Open Source Deformable Image Registration Research Articles

Evaluation of an Open Source Registration Package for Automatic Contour Propagation in Online Adaptive Intensity-Modulated Proton Therapy of Prostate Cancer.

Objective: Our goal was to investigate the performance of an open source deformable image registration package, elastix, for fast and robust contour propagation in the context of online-adaptive intensity-modulated proton therapy (IMPT) for prostate cancer.Methods: A planning and 7–10 repeat CT scans were available of 18 prostate cancer patients. Automatic contour propagation of repeat CT scans was performed using elastix and compared with manual delineations in terms of geometric accuracy and runtime. Dosimetric accuracy was quantified by generating IMPT plans using the propagated contours expanded with a 2 mm (prostate) and 3.5 mm margin (seminal vesicles and lymph nodes) and calculating dosimetric coverage based on the manual delineation. A coverage of V95% ≥ 98% (at least 98% of the target volumes receive at least 95% of the prescribed dose) was considered clinically acceptable.Results: Contour propagation runtime varied between 3 and 30 s for different registration settings. For the fastest setting, 83 in 93 (89.2%), 73 in 93 (78.5%), and 91 in 93 (97.9%) registrations yielded clinically acceptable dosimetric coverage of the prostate, seminal vesicles, and lymph nodes, respectively. For the prostate, seminal vesicles, and lymph nodes the Dice Similarity Coefficient (DSC) was 0.87 ± 0.05, 0.63 ± 0.18, and 0.89 ± 0.03 and the mean surface distance (MSD) was 1.4 ± 0.5 mm, 2.0 ± 1.2 mm, and 1.5 ± 0.4 mm, respectively.Conclusion: With a dosimetric success rate of 78.5–97.9%, this software may facilitate online adaptive IMPT of prostate cancer using a fast, free and open implementation.

Toward adaptive radiotherapy for lung patients: feasibility study on deforming planning CT to CBCT to assess the impact of anatomical changes on dosimetry

Changes in lung architecture during a course of radiotherapy can alter the planned dose distribution to the extent that it becomes clinically unacceptable. This study aims to validate a quantitative method of determining whether a replan is required during the course of conformal radiotherapy. The proposed method uses deformable image registration (DIR) to flexibly map planning CT (pCT) data to the anatomy of online CBCT images. The resulting deformed CT (dCT) images are used as a basis for assessing the effect of anatomical change on dose distributions. The study used retrospective data from a sample of seven replanned lung patients. The settings of an in-house, open-source DIR algorithm were first optimised for CT-to-CBCT registrations of the anatomy of the thorax. Using these optimised parameters, each patient’s pCT was deformed to the CBCT acquired immediately before the replan. Registration accuracy was rigorously validated both geometrically and dosimetrically to confirm that the dCTs could reliably be used to inform replan decisions. A retrospective evaluation of the changes in dose delivered over time was then carried out for a single patient to demonstrate the clinical application of the proposed method. The geometric analysis showed good agreement between deformed structures and those same structures manually outlined on the CBCT images. Results were consistently better than those achieved with rigid-only registration. In the dosimetric analysis, dose distributions derived from the dCTs were found to match closely to the ‘gold standard’ replan CT (rCT) distributions across dose volume histogram and absolute dose difference measures. The retrospective analysis of serial CBCTs of a single patient produced reliable quantitative assessment of the dose delivery. Had the proposed method been available at the time of treatment, it would have enabled a more objective replan decision. DIR is a valuable clinical tool for dose recalculation in adaptive radiotherapy protocols for lung cancer patients.

Influence of deformable image registration on 4D dose simulation for extracranial SBRT: A multi-registration framework study

To evaluate the influence of deformable image registration approaches on correspondence model-based 4D dose simulation in extracranial SBRT by means of open source deformable image registration (DIR) frameworks. Established DIR algorithms of six different open source DIR frameworks were considered and registration accuracy evaluated using freely available 4D image data. Furthermore, correspondence models (regression-based correlation of external breathing signal measurements and internal structure motion field) were built and model accuracy evaluated. Finally, the DIR algorithms were applied for motion field estimation in radiotherapy planning 4D CT data of five lung and five liver lesion patients, correspondence model formation, and model-based 4D dose simulation. Deviations between the original, statically planned and the 4D-simulated VMAT dose distributions were analyzed and correlated to DIR accuracy differences. Registration errors varied among the DIR approaches, with lower DIR accuracy translating into lower correspondence modeling accuracy. Yet, for lung metastases, indices of 4D-simulated dose distributions widely agreed, irrespective of DIR accuracy differences. In contrast, liver metastases 4D dose simulation results strongly vary for the different DIR approaches. Especially in treatment areas with low image contrast (e.g. the liver), DIR-based 4D dose simulation results strongly depend on the applied DIR algorithm, drawing resulting dose simulations and indices questionable.

An anthropomorphic abdominal phantom for deformable image registration accuracy validation in adaptive radiation therapy.

To design and construct a three-dimensional (3D) anthropomorphic abdominal phantom for geometric accuracy and dose summation accuracy evaluations of deformable image registration (DIR) algorithms for adaptive radiation therapy (ART). Organ molds, including liver, kidney, spleen, stomach, vertebra, and two metastasis tumors, were 3D printed using contours from an ovarian cancer patient. The organ molds were molded with deformable gels made of different mixtures of polyvinyl chloride (PVC) and the softener dioctyl terephthalate. Gels with different densities were obtained by a polynomial fitting curve that described the relation between the Hounsfield unit (HU) and PVC-softener blending ratio. The rigid vertebras were constructed by molding of white cement and cellulose pulp. The final abdominal phantom was assembled by arranging all the fabricated organs inside a hollow dummy according to their anatomies, and sealed by deformable gel with averaged HU of muscle and fat. Fiducial landmarks were embedded inside the phantom for spatial accuracy and dose accumulation accuracy studies. Two channels were excavated to facilitate ionization chamber insertion for dosimetric measurements. Phantom properties such as deformable gel elasticity and HU stability were studied. The dosimetric measurement accuracy in the phantom was performed, and the DIR accuracies of three DIR algorithms available in the open source DIR toolkit-DIRART were also validated. The constructed deformable gel showed elastic behavior and was stable in HU values over times, proving to be a practical material for the deformable phantom. The constructed abdominal phantom consisted of realistic anatomies in terms of both anatomical shapes and densities when compared with its reference patient. The dosimetric measurements showed a good agreement with the calculated doses from the treatment planning system. Fiducial-based accuracy analysis conducted on the constructed phantom demonstrated the feasibility of applying the phantom for organ-wise DIR accuracy assessment. We have designed and constructed an anthropomorphic abdominal deformable phantom with satisfactory elastic property, realistic organ density, and anatomy. This physical phantom can be used for routine validations of DIR geometric accuracy and dose accumulation accuracy in ART.

TH‐CD‐206‐08: An Anthropopathic Deformable Phantom for Geometric and Dose Accumulation Accuracy Validation of Deformable Image Registration

Purpose:To design and construct a three‐dimensional (3D) anthropopathic abdominal phantom for evaluating deformable image registration (DIR) accuracy on images and dose deformation in adaptive radiation therapy (ART).Method:Organ moulds, including liver, kidney, spleen, stomach, vertebra and two metastasis tumors, are 3D printed using the contours from an ovarian cancer patient. The organ moulds are molded with deformable gels that made of different mixtures of polyvinyl chloride (PVC) and the softener dioctyl terephthalate. Gels with different densities are obtained by a polynomial fitting curve which describes the relation between the CT number and PVC‐softener blending ratio. The rigid vertebras are constructed by moulding with white cement. The final abdominal phantom is assembled by arranging all the fabricated organs inside a hollow dummy according to their anatomies and sealed with deformable gel with averaged CT number of muscle and fat. Geometric and dosimetric landmarks are embedded inside the phantom for spatial accuracy and dose accumulation accuracy studies. Three DIR algorithms available in the open source DIR toolkit‐DIRART, including the Demons, the Horn‐Schunck and Lucas‐Kanade method and the Level‐Set Motion method, are tested using the constructed phantom.Results:Viscoelastic behavior is observed in the constructed deformable gel, which serves as an ideal material for the deformable phantom. The constructed abdominal phantom consists of highly realistic anatomy and the fabricated organs inside have close CT number to its reference patient. DIR accuracy studies conducted on the constructed phantom using three DIR approaches indicate that geometric accuracy of a DIR algorithm has achieved does not guarantee accuracy in dose accumulation.Conclusions:We have designed and constructed an anthropopathic abdominal deformable phantom with satisfactory elastic property, realistic organ density and anatomy. This physical phantom is recyclable and can be used for routine validations of DIR geometric accuracy and dose accumulation accuracy in ART.This work is supported in part by grant from VARIAN MEDICAL SYSTEMS INC, the National Natural Science Foundation of China (no 81428019 and no 81301940), the Guangdong Natural Science Foundation (2015A030313302) and the 2015 Pearl River S&T Nova Program of Guangzhou (201506010096).

SU‐D‐207A‐05: Investigating Sparse‐Sampled MRI for Motion Management in Thoracic Radiotherapy

Purpose:Sparse sampling and reconstruction‐based MRI techniques represent an attractive strategy to achieve sufficiently high image acquisition speed while maintaining image quality for the task of radiotherapy guidance. In this study, we examine rapid dynamic MRI using a sparse sampling sequence k‐t BLAST in capturing motion‐induced, cycle‐to‐cycle variations in tumor position. We investigate the utility of long‐term MRI‐based motion monitoring as a means of better characterizing respiration‐induced tumor motion compared to a single‐cycle 4DCT.Methods:An MRI‐compatible, programmable, deformable lung motion phantom with eleven 1.5 ml water marker tubes was placed inside a 3.0 T whole‐body MR scanner (Philips Ingenia). The phantom was programmed with 10 lung tumor motion traces previously recorded using the Synchrony system. 2D+t image sequences of a coronal slice were acquired using a balanced‐SSFP sequence combined with k‐t BLAST (accn=3, resolution=0.66×0.66×5 mm3; acquisition time = 110 ms/slice). kV fluoroscopic (ground truth) and 4DCT imaging was performed with the same phantom setup and motion trajectories. Marker positions in all three modalities were segmented and tracked using an opensource deformable image registration package, NiftyReg.Results:Marker trajectories obtained from rapid MRI exhibited <1 mm error compared to kv Fluoro trajectories in the presence of complex motion including baseline shifts and changes in respiratory amplitude, indicating the ability of MRI to monitor motion with adequate geometric fidelity for the purpose of radiotherapy guidance. In contrast, the trajectory derived from 4DCT exhibited significant errors up to 6 mm due to cycle‐to‐cycle variations and baseline shifts. Consequently, 4DCT was found to underestimate the range of marker motion by as much as 50%.Conclusion:Dynamic MRI is a promising tool for radiotherapy motion management as it permits for longterm, dose‐free, soft‐tissue‐based monitoring of motion, yielding richer and more accurate information about tumor position and motion range compared to the current state‐of‐the‐art, 4DCT.This work was partially supported through research funding from National Institutes of Health (R01CA169102).