Deformable Image Registration Errors Research Articles

Detecting and quantifying spatial misalignment between longitudinal kilovoltage computed tomography (kVCT) scans of the head and neck by using convolutional neural networks (CNNs).

Adaptive radiotherapy (ART) aims to address anatomical modifications appearing during the treatment of patients by modifying the planning treatment according to the daily positioning image. Clinical implementation of ART relies on the quality of the deformable image registration (DIR) algorithms included in the ART workflow. To translate ART into clinical practice, automatic DIR assessment is needed. This article aims to estimate spatial misalignment between two head and neck kilovoltage computed tomography (kVCT) images by using two convolutional neural networks (CNNs). The first CNN quantifies misalignments between 0 mm and 15 mm and the second CNN detects and classifies misalignments into two classes (poor alignment and good alignment). Both networks take pairs of patches of 33x33x33 mm3 as inputs and use only the image intensity information. The training dataset was built by deforming kVCT images with basis splines (B-splines) to simulate DIR error maps. The test dataset was built using 2500 landmarks, consisting of hard and soft landmark tissues annotated by 6 clinicians at 10 locations. The quantification CNN reaches a mean error of 1.26 mm (± 1.75 mm) on the landmark set which, depending on the location, has annotation errors between 1 mm and 2 mm. The errors obtained for the quantification network fit the computed interoperator error. The classification network achieves an overall accuracy of 79.32%, and although the classification network overdetects poor alignments, it performs well (i.e., it achieves a rate of 90.4%) in detecting poor alignments when given one. The performances of the networks indicate the feasibility of using CNNs for an agnostic and generic approach to misalignment quantification and detection.

Detailed dosimetric evaluation of inter-fraction and respiratory motion in lung stereotactic body radiation therapy based on daily 4D cone beam CT images

Objective. Periodic respiratory motion and inter-fraction variations are sources of geometric uncertainty in stereotactic body radiation therapy (SBRT) of pulmonary lesions. This study extensively evaluates and validates the separate and combined dosimetric effect of both factors using 4D-CT and daily 4D-cone beam CT (CBCT) dose accumulation scenarios. Approach. A first cohort of twenty early stage or metastatic disease lung cancer patients were retrospectively selected to evaluate each scenario. The planned-dose (3DRef) was optimized on a 3D mid-position CT. To estimate the dosimetric impact of respiratory motion (4DRef), inter-fractional variations (3DAcc) and the combined effect of both factors (4DAcc), three dose accumulation scenarios based on 4D-CT, daily mid-cone beam CT (CBCT) position and 4D-CBCT were implemented via CT-CT/CT-CBCT deformable image registration (DIR) techniques. Each scenario was compared to 3DRef. A separate cohort of ten lung SBRT patients was selected to validate DIR techniques. The distance discordance metric (DDM) was implemented per voxel and per patient for tumor and organs at risk (OARs), and the dosimetric impact for CT-CBCT DIR geometric errors was calculated. Main results. Median and interquartile range (IQR) of the dose difference per voxel were 0.05/2.69 Gy and −0.12/2.68 Gy for and For the IQR was considerably smaller −0.15/0.78 Gy. These findings were confirmed by dose volume histogram parameters calculated in tumor and OARs. For CT-CT/CT-CBCT DIR validation, DDM (95th percentile) was highest for heart (6.26 mm)/spinal cord (8.00 mm), and below 3 mm for tumor and the rest of OARs. The dosimetric impact of CT-CBCT DIR errors was below 2 Gy for tumor and OARs. Significance. The dosimetric impact of inter-fraction variations were shown to dominate those of periodic respiration in SBRT for pulmonary lesions. Therefore, treatment evaluation and dose-effect studies would benefit more from dose accumulation focusing on day-to-day changes then those that focus on respiratory motion.

Geometric Analysis and Dosimetric Evaluation of Various Deformable Image Registration (DIR) Algorithms for Abdominal Images with 3D Biomechanically Modelled Deformation

<h3>Purpose/Objective(s)</h3> The accuracy of DIR algorithms available on commercial radiotherapy planning platforms were examined and their clinical applicability geometrically and dosimetrically for abdominal sites were evaluated using biomechanically modelled deformation. <h3>Materials/Methods</h3> 5 pancreatic patients' planning CT were used in this study. Following the guideline of AAPM TG-132, a patient-specific QA workflow was developed and implemented to evaluate DIR for abdomen using digital phantoms, created with a commercial virtual phantom software (VPS). First, the planning CT images were deformed in superior-inferior direction to simulation respiratory motion in lung and abdomen, using the biomechanical model embedded in VPS. In addition, 5 mm motion in both anterior-posterior and lateral directions were added to region of interest (ROI), including stomach and duodenum, and small bowel. The added ROI deformations are consistent with the abdomen data observed in daily CBCT. The resulting 3D deformation vector field (DVF) and the corresponding deformed CT was then obtained, serving as ground truth. Second, the original planning CT and the deformed CT were imported to two commercial DIR systems (DS1 and DS2), to perform DIR with intensity-based algorithms, and derive DVFs. Quantitative differences between ground truth DVF and DS1 or DS2's DVFs, such as mean, max, and the distribution of DIR errors were tabulated. Then the original plan was re-calculated on the deformed CT, which was generated based on DS1 or DS2's DVF. The dose errors were then calculated on a pixel-to-pixel basis. The relationship between the DIR errors and the dose error ranges (25%-75% on the dose error distribution histogram) were investigated. As a pilot study, comprehensive reports were generated to show the DIR performances as patient specific QA records. <h3>Results</h3> The displacements on the ground truth DVF range from 0 to 37 mm. Geometric analysis showed DIR errors as tabulated below. Dosimetrically, as the DIR error increases, the dose error range also increase, for both DS1 and DS2. Dosimetric evaluation of the dose errors for ROIs were tabulated as below. <h3>Conclusion</h3> Preliminary evaluation of geometric and dosimetric accuracy of DIR on DS1 and DS2 were performed, with a realistic biomechanical model derived DVF serving as ground truth. A patient specific DIR QA workflow was developed for a pancreatic patient cohort, and it has potential to be implemented as a clinical workflow.

Patient-Specific Quality Assurance of Deformable Image Registrations Using Atlas for Adaptive Radiotherapy of Lung Cancer

<h3>Purpose/Objective(s)</h3> Evaluating the uncertainty of deformable image registration (DIR) is challenging because the ground truth is often unavailable. We developed an automated method to create realistic deformations via an atlas and assess DIR algorithms on a patient-specific level for adaptive radiotherapy. <h3>Materials/Methods</h3> A library of deformations was created by extracting the longitudinal anatomical changes observed from an atlas of 60 locally advanced non-small cell lung cancer patients treated with IMRT (60Gy, 30 fx). The deformation vector field (DVF) between the planning CT (pCT) and last weekly CBCT of an atlas patient was derived via a free-form DIR algorithm and served as a ground truth pattern. An inquiry patient was first matched to an atlas patient on gross tumor volume (GTV) location and volume. The pCT of an inquiry patient was then deformably registered to that of an atlas patient to establish a voxel-based correspondence. Then, the deformation pattern of the atlas patient was transferred to the inquiry patient pCT by applying the known DVF to the corresponding voxels, resulting in a digital CT phantom. Subsequently, this CT phantom was appended with CBCT artifacts using a physics-based augmentation (Alam, PMB 2021) to imitate a weekly CBCT. To evaluate DIRs, a large deformation diffeomorphic metric mapping (LDDMM) algorithm, and two commercial systems (C1 C2) were used between the phantom (both simulated CT and CBCT) and actual pCT of the inquiry patient. Derived DVFs were compared to the ground truth. The voxel-level geometric and dosimetric uncertainties of DIRs were calculated using the DVF95% errors, fraction of volume with ≤1.5mm errors (V1.5mm) and mean dose errors (D_mean) for GTV and esophagus. <h3>Results</h3> In a retrospective evaluation of 10 paired inquiry and atlas patients, the actual deformations observed between the pCT and weekly CBCTs of inquiry patients were well-contained by the combined patterns from the matched atlas, validating the feasibility of an atlas. LDDMM performed consistently well, especially for the CT-CBCT registration (p<0.01, two-tailed t-test). GTV mean dose is less sensitive to DIR errors than esophagus mean dose. <h3>Conclusion</h3> It is feasible to augment anatomical changes for a particular inquiry patient using deformation patterns from matched patients in an atlas and evaluate the uncertainty of DIR algorithms in realistic simulations. Integration of such an automated program facilitates the clinical implementation of adaptive radiotherapy that involves longitudinal imaging studies.

A validation framework to assess performance of commercial deformable image registration in lung radiotherapy

<h2>Abstract</h2><h3>Introduction</h3> Deformable image registration (DIR) can play an important role in the context of adaptive radiotherapy. The AAPM Task Group 132 (TG-132) has described several quantitative measures for DIR error assessment but they can only be accurately defined when there is a ground-truth present in high-contrast regions. This work aims to set out a framework to obtain optimal results for CT-CT lung DIR in clinical setting for a commercially available system by quantifying the DIR performance in both low- and high-contrast regions. <h3>Methods</h3> Five publicly available thorax datasets were used to assess the DIR quality. A "Ghost fiducial" method was implemented by windowing the contrast in a new feature provided by Varian Velocity v4.1. Target registration error (TRE) of the landmarks and Dice-similarity coefficient of the tumour were calculated at three different contrast settings to assess the algorithm in high- and low-contrast scenarios. <h3>Results</h3> For the original unedited dataset, higher resolution DIR methods showed best performance acceptable within the recommended limit according to TG-132, when actual displacements were less than 10 mm. The relation of the actual displacement of the landmarks and TRE shows the limited capacity of the algorithm to deal with movements larger than 10 mm. <h3>Conclusion</h3> This work found the performance of DIR methods and settings available in Varian Velocity v4.1 to be a function of contrast level as well as extent of motion. This highlights the need for multiple metrics to assess different aspects of DIR performance for various applications related to low-contrast and/or high-contrast regions.

A 3D cine-MRI acquisition technique and image analysis framework to quantify bowel motion demonstrated in gynecological cancer patients.

PurposeMagnetic resonance imaging (MRI) is increasingly used in radiation oncology for target delineation and radiotherapy treatment planning, for example, in patients with gynecological cancers. As a consequence of pelvic radiotherapy, a part of the bowel is irradiated, yielding risk of bowel toxicity. Existing dose‐effect models predicting bowel toxicity are inconclusive and bowel motion might be an important confounding factor. The exact motion of the bowel and dosimetric effects of its motion are yet uncharted territories in radiotherapy. In diagnostic radiology methods on the acquisition of dynamic MRI sequences were developed for bowel motility visualization and quantification. Our study aim was to develop an imaging technique based on three‐dimensional (3D) cine‐MRI to visualize and quantify bowel motion and demonstrate it in a cohort of gynecological cancer patients.MethodsWe developed an MRI acquisition suitable for 3D bowel motion quantification, namely a balanced turbo field echo sequence (TE = 1.39 ms, TR = 2.8 ms), acquiring images in 3.7 s (dynamic) with a 1.25 × 1.25 × 2.5 mm3 resolution, yielding a field of view of 200 × 200 × 125 mm3. These MRI bowel motion sequences were acquired in 22 gynecological patients. During a 10‐min scan, 160 dynamics were acquired. Subsequent dynamics were deformably registered using a B‐spline transformation model, resulting in 159 3D deformation vector fields (DVFs) per MRI set. From the 159 DVFs, the average vector length was calculated per voxel to generate bowel motion maps. Quality assurance was performed on all 159 DVFs per MRI, using the Jacobian Determinant and the Harmonic Energy as deformable image registration error metrics. In order to quantify bowel motion, we introduced the concept of cumulative motion–volume histogram (MVH) of the bowel bag volume. Finally, interpatient variation of bowel motion was analyzed using the MVH parameters M10%, M50%, and M90%. The M10%/M50%/M90% represents the minimum bowel motion per frame of 10%/50%/90% of the bowel bag volume.ResultsThe motion maps resulted in a visualization of areas with small and large movements within the bowel bag. After applying quality assurance, the M10%, M50%, and M90% were 4.4 (range 2.2–7.6) mm, 2.2 (range 0.9–4.1) mm, and 0.5 (range 0.2–1.4) mm per frame, on average over all patients, respectively.ConclusionWe have developed a method to visualize and quantify 3D bowel motion with the use of bowel motion specific MRI sequences in 22 gynecological cancer patients. This 3D cine‐MRI‐based quantification tool and the concept of MVHs can be used in further studies to determine the effect of radiotherapy on bowel motion and to find the relation with dose effects to the small bowel. In addition, the developed technique can be a very interesting application for bowel motility assessment in diagnostic radiology.

An MRI framework for respiratory motion modelling validation.

IntroductionRespiratory motion models establish a correspondence between respiratory‐correlated (RC) 4‐dimensional (4D) imaging and respiratory surrogates, to estimate time‐resolved (TR) 3D breathing motion. To evaluate the performance of motion models on real patient data, a validation framework based on magnetic resonance imaging (MRI) is proposed, entailing the use of RC 4DMRI to build the model, and on both (i) TR 2D cine‐MRI and (ii) additional 4DMRI data for testing intra‐/inter‐fraction breathing motion variability.MethodsRepeated MRI data were acquired in 7 patients with abdominal lesions. The considered model relied on deformable image registration (DIR) for building the model and compensating for inter‐fraction baseline variations. Both 2D and 3D validation were performed, by comparing model estimations with the ground truth 2D cine‐MRI and 4DMRI respiratory phases, respectively.ResultsThe median DIR error was comparable to the voxel size (1.33 × 1.33 × 5 mm3), with higher values in the presence of large inter‐fraction motion (median value: 2.97 mm). In the 2D validation, the median estimation error on anatomical landmarks’ position resulted below 4 mm in every scenario, whereas in the 3D validation it was 1.33 mm and 4.21 mm when testing intra‐ and inter‐fraction motion, respectively. The range of motion described in the cine‐MRI was comparable to the motion of the building 4DMRI, being always above the estimation error. Overall, the model performance was dependent on DIR error, presenting reduced accuracy when inter‐fraction baseline variations occurred.ConclusionsResults suggest the potential of the proposed framework in evaluating global motion models for organ motion management in MRI‐guided radiotherapy.

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

To develop and evaluate a robust deformable image registration (DIR) enhancement method based on radial basis function (RBF) expansion. To improve DIR accuracy using sparsely available measured displacements, it is crucial to estimate the motion correlation between the voxels. In the proposed method, we chose to derive this correlation from the initial displacement vector fields (DVFs), and represent it in the form of RBF expansion coefficients of the voxels. The method consists of three steps: (I) convert an initial DVF to a coefficient matrix comprising expansion coefficients of the Wendland's RBF; (II) modify the coefficient matrix under the guidance of sparely distributed landmarks to generate the post-enhancement coefficient matrix; and (III) convert the post-enhancement coefficient matrix to the post-enhancement DVF. The method was tested on five DIR algorithms using a digital phantom. 3D registration errors were calculated for comparisons between the pre-/post-enhancement DVFs and the ground-truth DVFs. Effects of the number and locations of landmarks on DIR enhancement were evaluated. After applying the DIR enhancement method, the 3D registration errors per voxel (unit: mm) were reduced from pre-enhancement to post-enhancement by 1.3 (2.4 to 1.1, 54.2%), 0.0 (0.9 to 0.9, 0.0%), 6.1 (8.2 to 2.1, 74.4%), 3.2 (4.7 to 1.5, 68.1%), and 1.7 (2.9 to 1.2, 58.6%) for the five tested DIR algorithms respectively. The average DIR error reduction was 2.5±2.3 mm (percentage error reduction: 51.1%±29.1%). 3D registration errors decreased inverse-exponentially as the number of landmarks increased, and were insensitive to the landmarks' locations in relation to the down-sampling DVF grids. We demonstrated the feasibility of a robust RBF-based method for enhancing DIR accuracy using sparsely distributed landmarks. This method has been shown robust and effective in reducing DVF errors using different numbers and distributions of landmarks for various DIR algorithms.

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.

A method for optimizing planning target volume margins for patients receiving lung stereotactic body radiotherapy

Lung stereotactic-body radiotherapy (SBRT) places additional requirements on targeting accuracy over standard approaches. In treatment planning, a tumour volume is geometrically expanded and the resulting planning target volume (PTV) is covered with the prescribed dose. This ensures full dose delivery despite various uncertainties encountered during treatment.We developed a retrospective technique for optimizing the PTV expansion for a patient population. The method relies on deformable image registration (DIR) of the planning CT to a treatment cone-beam CT (CBCT). The resulting transformation is used to map the planned target onto the treatment geometry, allowing the computation of the achieved target/PTV overlap.Basic validation of the method was performed using an anthropomorphic respiratory motion phantom. A self-validation technique was also implemented to allow estimation of the DIR error for the data being analyzed. Our workflow was used to retrospectively optimize PTV margin for 25 patients treated over 93 fractions. Targets for these patients were contoured on 4D CT images. SBRT delivery followed CBCT acquisition and a couch correction. A post-treatment CBCT was also acquired in some cases.Our basic validation demonstrated that the DIR-based technique is capable of transforming target volumes from planning CTs to treatment CBCTs with sub-mm accuracy. Our clinical analysis showed that the minimum percentages of target volumes covered for 3, 4, and 5 mm PTV margins were 92.1, 97.6, and 99.2, respectively. Analyzing data acquired before and just after treatment demonstrated that margins exceeding 5 mm did not significantly improve coverage. Finally, a 5 mm PTV margin achieved ⩾95% target volume coverage with ⩾95% probability.Our technique is accurate, automated, self-validating, and incorporates complex ITV shapes/deformations to allow PTV margin optimization. The analysis of clinical data indicates a 5 mm PTV margin is optimal for our process. This approach is generalizable to other disease sites and treatment strategies.

"Patient-specific validation of deformable image registration in radiation therapy: Overview and caveats".

Over the last few decades, deformable image registration (DIR) has gained popularity in image-guided radiation therapy for a number of applications, such as contour propagation, dose warping, and accumulation. Although this raises promising perspectives for the improvement of treatment outcomes and quality of radiotherapy clinical practice, the variety of proposed DIR algorithms, combined with the lack of an effective quantitative quality control metric of the registration, is slowing the transfer of DIR into the clinical routine. Recently, a task group (AAPM TG132) report was published outlining the essential aspects of DIR for image guidance in radiotherapy. However, an accurate and efficient patient-specific validation is not yet defined, and appropriate metrics should be identified to achieve the definition of both geometric and dosimetric accuracy. In this respect, the use of a dense set of anatomical landmarks, along with additional evaluations on contours or deformation field analysis, are likely to drive patient-specific DIR validation in clinical image-guided radiotherapy applications to account for geometric inaccuracies. Automatic and efficient strategies able to provide spatial information of DIR uncertainties and to evaluate monomodal and multimodal image registration, as well as to describe homogenous and un-contrasted regions are believed to represent the future direction in DIR validation. But especially in the case of DIR applications for dose mapping and accumulation, the need of accurate patient-specific validation is not only limited to the evaluation of geometric accuracy. In fact, the need to account for dosimetric inaccuracies due to DIR represents another important area in the field of adaptive treatments. Different approaches are currently being investigated to quantify the effect of DIR error on dose analysis, mainly relying on clinically relevant dose metrics, or on the study of deformation field properties for a voxel-by-voxel evaluation. However, novel research is required for the definition of dedicated and personalized measures capable to relate the geometric and dosimetric inaccuracies, thus bearing useful information for a safe use of DIR by clinical end users. In this paper we provide insights on DIR results evaluation on a patient-specific basis, facing the issues of both geometric and dosimetric paradigms. Challenges on DIR validation are overviewed and discussed, in order to push preliminary clinical guidelines forward on this fundamental topic and boost the implementation of more robust and reliable patient-specific evaluation metrics.

Inflammatory conditions affect the acetaminophen-induced liver injury in a novel 3D model of human multicellular microtissues

To introduce and evaluate a novel deformable 3-dimensional (3D) dosimetry system (Presage-Def/Optical-CT) and its application toward investigating the accuracy of dose deformation in a commercial deformable image registration (DIR) package.Presage-Def is a new dosimetry material consisting of an elastic polyurethane matrix doped with radiochromic leuco dye. Radiologic and mechanical properties were characterized using standard techniques. Dose-tracking feasibility was evaluated by comparing dose distributions between dosimeters irradiated with and without 27% lateral compression. A checkerboard plan of 5-mm square fields enabled precise measurement of true deformation using 3D dosimetry. Predicted deformation was determined from a commercial DIR algorithm.Presage-Def exhibited a linear dose response with sensitivity of 0.0032 ΔOD/(Gy∙cm). Mass density is 1.02 g/cm3, and effective atomic number is within 1.5% of water over a broad (0.03-10 MeV) energy range, indicating good water-equivalence. Elastic characteristics were close to that of liver tissue, with Young's modulus of 13.5-887 kPa over a stress range of 0.233-303 kPa, and Poisson's ratio of 0.475 (SE, 0.036). The Presage-Def/Optical-CT system successfully imaged the nondeformed and deformed dose distributions, with isotropic resolution of 1 mm. Comparison with the predicted deformed 3D dose distribution identified inaccuracies in the commercial DIR algorithm. Although external contours were accurately deformed (submillimeter accuracy), volumetric dose deformation was poor. Checkerboard field positioning and dimension errors of up to 9 and 14 mm, respectively, were identified, and the 3D DIR-deformed dose γ passing rate was only γ3%/3 mm = 60.0%.The Presage-Def/Optical-CT system shows strong potential for comprehensive investigation of DIR algorithm accuracy. Substantial errors in a commercial DIR were found in the conditions evaluated. This work highlights the critical importance of careful validation of DIR algorithms before clinical implementation.

P238] Dosimetric evaluation of deformable image registration error using 4DCT-MRI datasets

Purpose Deformable image registration (DIR) is irreplaceable for 4D dose calculation (4DDC). Any geometric errors from estimated deformable vector fields (DVFs) directly lead to error in accumulated dose distribution, influencing clinical decision. Quantifying DIR error is imperative but difficult due to the lack of DVF ground-truth. This study aims to evaluate the dosimetric impact of DIR error by utilizing the advantage of 4DCT-MRI images whose ground-truth motions (DVF) are pre-defined. Methods 6 synthetic 4DCT-MRI datasets were generated by respectively warping the reference CT of 3 liver patients (CTV volume of 122, 264 and 403 cc) with 2 deformable motion fields (as ground-truth DVF) extracted from 4DMRI (spatial-mean-peak-amplitude of (a) 7.82 and (b) 20.61 mm). The Velocity™ B-splines DIR was applied to each 4DCT-MRI datasets for estimating motion between phases using binary-mask encompassing the full patient or just liver. Geometric error was quantified by the differences of estimated DVFs and corresponding ground-truth DVFs. Dosimetric assessment was performed by analyzing the differences between estimated plans and ground truth 4D plans using estimated or ground-truth DVFs as input for 4DDC. Comparisons of such effects were conducted for single- and multi-field scanned proton therapy treatment plans using either single or re-scanned dose delivery. Results For all cases and scenarios, substantial dose differences were observed between the estimated and ground-truth DVFs and 4D plans. DVF-error magnitude were found to be 1.3±2.1 mm and 3.2±3.4 mm inside liver for (a) small and (b) large motion respectively (averaged over phases and patients). For single-field-no-rescan scenario, interplay effects (D5-D95-CTV) were (a) 2.3±3.3% and (b) 6.0±3.8% under-estimated. The volumes receiving point-to-point absolute dose difference over 10% mainly dominate outside target, being (a) 10.8±2.1% and (b) 24.4±4.4% of the 10 mm-extended-CTV. Applying either multi-field planning or rescanning is helpful for mitigating DIR-error. For multi-field-rescan scenario, difference in D5-D95 can reduce to 0.1±0.7/1.5±1.1% for (a)/(b) motion cases with max error within 3% being achieved for all investigated cases, and less than (a) 6.0±2.5% and (b) 4.8±2.8% volume in the extended-CTV can receive absolute dose difference over 10%. Conclusions The DIR error of Velocity™ was quantitatively estimated, whose dosimetric impacts were shown to be significant for local dose distribution but limited on the plan statistical quantities.

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.

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.

TU‐AB‐202‐02: Deformable Image Registration Accuracy Between External Beam Radiotherapy and HDR Brachytherapy CT Images for Cervical Cancer Using a 3D‐Printed Deformable Pelvis Phantom

Purpose:Accurate deformable image registration (DIR) between external beam radiotherapy (EBRT) and HDR brachytherapy (BT) CT images in cervical cancer is challenging. DSC has been evaluated only on the basis of the consistency of the structure, and its use does not guarantee an anatomically reasonable deformation. We evaluate the DIR accuracy for cervical cancer with DSC and anatomical landmarks using a 3D‐printed pelvis phantom.Methods:A 3D‐printed, deformable female pelvis phantom was created on the basis of the patient's CT image. Urethane and silicon were used as materials for creating the uterus and bladder, respectively, in the phantom. We performed DIR in two cases: case‐A with a full bladder (170 ml) in both the EBRT and BT images and case‐B with a full bladder in the BT image and a half bladder (100 ml) in the EBRT image. DIR was evaluated using DSCs and 70 uterus and bladder landmarks. A Hybrid intensity and structure DIR algorithm with two settings (RayStation) was used.Results:In the case‐A, DSCs of the intensity‐based DIR were 0.93 and 0.85 for the bladder and uterus, respectively, whereas those of hybrid‐DIR were 0.98 and 0.96, respectively. The mean landmark error values of intensity‐based DIR were 0.73±0.29 and 1.70±0.19 cm for the bladder and uterus, respectively, whereas those of Hybrid‐DIR were 0.43±0.33 and 1.23±0.25 cm, respectively. In both cases, the Hybrid‐DIR accuracy was better than the intensity‐based DIR accuracy for both evaluation methods. However, for several bladder landmarks, the Hybrid‐DIR landmark errors were larger than the corresponding intensity‐based DIR errors (e.g., 2.26 vs 1.25 cm).Conclusion:Our results demonstrate that Hybrid‐DIR can perform with a better accuracy than the intensity‐based DIR for both DSC and landmark errors; however, Hybrid‐DIR shows a larger landmark error for some landmarks because the technique focuses on both the structure and intensity.

TU‐AB‐202‐03: Prediction of PET Transfer Uncertainty by DIR Error Estimating Software, AUTODIRECT

Purpose:Deformable image registration (DIR) is a powerful tool, but DIR errors can adversely affect its clinical applications. To estimate voxel‐specific DIR uncertainty, a software tool, called AUTODIRECT (automated DIR evaluation of confidence tool), has been developed and validated. This work tests the ability of this software to predict uncertainty for the transfer of standard uptake values (SUV) from positron‐emission tomography (PET) with DIR.Methods:Virtual phantoms are used for this study. Each phantom has a planning computed tomography (CT) image and a diagnostic PET‐CT image set. A deformation was digitally applied to the diagnostic CT to create the planning CT image and establish a known deformation between the images. One lung and three rectum patient datasets were employed to create the virtual phantoms. Both of these sites have difficult deformation scenarios associated with them, which can affect DIR accuracy (lung tissue sliding and changes in rectal filling). The virtual phantoms were created to simulate these scenarios by introducing discontinuities in the deformation field at the lung rectum border. The DIR algorithm from Plastimatch software was applied to these phantoms. The SUV mapping errors from the DIR were then compared to that predicted by AUTODIRECT.Results:The SUV error distributions closely followed the AUTODIRECT predicted error distribution for the 4 test cases. The minimum and maximum PET SUVs were produced from AUTODIRECT at 95% confidence interval before applying gradient‐based SUV segmentation for each of these volumes. Notably, 93.5% of the target volume warped by the true deformation was included within the AUTODIRECT‐predicted maximum SUV volume after the segmentation, while 78.9% of the target volume was within the target volume warped by Plastimatch.Conclusion:The AUTODIRECT framework is able to predict PET transfer uncertainty caused by DIR, which enables an understanding of the associated target volume uncertainty.

Multi-institutional Validation Study of Commercially Available Deformable Image Registration Software for Thoracic Images

To assess the accuracy of the commercially available deformable image registration (DIR) software for thoracic images at multiple institutions. Thoracic 4-dimensional (4D) CT images of 10 patients with esophageal or lung cancer were used. Datasets for these patients were provided by DIR-lab (dir-lab.com) and included a coordinate list of anatomic landmarks (300 bronchial bifurcations) that had been manually identified. Deformable image registration was performed between the peak-inhale and -exhale images. Deformable image registration error was determined by calculating the difference at each landmark point between the displacement calculated by DIR software and that calculated by the landmark. Eleven institutions participated in this study: 4 used RayStation (RaySearch Laboratories, Stockholm, Sweden), 5 used MIM Software (Cleveland, OH), and 3 used Velocity (Varian Medical Systems, Palo Alto, CA). The ranges of the average absolute registration errors over all cases were as follows: 0.48 to 1.51mm (right-left), 0.53 to 2.86mm (anterior-posterior), 0.85 to 4.46mm (superior-inferior), and 1.26 to 6.20mm (3-dimensional). For each DIR software package, the average 3-dimensional registration error (range) was as follows: RayStation, 3.28mm (1.26-3.91mm); MIM Software, 3.29mm (2.17-3.61mm); and Velocity, 5.01mm (4.02-6.20mm). These results demonstrate that there was moderate variation among institutions, although the DIR software was the same. We evaluated the commercially available DIR software using thoracic 4D-CT images from multiple centers. Our results demonstrated that DIR accuracy differed among institutions because it was dependent on both the DIR software and procedure. Our results could be helpful for establishing prospective clinical trials and for the widespread use of DIR software. In addition, for clinical care, we should try to find the optimal DIR procedure using thoracic 4D-CT data.

Deformable image registration and interobserver variation in contour propagation for radiation therapy planning.

Deformable image registration (DIR) and interobserver variation inevitably introduce uncertainty into the treatment planning process. The purpose of the current work was to measure deformable image registration (DIR) errors and interobserver variability for regions of interest (ROIs) in the head and neck and pelvic regions. Measured uncertainties were combined to examine planning margin adequacy for contours propagated for adaptive therapy and to assess the trade‐off of DIR and interobserver uncertainty in atlas‐based automatic segmentation. Two experienced dosimetrists retrospectively contoured brainstem, spinal cord, anterior oral cavity, larynx, right and left parotids, optic nerves, and eyes on the planning CT (CT1) and attenuation‐correction CT of diagnostic PET/CT (CT2) for 30 patients who received radiation therapy for head and neck cancer. Two senior radiation oncology residents retrospectively contoured prostate, bladder, and rectum on the postseed‐implant CT (CT1) and planning CT (CT2) for 20 patients who received radiation therapy for prostate cancer. Interobserver variation was measured by calculating mean Hausdorff distances between the two observers' contours. CT2 was deformably registered to CT1 via commercially available multipass B‐spline DIR. CT2 contours were propagated and compared with CT1 contours via mean Hausdorff distances. These values were summed in quadrature with interobserver variation for margin analysis and compared with interobserver variation for statistical significance using two‐tailed t‐tests for independent samples (α=0.05). Combined uncertainty ranged from 1.5‐5.8 mm for head and neck structures and 3.1‐3.7 mm for pelvic structures. Conventional 5 mm margins may not be adequate to cover this additional uncertainty. DIR uncertainty was significantly less than interobserver variation for four head and neck and one pelvic ROI. DIR uncertainty was not significantly different than interobserver variation for four head and neck and one pelvic ROI. DIR uncertainty was significantly greater than interobserver variation for two head and neck and one pelvic ROI. The introduction of DIR errors may offset any reduction in interobserver variation by using atlas‐based automatic segmentation.PACS number(s): 87.57.nj, 87.55.D‐

An automated deformable image registration evaluation of confidence tool

Deformable image registration (DIR) is a powerful tool for radiation oncology, but it can produce errors. Beyond this, DIR accuracy is not a fixed quantity and varies on a case-by-case basis. The purpose of this study is to explore the possibility of an automated program to create a patient- and voxel-specific evaluation of DIR accuracy. AUTODIRECT is a software tool that was developed to perform this evaluation for the application of a clinical DIR algorithm to a set of patient images. In brief, AUTODIRECT uses algorithms to generate deformations and applies them to these images (along with processing) to generate sets of test images, with known deformations that are similar to the actual ones and with realistic noise properties. The clinical DIR algorithm is applied to these test image sets (currently 4). From these tests, AUTODIRECT generates spatial and dose uncertainty estimates for each image voxel based on a Student’s t distribution. In this study, four commercially available DIR algorithms were used to deform a dose distribution associated with a virtual pelvic phantom image set, and AUTODIRECT was used to generate dose uncertainty estimates for each deformation. The virtual phantom image set has a known ground-truth deformation, so the true dose-warping errors of the DIR algorithms were also known. AUTODIRECT predicted error patterns that closely matched the actual error spatial distribution. On average AUTODIRECT overestimated the magnitude of the dose errors, but tuning the AUTODIRECT algorithms should improve agreement. This proof-of-principle test demonstrates the potential for the AUTODIRECT algorithm as an empirical method to predict DIR errors.