (MIM software Inc., Cleveland, USA). The registration results for the following organs were analyzed: eye balls, optic nerves, brain stem, spinal cord and right and left parotid glands for head and neck; right and left lungs for chest; liver and right and left kidneys for abdomen; and rectum and bladder for pelvis. Dice similarity coefficient (DSC) for the organs was calculated from the results of RIR and DIR. The improvement in the DSC was observed. DIR improved the DSC values by more than 0.2 for simple shapes, well-defined boundaries and large volumes such as eye balls, brain stem, lungs and liver. The minimum DSC for these organs was approximately 0.7. The improvement in DSC for the organs eye balls, brain stem, lungs and liver had ceiling values 0.95, 0.90, 1.0 and 1.0, respectively. DSC for the spinal cord, parotid gland, bladder and kidney also improved by DIR compared to RIR; however, DIR could not improve the DSC value for rectum compared to RIR because of a large difference in the position, shape and size due to stool and gas.

Factual survey of the clinical use of deformable image registration software for radiotherapy in Japan

Deformable image registration (DIR) has recently become commercially available in the field of radiotherapy. However, there was no detailed information regarding the use of DIR software at each medical institution. Thus, in this study, we surveyed the status of the clinical use of DIR software for radiotherapy in Japan. The Japan Society of Medical Physics and the Japanese Society for Radiation Oncology mailing lists were used to announce this survey. The questionnaire was created by investigators working under the research grant of the Japanese Society for Radiation Oncology (2017–2018) and intended for the collection of information regarding the use of DIR in radiotherapy. The survey was completed by 161 institutions in Japan. The survey results showed that dose accumulation was the most frequent purpose for which DIR was used in clinical practice (73%). Various commissioning methods were performed, although they were not standardized. Qualitative evaluation with actual patient images was the most commonly used method (28%), although 30% of the total number of responses (42% of institutions) reported that they do not perform commissioning. We surveyed the current status of clinical use of DIR software for radiotherapy in Japan for the first time. Our results indicated that a certain number of institutions used DIR software for clinical practice, and various commissioning methods were performed, although they were not standardized. Taken together, these findings highlight the need for a technically unified approach for commissioning and quality assurance for the use of DIR software in Japan.

Effect of Deformation Methods on the Accuracy of Deformable Image Registration From Kilovoltage CT to Tomotherapy Megavoltage CT.

Introduction:The registration accuracy of megavoltage computed tomography images is limited by low image contrast when compared to that of kilovoltage computed tomography images. Such issues may degrade the deformable image registration accuracy. This study evaluates the deformable image registration from kilovoltage to megavoltage images when using different deformation methods and assessing nasopharyngeal carcinoma patient images.Methods:The kilovoltage and the megavoltage images from the first day and the 20th fractions of the treatment day of 12 patients with nasopharyngeal carcinoma were used to evaluate the deformable image registration application. The deformable image registration image procedures were classified into 3 groups, including kilovoltage to kilovoltage, megavoltage to megavoltage, and kilovoltage to megavoltage. Three deformable image registration methods were employed using the deformable image registration and adaptive radiotherapy software. The validation was compared by volume-based, intensity-based, and deformation field analyses.Results:The use of different deformation methods greatly affected the deformable image registration accuracy from kilovoltage to megavoltage. The asymmetric transformation with the demon method was significantly better than other methods and illustrated satisfactory value for adaptive applications. The deformable image registration accuracy from kilovoltage to megavoltage showed no significant difference from the kilovoltage to kilovoltage images when using the appropriate method of registration.Conclusions:The choice of deformation method should be considered when applying the deformable image registration from kilovoltage to megavoltage images. The deformable image registration accuracy from kilovoltage to megavoltage revealed a good agreement in terms of intensity-based, volume-based, and deformation field analyses and showed clinically useful methods for nasopharyngeal carcinoma adaptive radiotherapy in tomotherapy applications.

Evaluating the Accuracy of Commercial Deformable Image Registration Software for Real Patient Images Using Anthropomorphic Modeling

Deformable image registration (DIR) is widely used nowadays to register images acquired throughout the radiation oncology treatment course for target delineation, dose accumulation, and adaptive planning. However, the accuracy of commercial DIR software remains questionable and there lacks a “ground truth” to benchmark DIR software for real patient images. We introduced a new method based on anthropomorphic modeling to provide accurate DIR for head and neck patients and evaluated the accuracy of commercial DIR software. Four head and neck patients from the Cancer Imaging Archive, Collection Head-Neck Cetuximab were selected to demonstrate the concept. Each patient had two PETCTs and a planning CT taken with the patient at different positions. Two patients had arms up in PETCT and down in planning CT. The bony structures in each CT were automatically segmented into individual bone groups (e.g. each vertebra) using image processing, clustering, and pattern matching. Model-based DIR rigidly registers each bone group using Iterative Closest Point algorithm then deforms soft tissue with the bones using finite element method. Model-based DIR can be considered as the “truth” of DIR at the bones. The accuracy of rigid registrations and four commercial DIR software were evaluated. The mean absolute errors of DIR for multiple rigid registration (the best of three rigid registrations aligning to base of skull, mid-neck, and lower neck/shoulder respectively), DIR software A (constrained, intensity based, free-form algorithm), B (hybrid algorithm designed for head and neck), C (multi-resolution B-Spline algorithm) and D (hybrid algorithm using image intensity and region of interest constraints) were listed in Table 1.Abstract TU_38_3272; Table 1Mean absolute errors of multiple rigid registration and four DIR software for head and neck patient imagesMean absolute error (cm)HeadJawSpineClavicleScapulaeHumerus (Arms up/down)Humerus (Arm down)SternumRibsMultiple rigid registrations0.260.510.721.892.147.922.430.610.77Software A0.570.250.611.591.506.551.580.350.65Software B0.640.300.631.161.376.830.920.300.78Software C0.430.350.851.471.515.221.170.561.07Software D0.660.320.741.311.885.841.680.450.85 Open table in a new tab Anthropomorphic modeling provides the truth of DIR for bones in head and neck patient images which can be used to measure the accuracy of commercial DIR software. For the patients studied, the mean DIR errors were on the order of millimeters except for large posture change (arms up/down). The performance of DIR software was comparable with each other and with multiple rigid registrations despite the difference in the algorithm used. There is not an algorithm that outperforms others in all regions.

P200] Deformable Image Registration performances in head and neck patients: Impact of daily imaging quality

Purpose To investigate the accuracy and robustness of commercial algorithms for Deformable Image Registration (DIR) to propagate regions of interest (ROIs), against image quality typical of Cone Beam CT (CBCT) and Megavoltage Cone-beam CT (MVCT) images, using computational phantoms based on real head and neck (HN) patient images. Methods Eleven institutions joined the study with five commercial DIR solutions. Three real patient data-sets from different on board imaging devices (OBI by Varian, XVI by Elekta, and MVCT by Tomotherapy) were used with different image quality. Two specific Deformation Vector Fields (DVF) with realistic level of deformations, were generated with a dedicated software and then applied to each data-set. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Prognostic factors of DIR performances were carried out. Results Based on more than 700 DIR-mapped ROIs the 4-way ANOVA analysis states that DIR algorithm, image quality and organ type are significant predictors of DIR performances. A slightly dependence from applied DVF is detected (p = 0.035) with a post hoc test. Putting together all CI data of all institutions for the first DVF, the mean CI was 0.88 ± 0.05 (1 SD), and the mean MDC is 0.38 ± 0.96 (1 SD); for the second DVF, the mean CI was 0.86 ± 0.11 (1 SD), and the mean MDC is 0.43 ± 1.08 (1 SD). The thyroid, located near the FOV border, was the underperformer organ with large errors (unacceptable). All other organs were mapped with an acceptable accuracy (at voxel size level). One imaging system performs statistically better than the others, and one algorithm produced unsatisfactory results respect all the others. Conclusions This work studies the impact of CBCT and MVCT image quality on DIR performance in a ground truth provided scenario. Clinical issues like Adaptive Radiation Therapy (ART) need accurate and robust DIR software. The reported result highlight that in HN district the image quality and the implemented algorithm are strictly related to DIR performances.

Design and development of a nonrigid phantom for the quantitative evaluation of DIR-based mapping of simulated pulmonary ventilation.

The validation of deformable image registration (DIR)-based pulmonary ventilation mapping is time consuming and prone to inaccuracies and is also affected by deformation parameters. In this study, we developed a nonrigid phantom as a quality assurance (QA) tool that simulates ventilation to evaluate DIR-based images quantitatively. The phantom consists of an acrylic cylinder filled with polyurethane foam designed to simulate pulmonic alveoli. A polyurethane membrane is attached to the inferior end of the phantom to simulate the diaphragm. In addition, tracheobronchial-tree-shaped polyurethane tubes are inserted through the foam and converge outside the phantom to simulate the trachea. Solid polyurethane is also used to model arteries, which closely follow the model airways. Two three-dimensional (3D) CT scans were performed during exhalation and inhalation phases using xenon (Xe) gas as the inhaled contrast agent. The exhalation 3D-CT image is deformed to an inhalation 3D-CT image using our in-house program based on the NiftyReg open-source package. The target registration error (TRE) between the two images was calculated for 16 landmarks located in the simulated lung volume. The DIR-based ventilation image was generated using Jacobian determinant (JD) metrics. Subsequently, differences in the Hounsfield unit (HU) values between the two images were measured. The correlation coefficient between the JD and HU differences was calculated. In addition, three 4D-CT scans are performed to evaluate the reproducibility of the phantom motion and Xe gas distribution. The phantom exhibited a variety of displacements for each landmark (range: 1-20 mm). The reproducibility analysis indicated that the location differences were <1 mm for all landmarks, and the HU variation in the Xe gas distribution was close to zero. The mean TRE in the evaluation of spatial accuracy according to the DIR software was 1.47 ± 0.71 mm (maximum: 2.6 mm). The relationship between the JD and HU differences had a large correlation (R = -0.71) for the DIR software. The phantom implemented new features, namely, deformation and simulated ventilation. To assess the accuracy of the DIR-based mapping of the simulated pulmonary ventilation, the phantom allows for simulation of Xe gas wash-in and wash-out. The phantom may be an effective QA tool, because the DIR algorithm can be quickly changed and its accuracy evaluated with a high degree of precision.

Commissioning and validation of commercial deformable image registration software for adaptive contouring

PurposeTo report the commissioning and validation of deformable image registration(DIR) software for adaptive contouring. MethodsDIR (SmartAdapt®v13.6) was validated using two methods namely contour propagation accuracy and landmark tracking, using physical phantoms and clinical images of various disease sites. Five in-house made phantoms with various known deformations and a set of 10 virtual phantoms were used. Displacement in lateral, anterio-posterior (AP) and superior-inferior (SI) direction were evaluated for various organs and compared with the ground truth. Four clinical sites namely, brain (n = 5), HN (n = 9), cervix (n = 18) and prostate (n = 23) were used. Organs were manually delineated by a radiation oncologist, compared with the deformable image registration (DIR) generated contours. 3D slicer v4.5.0.1 was used to analyze Dice Similarity Co-efficient (DSC), shift in centre of mass (COM) and Hausdorff distances Hf95%/avg. ResultsMean (SD) DSC, Hf95% (mm), Hfavg (mm) and COM of all the phantoms 1–5 were 0.84 (0.2) mm, 5.1 (7.4) mm, 1.6 (2.2) mm, and 1.6 (0.2) mm respectively. Phantom-5 had the largest deformation as compared to phantoms 1–4, and hence had suboptimal indices. The virtual phantom resulted in consistent results for all the ROIs investigated. Contours propagated for brain patients were better with a high DSC score (0.91 (0.04)) as compared to other sites (HN: 0.84, prostate: 0.81 and cervix 0.77). A similar trend was seen in other indices too. The accuracy of propagated contours is limited for complex deformations that include large volume and shape change of bladder and rectum respectively. Visual validation of the propagated contours is recommended for clinical implementation. ConclusionThe DIR algorithm was commissioned and validated for adaptive contouring.

Comparison of rigid and deformable image registration accuracy of the liver during long-term transition after proton beam therapy

Objective: To identify the previously irradiated region is important in re-irradiation. We investigated the long-term transition of geometrical accuracy of a commercially available rigid and deformable image registration (RIR, DIR) software product, MIM Maestro, in livers of patients who underwent proton beam therapy (PBT). Methods: Image registration of pre-treatment and post-treatment CT was performed in 15 liver tumor patients who received PBT and follow up post-treatment CT examination was performed over time (2-7 times each, a total of 54 times). We performed RIR and DIR of pre-treatment CT and compared the post-treatment CT by calculating the similarity of whole liver (Dice similarity coefficient: DSC) and dislocation of metallic markers implanted close to the tumor (fiducial registration error). Results: In the plain CT, the DSC was bigger with RIR in 1 case and DIR in 53 cases (0.83 ± 0.07 vs. 0.92 ± 0.04), then consistently bigger with DIR in 14 patients. The fiducial registration error was smaller with RIR in 10 cases, DIR in 43 cases, and equal error in 1 case (11.8 ± 6.9 vs. 7.6 ± 9.7 mm), then consistently smaller with DIR in 10 patients. In the contrast-enhanced CT, the DSC was consistently bigger with DIR in all 15 patients, 54 cases in all (0.84 ± 0.06 vs. 0.92 ± 0.03). The fiducial registration error was smaller with RIR in 7 cases and DIR in 47 cases (11.0 ± 6.3 vs. 6.3 ± 7.3 mm), then consistently smaller with DIR in 12 patients. Conclusion: The DIR performance of MIM is therefore superior to that of RIR and its advantage is independent the term after PBT.

Propagation of registration uncertainty during multi-fraction cervical cancer brachytherapy

Multi-fraction cervical cancer brachytherapy is a form of image-guided radiotherapy that heavily relies on 3D imaging during treatment planning, delivery, and quality control. In this context, deformable image registration can increase the accuracy of dosimetric evaluations, provided that one can account for the uncertainties associated with the registration process. To enable such capability, we propose a mathematical framework that first estimates the registration uncertainty and subsequently propagates the effects of the computed uncertainties from the registration stage through to the visualizations, organ segmentations, and dosimetric evaluations. To ensure the practicality of our proposed framework in real world image-guided radiotherapy contexts, we implemented our technique via a computationally efficient and generalizable algorithm that is compatible with existing deformable image registration software. In our clinical context of fractionated cervical cancer brachytherapy, we perform a retrospective analysis on 37 patients and present evidence that our proposed methodology for computing and propagating registration uncertainties may be beneficial during therapy planning and quality control. Specifically, we quantify and visualize the influence of registration uncertainty on dosimetric analysis during the computation of the total accumulated radiation dose on the bladder wall. We further show how registration uncertainty may be leveraged into enhanced visualizations that depict the quality of the registration and highlight potential deviations from the treatment plan prior to the delivery of radiation treatment. Finally, we show that we can improve the transfer of delineated volumetric organ segmentation labels from one fraction to the next by encoding the computed registration uncertainties into the segmentation labels.