Simon Rit

Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intra patient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the con figuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.

On-the-fly motion-compensated cone-beam CT using an a priori model of the respiratory motion

Respiratory motion causes artifacts in cone-beam (CB) CT images acquired on slow rotating scanners integrated with linear accelerators. Respiration-correlated CBCT has been proposed to correct for the respiratory motion but only a subset of the CB projections is used to reconstruct each frame of the 4D CBCT image and, therefore, adequate image quality requires long acquisition times. In this article, the authors develop an on-the-fly solution to estimate and compensate for the respiratory motion in the reconstruction of a 3D CBCT image from all the CB projections. An a priori motion model of the patient respiratory cycle is estimated from the 4D planning CT. During the acquisition, the model is correlated with the respiration using a respiratory signal extracted from the CB projections. The estimated motion is next compensated for in an optimized reconstruction algorithm. The motion compensated for is forced to be null on average over the acquisition time to ensure that the compensation results in a CBCT image which describes the mean position of each organ, even if the a priori motion model is inaccurate. Results were assessed on simulated, phantom, and patient data. In all experiments, blur was visually reduced by motion-compensated CBCT. Simulations showed robustness to inaccuracies of the motion model observed on patient data such as amplitude variations, phase shifts, and setup errors, thus proving the efficiency of the compensation using an a priori motion model. Noise and view-aliasing artifacts were lower on motion-compensated CBCT images with 1 min scan than on respiration-correlated CBCT images with 4 min scan. Finally, on-the-fly motion estimation and motion-compensated reconstruction were within the acquisition time of the CB projections and the CBCT image available a few seconds after the end of the acquisition. In conclusion, the authors developed and implemented a method for correcting the respiratory motion during the treatment fractions which can replace respiration-correlated CBCT for improving image quality while decreasing acquisition time.

Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs

PURPOSE Four-dimensional computed tomography (4D CT) can provide patient-specific motion information for radiotherapy planning and delivery. Motion estimation in 4D CT is challenging due to the reduced image quality and the presence of artifacts. We aim to improve the robustness of deformable registration applied to respiratory-correlated imaging of the lungs, by using a global problem formulation and pursuing a restrictive parametrization for the spatiotemporal deformation model. METHODS A spatial transformation based on free-form deformations was extended to the temporal domain, by explicitly modeling the trajectory using a cyclic temporal model based on B-splines. A global registration criterion allowed to consider the entire image sequence simultaneously and enforce the temporal coherence of the deformation throughout the respiratory cycle. To ensure a parametrization capable of capturing the dynamics of respiratory motion, a prestudy was performed on the temporal dimension separately. The temporal parameters were tuned by fitting them to diaphragm motion data acquired for a large patient group. Suitable properties were retained and applied to spatiotemporal registration of 4D CT data. Registration results were validated using large sets of landmarks and compared to consecutive spatial registrations. To illustrate the benefit of the spatiotemporal approach, we also assessed the performance in the presence of motion-induced artifacts. RESULTS Cubic B-splines gave better or similar fitting results as lower orders and were selected because of their inherently stronger regularization. The fitting and registration errors increased gradually with the temporal control point spacing, representing a trade-off between achievable accuracy and sensitivity to noise and artifacts. A piecewise smooth trajectory model, allowing for a discontinuous change of speed at end-inhale, was found most suitable to account for the sudden changes of motion at this breathing phase. The spatiotemporal modeling allowed a reduction of the number of parameters of 45%, while maintaining registration accuracy within 0.1 mm. The approach reduced the sensitivity to artifacts. CONCLUSIONS Spatiotemporal registration can provide accurate motion estimation for 4D CT and improves the robustness to artifacts.

The Reconstruction Toolkit (RTK), an open-source cone-beam CT reconstruction toolkit based on the Insight Toolkit (ITK)

Purpose: To develop an open-source toolkit for fast cone-beam CT reconstruction based on the Insight Toolkit. Methods: We have started the Reconstruction Toolkit (RTK, http://www.openrtk.org), an open-source toolkit for cone-beam CT reconstruction, based on the Insight Toolkit (ITK, http://www.itk.org/) and using GPU code extracted from Plastimatch (http://www.plastimatch.org/). RTK is developed by an open consortium (see affiliations) under the non- contaminating Apache 2.0 license. The quality of the platform is daily checked with regression tests in partnership with Kitware which already supports ITK. Results: Several features are already available: Elekta, Varian and IBA inputs, multi-threaded Feldkamp-David-Kress reconstruction on CPU and GPU, Parker short scan weighting, multi-threaded CPU and GPU forward projectors, etc. Each feature is either accessible through command line tools or C++ classes that can be included in independent software. A MIDAS community (http://midas3.kitware.com) has been opened to provide CatPhan datasets of several vendors (Elekta, Varian and IBA). RTK will be used in the upcoming cone-beam CT scanner developed by IBA for proton therapy rooms. Many features are under development: new input format support, iterative reconstruction, hybrid Monte Carlo / deterministic CBCT simulation, etc. Conclusions: RTK has been built to freely share tomographic reconstruction development between researchers and is open for new contributions.

Comparison of Analytic and Algebraic Methods for Motion-Compensated Cone-Beam CT Reconstruction of the Thorax

Respiratory motion is a major concern in cone-beam (CB) computed tomography (CT) of the thorax. It causes artifacts such as blur, streaks, and bands, in particular when using slow-rotating scanners mounted on the gantry of linear accelerators. In this paper, we compare two approaches for motion-compensated CBCT reconstruction of the thorax. The first one is analytic; it is heuristically adapted from the method of Feldkamp, Davis, and Kress (FDK). The second one is algebraic: the system of linear equations is generated using a new algorithm for the projection of deformable volumes and solved using the simultaneous algebraic reconstruction technique (SART). For both methods, we propose to estimate the motion on patient data using a previously acquired 4-D CT image. The methods were tested on two digital and one mechanical motion-controlled phantoms and on a patient dataset. Our results indicate that the two methods correct most motion artifacts. However, the analytic method does not fully correct streaks and bands even if the motion is perfectly estimated due to the underlying approximation. In contrast, the algebraic method allows us full correction of respiratory-induced artifacts.

Automated segmentation of a motion mask to preserve sliding motion in deformable registration of thoracic CT

PURPOSE Deformable registration generally relies on the assumption that the sought spatial transformation is smooth. Yet, breathing motion involves sliding of the lung with respect to the chest wall, causing a discontinuity in the motion field, and the smoothness assumption can lead to poor matching accuracy. In response, alternative registration methods have been proposed, several of which rely on prior segmentations. We propose an original method for automatically extracting a particular segmentation, called a motion mask, from a CT image of the thorax. METHODS The motion mask separates moving from less-moving regions, conveniently allowing simultaneous estimation of their motion, while providing an interface where sliding occurs. The sought segmentation is subanatomical and based on physiological considerations, rather than organ boundaries. We therefore first extract clear anatomical features from the image, with respect to which the mask is defined. Level sets are then used to obtain smooth surfaces interpolating these features. The resulting procedure comes down to a monitored level set segmentation of binary label images. The method was applied to sixteen inhale-exhale image pairs. To illustrate the suitability of the motion masks, they were used during deformable registration of the thorax. RESULTS For all patients, the obtained motion masks complied with the physiological requirements and were consistent with respect to patient anatomy between inhale and exhale. Registration using the motion mask resulted in higher matching accuracy for all patients, and the improvement was statistically significant. Registration performance was comparable to that obtained using lung masks when considering the entire lung region, but the use of motion masks led to significantly better matching near the diaphragm and mediastinum, for the bony anatomy and for the trachea. The use of the masks was shown to facilitate the registration, allowing to reduce the complexity of the spatial transformation considerably, while maintaining matching accuracy. CONCLUSIONS We proposed an automated segmentation method for obtaining motion masks, capable of facilitating deformable registration of the thorax. The use of motion masks during registration leads to matching accuracies comparable to the use of lung masks for the lung region but motion masks are more suitable when registering the entire thorax.

Registration of sliding objects using direction dependent B-splines decomposition.

Sliding motion is a challenge for deformable image registration because it leads to discontinuities in the sought deformation. In this paper, we present a method to handle sliding motion using multiple B-spline transforms. The proposed method decomposes the sought deformation into sliding regions to allow discontinuities at their interfaces, but prevents unrealistic solutions by forcing those interfaces to match. The method was evaluated on 16 lung cancer patients against a single B-spline transform approach and a multi B-spline transforms approach without the sliding constraint at the interface. The target registration error (TRE) was significantly lower with the proposed method (TRE = 1.5 mm) than with the single B-spline approach (TRE = 3.7 mm) and was comparable to the multi B-spline approach without the sliding constraint (TRE = 1.4 mm). The proposed method was also more accurate along region interfaces, with 37% less gaps and overlaps when compared to the multi B-spline transforms without the sliding constraint.

Is abdominal compression useful in lung stereotactic body radiation therapy? A 4DCT and dosimetric lobe-dependent study

PURPOSE To determine the usefulness of abdominal compression in lung stereotactic body radiation therapy (SBRT) depending on lobe tumor location. MATERIALS AND METHODS Twenty-seven non-small cell lung cancer patients were immobilized in the Stereotactic Body Frame™ (Elekta). Eighteen tumors were located in an upper lobe, one in the middle lobe and nine in a lower lobe (one patient had two lesions). All patients underwent two four-dimensional computed tomography (4DCT) scans, with and without abdominal compression. Three-dimensional tumor motion amplitude was determined using manual landmark annotation. We also determined the internal target volume (ITV) and the influence of abdominal compression on lung dose-volume histograms. RESULTS The mean reduction of tumor motion amplitude was 3.5 mm (p = 0.009) for lower lobe tumors and 0.8 mm (p = 0.026) for upper/middle lobe locations. Compression increased tumor motion in 5 cases. Mean ITV reduction was 3.6 cm(3) (p = 0.039) for lower lobe and 0.2 cm(3) (p = 0.048) for upper/middle lobe lesions. Dosimetric gain of the compression for lung sparing was not clinically relevant. CONCLUSIONS The most significant impact of abdominal compression was obtained in patients with lower lobe tumors. However, minor or negative effects of compression were reported for other patients and lung sparing was not substantially improved. At our institute, patients with upper or middle lobe lesions are now systematically treated without compression and the usefulness of compression for lower lobe tumors is evaluated on an individual basis.

Quantification of the Variability of Diaphragm Motion and Implications for Treatment Margin Construction

PURPOSE To quantify the variability of diaphragm motion during free-breathing radiotherapy of lung patients and its effect on treatment margins to account for geometric uncertainties. METHODS AND MATERIALS Thirty-three lung cancer patients were analyzed. Each patient had 5-19 cone-beam scans acquired during different treatment fractions. The craniocaudal position of the diaphragm dome on the same side as the tumor was tracked over 2 min in the projection images, because it is both easily visible and a suitable surrogate to study the variability of the tumor motion and its impact on treatment margins. Intra-acquisition, inter-acquisition, and inter-patient variability of the respiratory cycles were quantified separately, as were the probability density functions (PDFs) of the diaphragm position over each cycle, each acquisition, and each patient. Asymmetric margins were simulated using each patient PDF and compared to symmetric margins computed from a margin recipe. RESULTS The peak-to-peak amplitude variability (1 SD) was 3.3 mm, 2.4 mm, and 6.1 mm for the intra-acquisition, inter-acquisition, and inter-patient variability, respectively. The average PDF of each cycle was similar to the sin(4) function but the PDF of each acquisition was closer to a skew-normal distribution because of the motion variability. Despite large interfraction baseline variability, the PDF of each patient was generally asymmetric with a longer end-inhale tail because the end-exhale position was more stable than the end-inhale position. The asymmetry of the PDF required asymmetric margins around the time-averaged position to account for the position uncertainty but the average difference was 1.0 mm (range, 0.0-4.4 mm) for a sharp penumbra and an idealized online setup correction protocol. CONCLUSION The respiratory motion is more irregular during the fractions than between the fractions. The PDF of the respiratory motion is asymmetrically distributed. Both the intra-acquisition variability and the PDF asymmetry have a limited impact on dose distributions and inferred margins. The use of a margin recipe to account for respiratory motion with an estimate of the average motion amplitude was adequate in almost all patients.

Filtered backprojection proton CT reconstruction along most likely paths

PURPOSE Proton CT (pCT) has the potential to accurately measure the electron density map of tissues at low doses but the spatial resolution is prohibitive if the curved paths of protons in matter is not accounted for. The authors propose to account for an estimate of the most likely path of protons in a filtered backprojection (FBP) reconstruction algorithm. METHODS The energy loss of protons is first binned in several proton radiographs at different distances to the proton source to exploit the depth-dependency of the estimate of the most likely path. This process is named the distance-driven binning. A voxel-specific backprojection is then used to select the adequate radiograph in the distance-driven binning in order to propagate in the pCT image the best achievable spatial resolution in proton radiographs. The improvement in spatial resolution is demonstrated using Monte Carlo simulations of resolution phantoms. RESULTS The spatial resolution in the distance-driven binning depended on the distance of the objects from the source and was optimal in the binned radiograph corresponding to that distance. The spatial resolution in the reconstructed pCT images decreased with the depth in the scanned object but it was always better than previous FBP algorithms assuming straight line paths. In a water cylinder with 20 cm diameter, the observed range of spatial resolutions was 0.7 - 1.6 mm compared to 1.0 - 2.4 mm at best with a straight line path assumption. The improvement was strongly enhanced in shorter 200° scans. CONCLUSIONS Improved spatial resolution was obtained in pCT images with filtered backprojection reconstruction using most likely path estimates of protons. The improvement in spatial resolution combined with the practicality of FBP algorithms compared to iterative reconstruction algorithms makes this new algorithm a candidate of choice for clinical pCT.