A three-dimensional head-and-neck phantom for validation of multimodality deformable image registration for adaptive radiotherapy

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.

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

PURPOSE

To study the dosimetry impact of deformable image registration (DIR) using radiophotoluminescent glass dosimeter (RPLD) and custom developed phantom with various inserts.

METHODS

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.

RESULTS

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 ± 1.36 mm (range, 0.03-4.43 mm) for tumor-proxy and 6.87 ± 5.49 mm (range, 0.54-17.47 mm) for rectum-proxy. These results suggested that DIR accuracy had wide range among DIR parameter setting.

CONCLUSIONS

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.

Evaluation of DIR schemes on tumor/organ with progressive shrinkage: accuracy of tumor/organ internal tissue tracking during the radiation treatment

PURPOSE

Accuracy of intratumoral treatment dose accumulation and response assessment highly depends on the accuracy of a DIR method. However, achievable accuracy of the existing DIR methods for tumor/organ with large and progressive shrinkage during the radiotherapy course have not been explored. This study aimed to use a bio-tissue phantom to quantify the achievable accuracy of different DIR schemes.

MATERIALS /METHODS

A fresh porcine liver was used for phantom material. Sixty gold markers were implanted on the surface and inside of the liver. To simulate the progressive radiation-induced tumor/organ shrinkage, the phantom was heated using a microwave oven incrementally from 30s to 200s in 8 phases. For each phase, the phantom was scanned by CT. Two extra image sets were generated from the original images: 1) the image set with overriding the high-density gold markers (feature image); 2) the image set with overriding the entire phantom to the mean soft tissue intensity (featureless image). Ten DIR schemes were evaluated to mimic clinical treatment situations of tumor/critical organ with respect to their surface and internal condition of image features, availability of intermediate feedback images and DIR methods. The internal marker's positions were utilized to evaluate DIR accuracy quantified by target registration error (TRE).

RESULTS

Volume reduction was about 20% ∼ 40% of the initial volume after 90s ∼ 200s of the heating. Without image features on the surface and inside of the phantom, the hybrid-DIR (image-based DIR followed by biomechanical model-based refinement) with the surface constraint achieved the registration TRE from 2.6 ± 1.2mm to 5.3 ± 2.6mm proportional to the %volume shrinkage. Meanwhile, the hybrid-DIR with the surface-marker constraint achieved the TRE from 2.4 ± 1.2mm to 2.6 ± 1.0mm. If both the surface and internal image features would be viable on the feedback images, the achievable accuracy could be minimal with the TRE from 1.6±0.9mm to 1.9 ± 1.2mm.

CONCLUSIONS

Standard DIR methods cannot guarantee intratumoral tissue registration accuracy for tumor/organ with large progressive shrinkage. Achievable accuracy with using the hybrid DIR method is highly dependent on the surface registration accuracy. If the surface registration mean TRE can be controlled within 2mm, the mean TRE of internal tissue can be controlled within 3mm.

Rigid and Deformable Image Registration for Radiation Therapy: A Self-Study Evaluation Guide for NRG Oncology Clinical Trial Participation

PURPOSE

The registration of multiple imaging studies to radiation therapy computed tomography simulation, including magnetic resonance imaging, positron emission tomography-computed tomography, etc. is a widely used strategy in radiation oncology treatment planning, and these registrations have valuable roles in image guidance, dose composition/accumulation, and treatment delivery adaptation. The NRG Oncology Medical Physics subcommittee formed a working group to investigate feasible workflows for a self-study credentialing process of image registration commissioning.

METHODS AND MATERIALS

The American Association of Physicists in Medicine (AAPM) Task Group 132 (TG132) report on the use of image registration and fusion algorithms in radiation therapy provides basic guidelines for quality assurance and quality control of the image registration algorithms and the overall clinical process. The report recommends a series of tests and the corresponding metrics that should be evaluated and reported during commissioning and routine quality assurance, as well as a set of recommendations for vendors. The NRG Oncology medical physics subcommittee working group found incompatibility of some digital phantoms with commercial systems. Thus, there is still a need to provide further recommendations in terms of compatible digital phantoms, clinical feasible workflow, and achievable thresholds, especially for future clinical trials involving deformable image registration algorithms. Nine institutions participated and evaluated 4 commonly used commercial imaging registration software and various versions in the field of radiation oncology.

RESULTS AND CONCLUSIONS

The NRG Oncology Working Group on image registration commissioning herein provides recommendations on the use of digital phantom/data sets and analytical software access for institutions and clinics to perform their own self-study evaluation of commercial imaging systems that might be employed for coregistration in radiation therapy treatment planning and image guidance procedures. Evaluation metrics and their corresponding values were given as guidelines to establish practical tolerances. Vendor compliance for image registration commissioning was evaluated, and recommendations were given for future development.

Development of a physical geometric phantom for deformable image registration credentialing of radiotherapy centers for a clinical trial

PURPOSE

This study aimed to develop a physical geometric phantom for the deformable image registration (DIR) credentialing of radiotherapy centers for a clinical trial and tested the feasibility of the proposed phantom at multiple domestic and international institutions.

METHODS AND MATERIALS

The phantom reproduced tumor shrinkage, rectum shape change, and body shrinkage using several physical phantoms with custom inserts. We tested the feasibility of the proposed phantom using 5 DIR patterns at 17 domestic and 2 international institutions (21 datasets). Eight institutions used the MIM software (MIM Software Inc, Cleveland, OH); seven used Velocity (Varian Medical Systems, Palo Alto, CA), and six used RayStation (RaySearch Laboratories, Stockholm, Sweden). The DIR accuracy was evaluated using the Dice similarity coefficient (DSC) and Hausdorff distance (HD).

RESULTS

The mean and one standard deviation (SD) values (range) of DSC were 0.909 ± 0.088 (0.434-0.984) and 0.909 ± 0.048 (0.726-0.972) for tumor and rectum proxies, respectively. The mean and one SD values (range) of the HD value were 5.02 ± 3.32 (1.53-20.35) and 5.79 ± 3.47 (1.22-21.48) (mm) for the tumor and rectum proxies, respectively. In three patterns evaluating the DIR accuracy within the entire phantom, 61.9% of the data had more than a DSC of 0.8 in both tumor and rectum proxies. In two patterns evaluating the DIR accuracy by focusing on tumor and rectum proxies, all data had more than a DSC of 0.8 in both tumor and rectum proxies.

CONCLUSIONS

The wide range of DIR performance highlights the importance of optimizing the DIR process. Thus, the proposed method has considerable potential as an evaluation tool for DIR credentialing and quality assurance.

Performance of a deformable image registration algorithm for CT and cone beam CT using physical multi-density geometric and digital anatomic phantoms

PURPOSE

To study the accuracy of deformable registration algorithm for CT and cone beam CT (CBCT) using a combination of physical and digital phantoms.

MATERIALS AND METHODS

The physical phantoms consisted of objects over a range of electron densities, shape and sizes. The system was tested for simple and complex scenarios including performance in the presence of metallic artefacts. Clinically present deformations were simulated using a set of five geometric and anatomic virtual phantoms.

RESULTS

The system could not account for large changes in size, shape and Hounsfield units. Deformations of low intensity structures and small objects were highly inaccurate, and errors were prominent for volume reduction scenario than volume growth. The presence of artefacts did alter the performance of the algorithm. Objects of low density and that close to artefacts were affected the most. Overall, deformations to CBCT were poor. In virtual phantoms, the system could not handle gas pockets and deformation errors in inverse direction were higher than that in forward direction.

CONCLUSION

The algorithm was tested for several non-clinical and clinical scenarios. The performance was acceptable for realistic and clinically present deformations. However, it is necessary to tread cautiously for structures with small volumes and large reductions in volume.

Quantifying the accuracy of deformable image registration for cone-beam computed tomography with a physical phantom

PURPOSE

Kilo-voltage cone-beam computed tomography (CBCT) is widely used for patient alignment, contour propagation, and adaptive treatment planning in radiation therapy. In this study, we evaluated the accuracy of deformable image registration (DIR) for CBCT under various imaging protocols with different noise and patient dose levels.

METHODS

A physical phantom previously developed to facilitate end-to-end testing of the DIR accuracy was used with Varian Velocity v4.0 software to evaluate the performance of image registration from CT to CT, CBCT to CT, and CBCT to CBCT. The phantom is acrylic and includes several inserts that simulate different tissue shapes and properties. Deformations and anatomic changes were simulated by changing the rotations of both the phantom and the inserts. CT images (from a head and neck protocol) and CBCT images (from pelvis, head and "Image Gently" protocols) were obtained with different image noise and dose levels. Large inserts were filled with Mobil DTE oil to simulate soft tissue, and small inserts were filled with bone materials. All inserts were contoured before the DIR process to provide a ground truth contour size and shape for comparison. After the DIR process, all deformed contours were compared with the originals using Dice similarity coefficient (DSC) and mean distance to agreement (MDA). Both large and small volume of interests (VOIs) for DIR volume selection were tested by simulating a DIR process that included whole patient image volume and clinical target volumes (CTV) only (for CTVs propagation).

RESULTS

For cross-modality DIR registration (CT to CBCT), the DSC were >0.8 and the MDA were <3 mm for CBCT pelvis, and CBCT head protocols. For CBCT to CBCT and CT to CT, the DIR accuracy was improved relative to the cross-modality tests. For smaller VOIs, the DSC were >0.8 and MDA <2 mm for all modalities.

CONCLUSIONS

The accuracy of DIR depends on the quality of the CBCT image at different dose and noise levels.

Deformable Registration for Dose Accumulation

As deformable image registration makes its way into the clinical routine, the summation of doses from fractionated treatment regimens to evaluate cumulative doses to targets and healthy tissues is also becoming a frequently utilized tool in the context of image-guided adaptive radiotherapy. Accounting for daily geometric changes using deformable image registration and dose accumulation potentially enables a better understanding of dose-volume-effect relationships, with the goal of translation of this knowledge to personalization of treatment, to further enhance treatment outcomes. Treatment adaptation involving image deformation requires patient-specific quality assurance of the image registration and dose accumulation processes, to ensure that uncertainties in the 3D dose distributions are identified and appreciated from a clinical relevance perspective. While much research has been devoted to identifying and managing the uncertainties associated with deformable image registration and dose accumulation approaches, there are still many unanswered questions. Here, we provide a review of current deformable image registration and dose accumulation techniques, and related clinical application. We also discuss salient issues that need to be deliberated when applying deformable algorithms for dose mapping and accumulation in the context of adaptive radiotherapy and response assessment.

Head and Neck Cancer Adaptive Radiation Therapy (ART): Conceptual Considerations for the Informed Clinician

For nearly 2 decades, adaptive radiation therapy (ART) has been proposed as a method to account for changes in head and neck tumor and normal tissue to enhance therapeutic ratios. While technical advances in imaging, planning and delivery have allowed greater capacity for ART delivery, and a series of dosimetric explorations have consistently shown capacity for improvement, there remains a paucity of clinical trials demonstrating the utility of ART. Furthermore, while ad hoc implementation of head and neck ART is reported, systematic full-scale head and neck ART remains an as yet unreached reality. To some degree, this lack of scalability may be related to not only the complexity of ART, but also variability in the nomenclature and descriptions of what is encompassed by ART. Consequently, we present an overview of the history, current status, and recommendations for the future of ART, with an eye toward improving the clarity and description of head and neck ART for interested clinicians, noting practical considerations for implementation of an ART program or clinical trial. Process level considerations for ART are noted, reminding the reader that, paraphrasing the writer Elbert Hubbard, "Art is not a thing, it is a way."

A multiparametric method to assess the MIM deformable image registration algorithm

A quantitative evaluation of the performances of the deformable image registration (DIR) algorithm implemented in MIM-Maestro was performed using multiple similarity indices. Two phantoms, capable of mimicking different anatomical bending and tumor shrinking were built and computed tomography (CT) studies were acquired after applying different deformations. Three different contrast levels between internal structures were artificially created modifying the original CT values of one dataset. DIR algorithm was applied between datasets with increasing deformations and different contrast levels and manually refined with the Reg Refine tool. DIR algorithm ability in reproducing positions, volumes, and shapes of deformed structures was evaluated using similarity indices such as: landmark distances, Dice coefficients, Hausdorff distances, and maximum diameter differences between segmented structures. Similarity indices values worsen with increasing bending and volume difference between reference and target image sets. Registrations between images with low contrast (40 HU) obtain scores lower than those between images with high contrast (970 HU). The use of Reg Refine tool leads generally to an improvement of similarity parameters values, but the advantage is generally less evident for images with low contrast or when structures with large volume differences are involved. The dependence of DIR algorithm on image deformation extent and different contrast levels is well characterized through the combined use of multiple similarity indices.

Characterization of a new physical phantom for testing rigid and deformable image registration

The purpose of this study was to describe a new user-friendly, low-cost phantom that was developed to test the accuracy of rigid and deformable image registration (DIR) systems and to demonstrate the functional efficacy of the new phantom. The phantom was constructed out of acrylic and includes a variety of inserts that simulate different tissue shapes and properties. It can simulate deformations and location changes in patient anatomy by changing the rotations of both the phantom and the inserts. CT scans of this phantom were obtained and used to test the rigid and deformable registration accuracy of the Velocity software. Eight rotation and translation scenarios were used to test the rigid registration accuracy, and 11 deformation scenarios were used to test the DIR accuracy. The mean rotation accuracies in the X-Y (axial) and X-Z (coronal) planes were 0.50° and 0.13°, respectively. The mean translation accuracy was 1 mm in both the X and Y direction and was tested in soft tissue and bone. The DIR accuracies for soft tissue and bone were 0.93 (mean Dice similarity coefficient), 8.3 and 4.5 mm (mean Hausdouff distance), 0.95 and 0.79 mm (mean distance), and 1.13 and 1.12 (mean volume ratio) for soft tissue content (DTE oil) and bone, respectively. The new phantom has a simple design and can be constructed at a low cost. This phantom will allow DIR systems to be effectively and efficiently verified to ensure system performance.

The evaluation of a hybrid biomechanical deformable registration method on a multistage physical phantom with reproducible deformation

BACKGROUND

Advanced clinical applications, such as dose accumulation and adaptive radiation therapy, require deformable image registration (DIR) algorithms capable of voxel-wise accurate mapping of treatment dose or functional imaging. By utilizing a multistage deformable phantom, the authors investigated scenarios where biomechanical refinement method (BM-DIR) may be better than the pure image intensity based deformable registration (IM-DIR).

METHODS

The authors developed a biomechanical-model based DIR refinement method (BM-DIR) to refine the deformable vector field (DVF) from any initial intensity-based DIR (IM-DIR). The BM-DIR method was quantitatively evaluated on a novel phantom capable of ten reproducible gradually-increasing deformation stages using the urethra tube as a surrogate. The internal DIR accuracy was inspected in term of the Dice similarity coefficient (DSC), Hausdorff and mean surface distance as defined in of the urethra structure inside the phantom and compared with that of the initial IM-DIR under various stages of deformation. Voxel-wise deformation vector discrepancy and Jacobian regularity were also inspected to evaluate the output DVFs. In addition to phantom, two pairs of Head&Neck patient MR images with expert-defined landmarks inside parotids were utilized to evaluate the BM-DIR accuracy with target registration error (TRE).

RESULTS

The DSC and surface distance measures of the inner urethra tube indicated the BM-DIR method can improve the internal DVF accuracy on masked MR images for the phases of a large degree of deformation. The smoother Jacobian distribution from the BM-DIR suggests more physically-plausible internal deformation. For H&N cancer patients, the BM-DIR improved the TRE from 0.339 cm to 0.210 cm for the landmarks inside parotid on the masked MR images.

CONCLUSIONS

We have quantitatively demonstrated on a multi-stage physical phantom and limited patient data that the proposed BM-DIR can improve the accuracy inside solid organs with large deformation where distinctive image features are absent.

Online daily assessment of dose change in head and neck radiotherapy without dose-recalculation

BACKGROUND

Head and neck cancers are commonly treated with radiation therapy, but due to possible volume changes, plan adaptation may be required during the course of treatment. Currently, plan adaptations consume significant clinical resources. Existing methods to evaluate the need for plan adaptation requires deformable image registration (DIR) to a new CT simulation or daily cone beam CT (CBCT) images and the recalculation of the dose distribution. In this study, we explore a tool to assist the decision for plan adaptation using a CBCT without re-computation of dose, allowing for rapid online assessment.

METHODS

This study involved 18 head and neck cancer patients treated with CBCT image guidance who had their treatment plan modified based on a new CT simulation (ReCT). Dose changes were estimated using different methods and compared to the current gold standard of using DIR between the planning CT scan (PCT) and ReCT with recomputed dose. The first and second methods used DIR between the PCT and daily CBCT with the planned dose or recalculated dose from the ReCT respectively, with the dose transferred to the CBCT using rigid registration. The necessity of plan adaptation was assessed by the change in dose to 95% of the planning target volume (D95) and mean dose to the parotids.

RESULTS

The treatment plans were adapted clinically for all 18 patients but only 7 actually needed an adaptation yielding 11 unnecessary adaptations. Applying a method using the daily CBCT with the planned dose distribution would have yielded only four unnecessary adaptations and no missed adaptations: a significant improvement from that done clinically.

CONCLUSION

Using the DIR between the planning CT and daily CBCT can flag cases for plan adaptation before every fraction while not requiring a new re-planning CT scan and dose recalculation.

Validation of a deformable MRI to CT registration algorithm employing same day planning MRI for surrogate analysis

Purpose

Validating deformable multimodality image registrations is challenging due to intrinsic differences in signal characteristics and their spatial intensity distributions. Evaluating multimodality registrations using these spatial intensity distributions is also complicated by the fact that these metrics are often employed in the registration optimization process. This work evaluates rigid and deformable image registrations of the prostate in between diagnostic‐MRI and radiation treatment planning‐CT by utilizing a planning‐MRI after fiducial marker placement as a surrogate. The surrogate allows for the direct quantitative analysis that can be difficult in the multimodality domain.

Methods

For thirteen prostate patients, T2 images were acquired at two different time points, the first several weeks prior to planning (diagnostic‐MRI) and the second on the same day as the planning‐CT (planning‐MRI). The diagnostic‐MRI was deformed to the planning‐CT utilizing a commercially available algorithm which synthesizes a deformable image registration (DIR) algorithm from local rigid registrations. The planning‐MRI provided an independent surrogate for the planning‐CT for assessing registration accuracy using image similarity metrics, including Pearson correlation and normalized mutual information (NMI). A local analysis was performed by looking only within the prostate, proximal seminal vesicles, penile bulb, and combined areas.

Results

The planning‐MRI provided an excellent surrogate for the planning‐CT with residual error in fiducial alignment between the two datasets being submillimeter, 0.78 mm. DIR was superior to the rigid registration in 11 of 13 cases demonstrating a 27.37% improvement in NMI (P < 0.009) within a regional area surrounding the prostate and associated critical organs. Pearson correlations showed similar results, demonstrating a 13.02% improvement (P < 0.013).

Conclusion

By utilizing the planning‐MRI as a surrogate for the planning‐CT, an independent evaluation of registration accuracy is possible. This population provides an ideal testing ground for MRI to CT DIR by obviating the need for multimodality comparisons which are inherently more challenging.

Technical Note: The impact of deformable image registration methods on dose warping

PURPOSE

The purpose of this study was to investigate the clinical-relevant discrepancy between doses warped by pure image based deformable image registration (IM-DIR) and by biomechanical model based DIR (BM-DIR) on intensity-homogeneous organs.

METHODS AND MATERIALS

Ten patients (5Head&Neck, 5Prostate) were included. A research DIR tool (ADMRIE_v1.12) was utilized for IM-DIR. After IM-DIR, BM-DIR was carried out for organs (parotids, bladder, and rectum) which often encompass sharp dose gradient. Briefly, high-quality tetrahedron meshes were generated and deformable vector fields (DVF) from IM-DIR were interpolated to the surface nodes of the volume meshes as boundary condition. Then, a FEM solver (ABAQUS_v6.14) was used to simulate the displacement of internal nodes, which were then interpolated to image-voxel grids to get the more physically plausible DVF. Both geometrical and subsequent dose warping discrepancies were quantified between the two DIR methods. Target registration discrepancy(TRD) was evaluated to show the geometry difference. The re-calculated doses on second CT were warped to the pre-treatment CT via two DIR. Clinical-relevant dose parameters and γ passing rate were compared between two types of warped dose. The correlation was evaluated between parotid shrinkage and TRD/dose discrepancy.

RESULT

The parotid shrunk to 75.7% ± 9% of its pre-treatment volume and the percentage of volume with TRD>1.5 mm) was 6.5% ± 4.7%. The normalized mean-dose difference (NMDD) of IM-DIR and BM-DIR was -0.8% ± 1.5%, with range (-4.7% to 1.5%). 2 mm/2% passing rate was 99.0% ± 1.4%. A moderate correlation was found between parotid shrinkage and TRD and NMDD. The bladder had a NMDD of -9.9% ± 9.7%, with BM-DIR warped dose systematically higher. Only minor deviation was observed for rectum NMDD (0.5% ± 1.1%).

CONCLUSION

Impact of DIR method on treatment dose warping is patient and organ-specific. Generally, intensity-homogeneous organs, which undergo larger deformation/shrinkage during treatment and encompass sharp dose gradient, will have greater dose warping uncertainty. For these organs, BM-DIR could be beneficial to the evaluation of DIR/dose-warping uncertainty.

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.

Prognostic factors associated with the accuracy of deformable image registration in lung cancer patients treated with stereotactic body radiotherapy

We evaluated the accuracy of an in-house program in lung stereotactic body radiation therapy (SBRT) cancer patients, and explored the prognostic factors associated with the accuracy of deformable image registrations (DIRs). The accuracy of the 3 programs which implement the free-form deformation and the B-spline algorithm was compared regarding the structures on 4-dimensional computed tomography (4DCT) image datasets between the peak-inhale and peak-exhale phases. The dice similarity coefficient (DSC) and normalized DSC (NDSC) were measured for the gross tumor volumes from 19 lung SBRT patients. We evaluated the accuracy of DIR using gross tumor volume, magnitude of displacement from 0% phase to 50% phase, whole lung volume in the 50% phase image, and status of tumor pleural attachment. The median NDSC values using the NiftyReg, MIM Maestro and Velocity AI programs were 1.027, 1.005, and 0.946, respectively, indicating that NiftyReg and MIM Maestro programs had similar accuracy with an uncertainty of < 1 mm. Larger uncertainty of 1 to 2 mm was observed using the Velocity AI program. The NiftyReg and the MIM programs provided higher NDSC values than the median values when the gross tumor volume was attached to the pleura (p <0.05). However, it showed a different trend in using the Velocity AI program. All software programs provided unexpected results, and there is a possibility that such results would reduce the accuracy of 4D treatment planning and adaptive radiotherapy. The unexpected results may be because the tumors are surrounded by other tissues, and there are differences regarding the region of interest for rigid and nonrigid registration. Furthermore, our results indicated that the pleural attachment status might be an important predictor of DIR accuracy for thoracic images, indicating that there is a potentially large dose distribution discrepancy concerning 4D treatment planning and adaptive radiotherapy.

Representing the dosimetric impact of deformable image registration errors

Deformable image registration (DIR) is emerging as a tool in radiation therapy for calculating the cumulative dose distribution across multiple fractions of treatment. Unfortunately, due to the variable nature of DIR algorithms and dependence of performance on image quality, registration errors can result in dose accumulation errors. In this study, landmarked images were used to characterize the DIR error throughout an image space and determine its impact on dosimetric analysis. Ten thoracic 4DCT images with 300 landmarks per image study matching the end-inspiration and end-expiration phases were obtained from 'dir-labs'. DIR was performed using commercial software MIM Maestro. The range of dose uncertainty (RDU) was calculated at each landmark pair as the maximum and minimum of the doses within a sphere around the landmark in the end-expiration phase. The radius of the sphere was defined by a measure of DIR error which included either the actual DIR error, mean DIR error per study, constant errors of 2 or 5 mm, inverse consistency error, transitivity error or the distance discordance metric (DDM). The RDUs were evaluated using the magnitude of dose uncertainty (MDU) and inclusion rate (IR) of actual error lying within the predicted RDU. The RDU was calculated for 300 landmark pairs on each 4DCT study for all measures of DIR error. The most representative RDU was determined using the actual DIR error with a MDU of 2.5 Gy and IR of 97%. Across all other measures of DIR error, the DDM was most predictive with a MDU of 2.5 Gy and IR of 86%, closest to the actual DIR error. The proposed method represents the range of dosimetric uncertainty of DIR error using either landmarks at specific voxels or measures of registration accuracy throughout the volume.

Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms

PURPOSE

Accurate deformable image registration is necessary for longitudinal studies. The error associated with commercial systems has been evaluated using computed tomography (CT). Several in-house algorithms have been evaluated for use with magnetic resonance imaging (MRI), but there is still relatively little information about MRI deformable image registration. This work presents an evaluation of two deformable image registration systems, one commercial (Velocity) and one in-house (demons-based algorithm), with MRI using two different metrics to quantify the registration error.

METHODS

The registration error was analyzed with synthetic MR images. These images were generated from interpatient and intrapatient variation models trained on 28 patients. Four synthetic post-treatment images were generated for each of four synthetic pretreatment images, resulting in 16 image registrations for both the T1- and T2-weighted images. The synthetic post-treatment images were registered to their corresponding synthetic pretreatment image. The registration error was calculated between the known deformation vector field and the generated deformation vector field from the image registration system. The registration error was also analyzed using a porcine phantom with ten implanted 0.35-mm diameter gold markers. The markers were visible on CT but not MRI. CT, T1-weighted MR, and T2-weighted MR images were taken in four different positions. The markers were contoured on the CT images and rigidly registered to their corresponding MR images. The MR images were deformably registered and the distance between the projected marker location and true marker location was measured as the registration error.

RESULTS

The synthetic images were evaluated only on Velocity. Root mean square errors (RMSEs) of 0.76 mm in the left-right (LR) direction, 0.76 mm in the anteroposterior (AP) direction, and 0.69 mm in the superior-inferior (SI) direction were observed for the T1-weighted MR images. RMSEs of 1.1 mm in the LR direction, 0.75 mm in the AP direction, and 0.81 mm in the SI direction were observed for the T2-weighted MR images. The porcine phantom MR images, when evaluated with Velocity, had RMSEs of 1.8, 1.5, and 2.7 mm in the LR, AP, and SI directions for the T1-weighted images and 1.3, 1.2, and 1.6 mm in the LR, AP, and SI directions for the T2-weighted images. When the porcine phantom images were evaluated with the in-house demons-based algorithm, RMSEs were 1.2, 1.5, and 2.1 mm in the LR, AP, and SI directions for the T1-weighted images and 0.81, 1.1, and 1.1 mm in the LR, AP, and SI directions for the T2-weighted images.

CONCLUSIONS

The MRI registration error was low for both Velocity and the in-house demons-based algorithm according to both image evaluation methods, with all RMSEs below 3 mm. This implies that both image registration systems can be used for longitudinal studies using MRI.

Registration error of the liver CT using deformable image registration of MIM Maestro and Velocity AI

BACKGROUND

Understanding the irradiated area and dose correctly is important for the reirradiation of organs that deform after irradiation, such as the liver. We investigated the spatial registration error using the deformable image registration (DIR) software products MIM Maestro (MIM) and Velocity AI (Velocity).

METHODS

Image registration of pretreatment computed tomography (CT) and posttreatment CT was performed in 24 patients with liver tumors. All the patients received proton beam therapy, and the follow-up period was 4-14 (median: 10) months. We performed DIR of the pretreatment CT and compared it with that of the posttreatment CT by calculating the dislocation of metallic markers (implanted close to the tumors).

RESULTS

The fiducial registration error was comparable in both products: 0.4-32.9 (9.3 ± 9.9) mm for MIM and 0.5-38.6 (11.0 ± 10.0) mm for Velocity, and correlated with the tumor diameter for MIM (r = 0.69, P = 0.002) and for Velocity (r = 0.68, P = 0.0003). Regarding the enhancement effect, the fiducial registration error was 1.0-24.9 (7.4 ± 7.7) mm for MIM and 0.3-29.6 (8.9 ± 7.2) mm for Velocity, which is shorter than that of plain CT (P = 0.04, for both).

CONCLUSIONS

The DIR performance of both MIM and Velocity is comparable with regard to the liver. The fiducial registration error of DIR depends on the tumor diameter. Furthermore, contrast-enhanced CT improves the accuracy of both MIM and Velocity.

INSTITUTIONAL REVIEW BOARD APPROVAL

H28-102; July 14, 2016 approved.

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.

A Comparative Evaluation of 3 Different Free-Form Deformable Image Registration and Contour Propagation Methods for Head and Neck MRI: The Case of Parotid Changes During Radiotherapy

Purpose:

To validate and compare the deformable image registration and parotid contour propagation process for head and neck magnetic resonance imaging in patients treated with radiotherapy using 3 different approaches—the commercial MIM, the open-source Elastix software, and an optimized version of it.

Materials and Methods:

Twelve patients with head and neck cancer previously treated with radiotherapy were considered. Deformable image registration and parotid contour propagation were evaluated by considering the magnetic resonance images acquired before and after the end of the treatment. Deformable image registration, based on free-form deformation method, and contour propagation available on MIM were compared to Elastix. Two different contour propagation approaches were implemented for Elastix software, a conventional one (DIR_Trx) and an optimized homemade version, based on mesh deformation (DIR_Mesh). The accuracy of these 3 approaches was estimated by comparing propagated to manual contours in terms of average symmetric distance, maximum symmetric distance, Dice similarity coefficient, sensitivity, and inclusiveness.

Results:

A good agreement was generally found between the manual contours and the propagated ones, without differences among the 3 methods; in few critical cases with complex deformations, DIR_Mesh proved to be more accurate, having the lowest values of average symmetric distance and maximum symmetric distance and the highest value of Dice similarity coefficient, although nonsignificant. The average propagation errors with respect to the reference contours are lower than the voxel diagonal (2 mm), and Dice similarity coefficient is around 0.8 for all 3 methods.

Conclusion:

The 3 free-form deformation approaches were not significantly different in terms of deformable image registration accuracy and can be safely adopted for the registration and parotid contour propagation during radiotherapy on magnetic resonance imaging. More optimized approaches (as DIR_Mesh) could be preferable for critical deformations.

Analysis of GTV reduction during radiotherapy for oropharyngeal cancer: Implications for adaptive radiotherapy

BACKGROUND AND PURPOSE

Adaptive field size reduction based on gross tumor volume (GTV) shrinkage imposes risk on coverage. Fiducial markers were used as surrogate for behavior of tissue surrounding the GTV edge to assess this risk by evaluating if GTVs during treatment are dissolving or actually shrinking.

MATERIALS AND METHODS

Eight patients with oropharyngeal tumors treated with chemo-radiation were included. Before treatment, fiducial markers (0.035×0.2cm², n=40) were implanted at the edge of the primary tumor. All patients underwent planning-CT, daily cone beam CT (CBCT) and MRIs (pre-treatment, weeks 3 and 6). Marker displacement on CBCT was compared to local GTV surface displacement on MRIs. Additionally, marker displacement relative to the GTV surfaces during treatment was measured.

RESULTS

GTV surface displacement derived from MRI was larger than derived from fiducial markers (average difference: 0.1cm in week 3). During treatment, the distance between markers and GTV surface on MRI in week 3 increased in 33%>0.3cm and in 10%>0.5cm. The MRI-GTV shrank faster than the surrounding tissue represented by the markers, i.e. adapting to GTV shrinkage may cause under-dosage of microscopic disease.

CONCLUSIONS

We showed that adapting to primary tumor GTV shrinkage on MRI mid-treatment is potentially not safe since at least part of the GTV is likely to be dissolving. Adjustment to clear anatomical boundaries, however, may be done safely.

Evaluation of the tool "Reg Refine" for user-guided deformable image registration

“Reg Refine” is a tool available in the MIM Maestro v6.4.5 platform (www.mimsoftware.com) that allows the user to actively participate in the deformable image registration process. The purpose of this work was to evaluate the efficacy of this tool and investigate strategies for how to apply it effectively. This was done by performing DIR on two publicly available ground‐truth models, the Pixel‐based Breathing Thorax Model (POPI) for lung, and the Deformable Image Registration Evaluation Project (DIREP) for head and neck. Image noise matched in both magnitude and texture to clinical CBCT scans was also added to each model to simulate the use case of CBCT–CT alignment. For lung, the results showed Reg Refine effective at improving registration accuracy when controlled by an expert user within the context of large lung deformation. CBCT noise was also shown to have no effect on DIR performance while using the MIM algorithm for this site. For head and neck, the results showed CBCT noise to have a large effect on the accuracy of registration, specifically for low‐contrast structures such as the brainstem and parotid glands. In these cases, the Reg Refine tool was able to improve the registration accuracy when controlled by an expert user. Several strategies for how to achieve these results have been outlined to assist other users and provide feedback for developers of similar tools.

PACS number(s): 87.44.Qr, 87.57.nj, 87.57.c

Performance variations among clinically available deformable image registration tools in adaptive radiotherapy - how should we evaluate and interpret the result?

The purpose of this study is to evaluate the performance variations in commercial deformable image registration (DIR) tools for adaptive radiation therapy and further to interpret the differences using clinically available terms. Three clinical examples (prostate, head and neck (HN), and cranial spinal irradiation (CSI) with L‐spine boost) were evaluated in this study. Firstly, computerized deformed CT images were generated using simulation QA software with virtual deformations of bladder filling (prostate), neck flexion/bite‐block repositioning/tumor shrinkage (HN), and vertebral body rotation (CSI). The corresponding transformation matrices served as a “reference” for the following comparisons. Three commercialized DIR algorithms: the free‐form deformation from MIMVista 5.5 and the RegRefine from MIMMaestro 6.0, the multipass B‐spline from VelocityAI v3.0.1, and the adaptive demons from OnQ rts 2.1.15, were applied between the initial images and the deformed CT sets. The generated adaptive contours and dose distributions were compared with the “reference” and among each other. The performance in transferring contours was comparable among all three tools with an average Dice similarity coefficient of 0.81 for all the organs. However, the dose warping accuracy appeared to rely on the evaluation end points and methodologies. Point‐dose differences could show a difference of up to 23.3 Gy inside the PTVs and to overestimate up to 13.2 Gy for OARs, which was substantial for a 72 Gy prescription dose. Dosevolume histogram‐based evaluation might not be sensitive enough to illustrate all the detailed variations, while isodose assessment on a slice‐by‐slice basis could be tedious. We further explored the possibility of using 3D gamma index analysis for warping dose variation assessment, and observed differences in dose warping using different DIR tools. Overall, our results demonstrated that evaluation based only on the performance of contour transformation could not guarantee the accuracy in dose warping, while dose‐transferring validation strongly relied on the evaluation endpoint. As dose‐transferring errors could cause misinterpretations when attempting to accumulate dose for adaptive radiation therapy and more DIR tools are available for clinical use, a standard and clinically meaningful quality assurance criterion should be established for DIR QA in the near future.

PACS number(s): 87.57

A Feasibility Study of Tumor Motion Estimate With Regional Deformable Registration Method for 4-Dimensional Radiation Therapy of Lung Cancer

This study aims to employ 4-dimensional computed tomography to quantify intrafractional tumor motion for patients with lung cancer to improve target localization in radiation therapy. A multistage regional deformable registration was implemented to calculate the excursion of gross tumor volume (GTV) during a breathing cycle. GTV was initially delineated on 0% phase of 4-dimensional computed tomography manually, and a subregion with 20 mm margin supplemented to GTV was generated with Eclipse treatment planning system (Varian Medical Systems, Palo Alto, California). The structures, together with the 4-dimensional computed tomography set, were exported into an in-house software, with which a 3-stage B-spline deformable registration was carried out to map the subregion and warp GTV contour to other breathing phases. The center of mass of the GTV was computed using the contours, and the tumor motion was appraised as the excursion of the center of mass between 0% phase and other phases. Application of the algorithm to the 10 patients showed that clinically satisfactory outcomes were achievable with a spatial accuracy around 2 mm for GTV contour propagation between adjacent phases and 3 mm between opposite phases. The tumor excursion was determined in the vast range of 1 mm through 1.6 cm, depending on the tumor location and tumor size. Compared to the traditional whole image-based registration, the regional method was found computationally a factor of 5 more efficient. The proposed technique has demonstrated its capability in extracting thoracic tumor motion and should find its application in 4-dimensional radiation therapy in the future to maximally utilize the available spatial-temporal information.