Statistical modeling of 4D respiratory lung motion using diffeomorphic image registration

Reconstruction of four-dimensional computed tomography lung images by applying spatial and temporal anatomical constraints using a Bayesian model

Current four-dimensional computed tomography (4-D CT) lung image reconstruction methods rely on respiratory gating, such as surrogate, to sort the large number of axial images captured during multiple breathing cycles into serial three-dimensional CT images of different respiratory phases. Such sorting methods may be subject to external surrogate signal noises due to poor reproducibility of breathing cycles. New image-matching-based reconstruction algorithms refine the 4-D CT reconstruction by matching neighboring image slices, and they generally work better for the cine mode of 4-D CT acquisition than the helical mode due to different table positions of axial images in the helical mode. We propose a Bayesian model (BM) based automated 4-D CT lung image reconstruction for helical mode scans. BM allows for applying new spatial and temporal anatomical constraints in the optimization procedure. Using an iterative optimization procedure, each axial image is assigned to a respiratory phase to make sure the anatomical structures are spatially and temporally smooth based on the BM framework. In experiments, we visually and quantitatively compared the results of the proposed BM-based 4-D CT reconstruction with the respiratory surrogate and the normalized cross-correlation based image matching method using both simulated and actual 4-D patient scans. The results indicated that the proposed algorithm yielded more accurate reconstruction and fewer artifacts in the 4-D CT image series.

Estimating dynamic lung images from high-dimension chest surface motion using 4D statistical model

Computed Tomography (CT) has been widely used in image-guided procedures such as intervention and radiotherapy of lung cancer. However, due to poor reproducibility of breath holding or respiratory cycles, discrepancies between static images and patient's current lung shape and tumor location could potentially reduce the accuracy for image guidance. Current methods are either using multiple intra-procedural scans or monitoring respiratory motion with tracking sensors. Although intra-procedural scanning provides more accurate information, it increases the radiation dose and still only provides snapshots of patient's chest. Tracking-based breath monitoring techniques can effectively detect respiratory phases but have not yet provided accurate tumor shape and location due to low dimensional signals. Therefore, estimating the lung motion and generating dynamic CT images from real-time captured high-dimensional sensor signals acts as a key component for image-guided procedures. This paper applies a principal component analysis (PCA)-based statistical model to establish the relationship between lung motion and chest surface motion from training samples, on a template space, and then uses this model to estimate dynamic images for a new patient from the chest surface motion. Qualitative and quantitative results showed that the proposed high-dimensional estimation algorithm yielded more accurate 4D-CT compared to fiducial marker-based estimation.

A Virtual Reality System for PTCD Simulation Using Direct Visuo-Haptic Rendering of Partially Segmented Image Data

This study presents a new visuo-haptic virtual reality (VR) training and planning system for percutaneous transhepatic cholangio-drainage (PTCD) based on partially segmented virtual patient models. We only use partially segmented image data instead of a full segmentation and circumvent the necessity of surface or volume mesh models. Haptic interaction with the virtual patient during virtual palpation, ultrasound probing and needle insertion is provided. Furthermore, the VR simulator includes X-ray and ultrasound simulation for image-guided training. The visualization techniques are GPU-accelerated by implementation in Cuda and include real-time volume deformations computed on the grid of the image data. Computation on the image grid enables straightforward integration of the deformed image data into the visualization components. To provide shorter rendering times, the performance of the volume deformation algorithm is improved by a multigrid approach. To evaluate the VR training system, a user evaluation has been performed and deformation algorithms are analyzed in terms of convergence speed with respect to a fully converged solution. The user evaluation shows positive results with increased user confidence after a training session. It is shown that using partially segmented patient data and direct volume rendering is suitable for the simulation of needle insertion procedures such as PTCD.

An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration

Several biomedical applications require accurate image registration that can cope effectively with complex organ deformations. This paper addresses this problem by introducing a generic deformable registration algorithm with a new regularization scheme, which is performed through bilateral filtering of the deformation field. The proposed approach is primarily designed to handle smooth deformations both between and within body structures, and also more challenging deformation discontinuities exhibited by sliding organs. The conventional Gaussian smoothing of deformation fields is replaced by a bilateral filtering procedure, which compromises between the spatial smoothness and local intensity similarity kernels, and is further supported by a deformation field similarity kernel. Moreover, the presented framework does not require any explicit prior knowledge about the organ motion properties (e.g. segmentation) and therefore forms a fully automated registration technique. Validation was performed using synthetic phantom data and publicly available clinical 4D CT lung data sets. In both cases, the quantitative analysis shows improved accuracy when compared to conventional Gaussian smoothing. In addition, we provide experimental evidence that masking the lungs in order to avoid the problem of sliding motion during registration performs similarly in terms of the target registration error when compared to the proposed approach, however it requires accurate lung segmentation. Finally, quantification of the level and location of detected sliding motion yields visually plausible results by demonstrating noticeable sliding at the pleural cavity boundaries.

Gauss-Newton inspired preconditioned optimization in large deformation diffeomorphic metric mapping

In this work, we propose a novel preconditioned optimization method in the paradigm of Large Deformation Diffeomorphic Metric Mapping (LDDMM). The preconditioned update scheme is formulated for the non-stationary and the stationary parameterizations of diffeomorphisms, yielding three different LDDMM methods. The preconditioning matrices are inspired in the Hessian approximation used in Gauss-Newton method. The derivatives are computed using Frechet differentials. Thus, optimization is performed in a Sobolev space, in contrast to optimization in L(2) commonly used in non-rigid registration literature. The proposed LDDMM methods have been evaluated and compared with their respective implementations of gradient descent optimization. Evaluation has been performed using real and simulated images from the Non-rigid Image Registration Evaluation Project (NIREP). The experiments conducted in this work reported that our preconditioned LDDMM methods achieved a performance similar or superior to well-established-in-literature gradient descent non-stationary LDDMM in the great majority of cases. Moreover, preconditioned optimization showed a substantial reduction in the execution time with an affordable increase of the memory usage per iteration. Additional experiments reported that optimization using Frechet differentials should be preferable to optimization using L(2) differentials.

Gastroscopic image graph: application to noninvasive multitarget tracking under gastroscopy

Gastroscopic examination is one of the most common methods for gastric disease diagnosis. In this paper, a multitarget tracking approach is proposed to assist endoscopists in identifying lesions under gastroscopy. This approach analyzes numerous preobserved gastroscopic images and constructs a gastroscopic image graph. In this way, the deformation registration between gastroscopic images is regarded as a graph search problem. During the procedure, the endoscopist marks suspicious lesions on the screen and the graph is utilized to locate and display the lesions in the appropriate frames based on the calculated registration model. Compared to traditional gastroscopic lesion surveillance methods (e.g., tattooing or probe-based optical biopsy), this approach is noninvasive and does not require additional instruments. In order to assess and quantify the performance, this approach was applied to stomach phantom data and in vivo data. The clinical experimental results demonstrated that the accuracy at angularis, antral, and stomach body was 6.3 ± 2.4 mm, 7.6 ± 3.1 mm, and 7.9 ± 1.6 mm, respectively. The mean accuracy was 7.31 mm, average targeting time was 56 ms, and the P value was 0.032, which makes it an attractive candidate for clinical practice. Furthermore, this approach provides a significant reference for endoscopic target tracking of other soft tissue organs.

Estimation of lung motion fields in 4D CT data by variational non-linear intensity-based registration: A comparison and evaluation study

Accurate and robust estimation of motion fields in respiration-correlated CT (4D CT) images, usually performed by non-linear registration of the temporal CT frames, is a precondition for the analysis of patient-specific breathing dynamics and subsequent image-supported diagnostics and treatment planning. In this work, we present a comprehensive comparison and evaluation study of non-linear registration variants applied to the task of lung motion estimation in thoracic 4D CT data. In contrast to existing multi-institutional comparison studies (e.g. MIDRAS and EMPIRE10), we focus on the specific but common class of variational intensity-based non-parametric registration and analyze the impact of the different main building blocks of the underlying optimization problem: the distance measure to be minimized, the regularization approach and the transformation space considered during optimization. In total, 90 different combinations of building block instances are compared. Evaluated on proprietary and publicly accessible 4D CT images, landmark-based registration errors (TRE) between 1.14 and 1.20 mm for the most accurate registration variants demonstrate competitive performance of the applied general registration framework compared to other state-of-the-art approaches for lung CT registration. Although some specific trends can be observed, effects of interchanging individual instances of the building blocks on the TRE are in general rather small (no single outstanding registration variant existing); the same level of accuracy is, however, associated with significantly different degrees of motion field smoothness and computational demands. Consequently, the building block combination of choice will depend on application-specific requirements on motion field characteristics.

Simulation of range imaging-based estimation of respiratory lung motion. Influence of noise, signal dimensionality and sampling patterns

OBJECTIVES

A major problem associated with the irradiation of thoracic and abdominal tumors is respiratory motion. In clinical practice, motion compensation approaches are frequently steered by low-dimensional breathing signals (e.g., spirometry) and patient-specific correspondence models, which are used to estimate the sought internal motion given a signal measurement. Recently, the use of multidimensional signals derived from range images of the moving skin surface has been proposed to better account for complex motion patterns. In this work, a simulation study is carried out to investigate the motion estimation accuracy of such multidimensional signals and the influence of noise, the signal dimensionality, and different sampling patterns (points, lines, regions).

METHODS

A diffeomorphic correspondence modeling framework is employed to relate multidimensional breathing signals derived from simulated range images to internal motion patterns represented by diffeomorphic non-linear transformations. Furthermore, an automatic approach for the selection of optimal signal combinations/patterns within this framework is presented.

RESULTS

This simulation study focuses on lung motion estimation and is based on 28 4D CT data sets. The results show that the use of multidimensional signals instead of one-dimensional signals significantly improves the motion estimation accuracy, which is, however, highly affected by noise. Only small differences exist between different multidimensional sampling patterns (lines and regions). Automatically determined optimal combinations of points and lines do not lead to accuracy improvements compared to results obtained by using all points or lines.

CONCLUSIONS

Our results show the potential of multidimensional breathing signals derived from range images for the model-based estimation of respiratory motion in radiation therapy.

Personalising population-based respiratory motion models of the heart using neighbourhood approximation based on learnt anatomical features

Respiratory motion models have been proposed for the estimation and compensation of respiratory motion during image acquisition and image-guided interventions on organs in the chest and abdomen. However, such techniques are not commonly used in the clinic. Subject-specific motion models require a dynamic calibration scan that interrupts the clinical workflow and is often impractical to acquire, while population-based motion models are not as accurate as subject-specific motion models. To address this lack of accuracy, we propose a novel personalisation framework for population-based respiratory motion models and demonstrate its application to respiratory motion of the heart. The proposed method selects a subset of the population sample which is more likely to represent the cardiac respiratory motion of an unseen subject, thus providing a more accurate motion model. The selection is based only on anatomical features of the heart extracted from a static image. The features used are learnt using a neighbourhood approximation technique from a set of training datasets for which respiratory motion estimates are available. Results on a population sample of 28 adult healthy volunteers show average improvements in estimation accuracy of 20% compared to a standard population-based motion model, with an average value for the 50th and 95th quantiles of the estimation error of 1.6mm and 4.7 mm respectively. Furthermore, the anatomical features of the heart most strongly correlated to respiratory motion are investigated for the first time, showing the features on the apex in proximity to the diaphragm and the rib cage, on the left ventricle and interventricular septum to be good predictors of the similarity in cardiac respiratory motion.

Model-guided respiratory organ motion prediction of the liver from 2D ultrasound

With the availability of new and more accurate tumour treatment modalities such as high-intensity focused ultrasound or proton therapy, accurate target location prediction has become a key issue. Various approaches for diverse application scenarios have been proposed over the last decade. Whereas external surrogate markers such as a breathing belt work to some extent, knowledge about the internal motion of the organs inherently provides more accurate results. In this paper, we combine a population-based statistical motion model and information from 2d ultrasound sequences in order to predict the respiratory motion of the right liver lobe. For this, the motion model is fitted to a 3d exhalation breath-hold scan of the liver acquired before prediction. Anatomical landmarks tracked in the ultrasound images together with the model are then used to reconstruct the complete organ position over time. The prediction is both spatial and temporal, can be computed in real-time and is evaluated on ground truth over long time scales (5.5 min). The method is quantitatively validated on eight volunteers where the ultrasound images are synchronously acquired with 4D-MRI, which provides ground-truth motion. With an average spatial prediction accuracy of 2.4 mm, we can predict tumour locations within clinically acceptable margins.

Complex lung motion estimation via adaptive bilateral filtering of the deformation field

Estimation of physiologically plausible deformations is critical for several medical applications. For example, lung cancer diagnosis and treatment requires accurate image registration which preserves sliding motion in the pleural cavity, and the rigidity of chest bones. This paper addresses these challenges by introducing a novel approach for regularisation of non-linear transformations derived from a bilateral filter. For this purpose, the classic Gaussian kernel is replaced by a new kernel that smoothes the estimated deformation field with respect to the spatial position, intensity and deformation dissimilarity. The proposed regularisation is a spatially adaptive filter that is able to preserve discontinuity between the lungs and the pleura and reduces any rigid structures deformations in volumes. Moreover, the presented framework is fully automatic and no prior knowledge of the underlying anatomy is required. The performance of our novel regularisation technique is demonstrated on phantom data for a proof of concept as well as 3D inhale and exhale pairs of clinical CT lung volumes. The results of the quantitative evaluation exhibit a significant improvement when compared to the corresponding state-of-the-art method using classic Gaussian smoothing.

Multivariate regression approaches for surrogate-based diffeomorphic estimation of respiratory motion in radiation therapy

Breathing-induced location uncertainties of internal structures are still a relevant issue in the radiation therapy of thoracic and abdominal tumours. Motion compensation approaches like gating or tumour tracking are usually driven by low-dimensional breathing signals, which are acquired in real-time during the treatment. These signals are only surrogates of the internal motion of target structures and organs at risk, and, consequently, appropriate models are needed to establish correspondence between the acquired signals and the sought internal motion patterns. In this work, we present a diffeomorphic framework for correspondence modelling based on the Log-Euclidean framework and multivariate regression. Within the framework, we systematically compare standard and subspace regression approaches (principal component regression, partial least squares, canonical correlation analysis) for different types of common breathing signals (1D: spirometry, abdominal belt, diaphragm tracking; multi-dimensional: skin surface tracking). Experiments are based on 4D CT and 4D MRI data sets and cover intra- and inter-cycle as well as intra- and inter-session motion variations. Only small differences in internal motion estimation accuracy are observed between the 1D surrogates. Increasing the surrogate dimensionality, however, improved the accuracy significantly; this is shown for both 2D signals, which consist of a common 1D signal and its time derivative, and high-dimensional signals containing the motion of many skin surface points. Eventually, comparing the standard and subspace regression variants when applied to the high-dimensional breathing signals, only small differences in terms of motion estimation accuracy are found.

Region-Based Iterative Reconstruction of Structurally Changing Objects in CT

X-ray computed tomography (CT) is a powerful tool for noninvasive imaging of time-varying objects. In the past, methods have been proposed to reconstruct images from continuously changing objects. For discretely or structurally changing objects, however, such methods fail to reconstruct high quality images, mainly because assumptions about continuity are no longer valid. In this paper, we propose a method to reconstruct structurally changing objects. Starting from the observation that there exist regions within the scanned object that remain unchanged over time, we introduce an iterative optimization routine that can automatically determine these regions and incorporate this knowledge in an algebraic reconstruction method. The proposed algorithm was validated on simulation data and experimental μCT data, illustrating its capability to reconstruct structurally changing objects more accurately in comparison to current techniques.

Estimating the 4D respiratory lung motion by spatiotemporal registration and super-resolution image reconstruction

PURPOSE

One of the main challenges in lung cancer radiation therapy is how to reduce the treatment margin but accommodate the geometric uncertainty of moving tumor. 4D-CT is able to provide the full range of motion information for the lung and tumor. However, accurate estimation of lung motion with respect to the respiratory phase is difficult due to various challenges in image registration, e.g., motion artifacts and large interslice thickness in 4D-CT. Meanwhile, the temporal coherence across respiration phases is usually not guaranteed in the conventional registration methods which consider each phase image in 4D-CT independently. To address these challenges, the authors present a unified approach to estimate the respiratory lung motion with two iterative steps.

METHODS

First, the authors propose a novel spatiotemporal registration algorithm to align all phase images of 4D-CT (in low-resolution) to a high-resolution group-mean image in the common space. The temporal coherence of registration is maintained by a set of temporal fibers that delineate temporal correspondences across different respiratory phases. Second, a super-resolution technique is utilized to build the high-resolution group-mean image with more anatomical details than any individual phase image, thus largely alleviating the registration uncertainty especially in correspondence detection. In particular, the authors use the concept of sparse representation to keep the group-mean image as sharp as possible.

RESULTS

The performance of our 4D motion estimation method has been extensively evaluated on both the simulated datasets and real lung 4D-CT datasets. In all experiments, our method achieves more accurate and consistent results in lung motion estimation than all other state-of-the-art approaches under comparison.

CONCLUSIONS

The authors have proposed a novel spatiotemporal registration method to estimate the lung motion in 4D-CT. Promising results have been obtained, which indicates the high applicability of our method in clinical lung cancer radiation therapy.

Hybrid feature-based diffeomorphic registration for tumor tracking in 2-D liver ultrasound images

Real-time ultrasound image acquisition is a pivotal resource in the medical community, in spite of its limited image quality. This poses challenges to image registration methods, particularly to those driven by intensity values. We address these difficulties in a novel diffeomorphic registration technique for tumor tracking in series of 2-D liver ultrasound. Our method has two main characteristics: 1) each voxel is described by three image features: intensity, local phase, and phase congruency; 2) we compute a set of forces from either local information (Demons-type of forces), or spatial correspondences supplied by a block-matching scheme, from each image feature. A family of update deformation fields which are defined by these forces, and inform upon the local or regional contribution of each image feature are then composed to form the final transformation. The method is diffeomorphic, which ensures the invertibility of deformations. The qualitative and quantitative results yielded by both synthetic and real clinical data show the suitability of our method for the application at hand.

Customized prediction of respiratory motion with clustering from multiple patient interaction

Information processing of radiotherapy systems has become an important research area for sophisticated radiation treatment methodology. Geometrically precise delivery of radiotherapy in the thorax and upper abdomen is compromised by respiratory motion during treatment. Accurate prediction of the respiratory motion would be beneficial for improving tumor targeting. However, a wide variety of breathing patterns can make it difficult to predict the breathing motion with explicit models. We proposed a respiratory motion predictor, that is, customized prediction with multiple patient interactions using neural network (CNN). For the preprocedure of prediction for individual patient, we construct the clustering based on breathing patterns of multiple patients using the feature selection metrics that are composed of a variety of breathing features. In the intraprocedure, the proposed CNN used neural networks (NN) for a part of the prediction and the extended Kalman filter (EKF) for a part of the correction. The prediction accuracy of the proposed method was investigated with a variety of prediction time horizons using normalized root mean squared error (NRMSE) values in comparison with the alternate recurrent neural network (RNN). We have also evaluated the prediction accuracy using the marginal value that can be used as the reference value to judge how many signals lie outside the confidence level. The experimental results showed that the proposed CNN can outperform RNN with respect to the prediction accuracy with an improvement of 50%.

Automated volumes-of-interest identification for classical and atypical Parkinsonian syndrome differentiation using T2' MR imaging

OBJECTIVES

In clinical routine, patients with classical Parkinsonian syndromes (CPS) need to be differentiated from those with atypical Parkinsonian syndromes (APS), particularly with respect to prognosis and treatment decision. To date, this diagnosis is mainly based on clinical criteria, leading to failure rates up to 25%, motivating the development of image-based decision support systems. Magnetic resonance imaging (MRI) and in particular T2´ image sequences have been suggested as a potential marker for differential diagnosis. The aim of this study was to investigate whether automatically identified T2´ volumes-of-interest (VOIs) can be used for an automatic differentiation of CPS and APS patients.

MATERIAL AND METHODS

74 MRI datasets were available for this hypothesis generating trial, including image sequences from 24 healthy subjects, 33 CPS and 17 APS patients. First, a problem-specific reference atlas was generated using the healthy control datasets. Next, patients' datasets were registered to the atlas. Voxel-wise t-tests, reflecting significance levels of T2´ value differences between CPS and APS patients, were then applied for calculation of a p-map. Finally, the calculated p-map was thresholded and a connected component analysis was performed for final VOI detection. In parallel, manually defined VOIs were determined in grey and white matter for comparison.

RESULTS

Three VOIs in parts of the basal ganglia and the left occipital lobe were automatically identified by the presented method. There was a trend for higher area under the curve on multivariable receiver operating characteristic curves for automatically determined VOIs over manually defined VOIs (0.939 vs. 0.818, p = 0.0572).

CONCLUSION

The diagnostic role of automatically defined VOIs in differentiation of CPS and APS patients based on T2´ image sequences should be further investigated.

Irregular breathing classification from multiple patient datasets using neural networks

Complicated breathing behaviors including uncertain and irregular patterns can affect the accuracy of predicting respiratory motion for precise radiation dose delivery [3-6, 25, 36]. So far investigations on irregular breathing patterns have been limited to respiratory monitoring of only extreme inspiration and expiration [37]. Using breathing traces acquired on a Cyberknife treatment facility, we retrospectively categorized breathing data into several classes based on the extracted feature metrics derived from breathing data of multiple patients. The novelty of this paper is that the classifier using neural networks can provide clinical merit for the statistical quantitative modeling of irregular breathing motion based on a regular ratio representing how many regular/irregular patterns exist within an observation period. We propose a new approach to detect irregular breathing patterns using neural networks, where the reconstruction error can be used to build the distribution model for each breathing class. The proposed irregular breathing classification used a regular ratio to decide whether or not the current breathing patterns were regular. The sensitivity, specificity, and receiver operating characteristic (ROC) curve of the proposed irregular breathing pattern detector was analyzed. The experimental results of 448 patients breathing patterns validated the proposed irregular breathing classifier.

Regression-based cardiac motion prediction from single-phase CTA

State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3±0.5 mm, compared to values of 2.7±0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure.

Towards more precise, minimally-invasive tumour treatment under free breathing

In recent years, significant advances have been made towards compensating respiratory organ motion for the treatment of tumours, e.g. for the liver. Among the most promising approaches are statistical population models of organ motion. In this paper we give an overview on our work in the field.We explain how 4D motion data can be acquired, how these motion models can then be built and applied in realistic scenarios. The application of the motion models is first shown on a case where 3D surrogate marker data is available. Then we will evaluate the prediction accuracy if only 2D and lastly 1D surrogate marker motion data is available. For all three scenarios we will give quantitative prediction accuracy results.

A 4D statistical shape model for automated segmentation of lungs with large tumors

Segmentation of lungs with large tumors is a challenging and time-consuming task, especially for 4D CT data sets used in radiation therapy. Existing lung segmentation methods are ineffective in these cases, because they are either not able to deal with large tumors and/or process every 3D image independently neglecting temporal information. In this paper, we present a approach for model-based 4D segmentation of lungs with large tumors in 4D CT data sets. In our approach, a 4D statistical shape model that accounts for inter- and intra-patient variability is fitted to the 4D image sequence, and the segmentation result is refined by a 4D graph-based optimal surface finding. The approach is evaluated using 10 4D CT data sets of lung tumor patients. The segmentation results are compared with a standard intensity-based approach and a 3D version of the presented model-based segmentation method. The intensity-based approach shows a better performance for normal lungs, however, fails in presence of large lung tumors. Although overall performance of 3D and 4D model-based segmentation is similar, the results indicate improved temporal coherence and improved robustness with respect to the segmentation parameters for the 4D model-based segmentation.

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 intrapatient 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 configuration 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.

Towards accurate dose accumulation for Step-&-Shoot IMRT: Impact of weighting schemes and temporal image resolution on the estimation of dosimetric motion effects

PURPOSE

Breathing-induced motion effects on dose distributions in radiotherapy can be analyzed using 4D CT image sequences and registration-based dose accumulation techniques. Often simplifying assumptions are made during accumulation. In this paper, we study the dosimetric impact of two aspects which may be especially critical for IMRT treatment: the weighting scheme for the dose contributions of IMRT segments at different breathing phases and the temporal resolution of 4D CT images applied for dose accumulation.

METHODS

Based on a continuous problem formulation a patient- and plan-specific scheme for weighting segment dose contributions at different breathing phases is derived for use in step-&-shoot IMRT dose accumulation. Using 4D CT data sets and treatment plans for 5 lung tumor patients, dosimetric motion effects as estimated by the derived scheme are compared to effects resulting from a common equal weighting approach. Effects of reducing the temporal image resolution are evaluated for the same patients and both weighting schemes.

RESULTS

The equal weighting approach underestimates dosimetric motion effects when considering single treatment fractions. Especially interplay effects (relative misplacement of segments due to respiratory tumor motion) for IMRT segments with only a few monitor units are insufficiently represented (local point differences >25% of the prescribed dose for larger tumor motion). The effects, however, tend to be averaged out over the entire treatment course. Regarding temporal image resolution, estimated motion effects in terms of measures of the CTV dose coverage are barely affected (in comparison to the full resolution) when using only half of the original resolution and equal weighting. In contrast, occurence and impact of interplay effects are poorly captured for some cases (large tumor motion, undersized PTV margin) for a resolution of 10/14 phases and the more accurate patient- and plan-specific dose accumulation scheme.

CONCLUSIONS

Radiobiological consequences of reported single fraction local point differences >25% of the prescribed dose are widely unclear and should be subject to future investigation. Meanwhile, if aiming at accurate and reliable estimation of dosimetric motion effects, precise weighting schemes such as the presented patient- and plan-specific scheme for step-&-shoot IMRT and full available temporal 4D CT image resolution should be applied for IMRT dose accumulation.

Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator

Respiratory motion models have potential application for estimating and correcting the effects of motion in a wide range of applications, for example in PET-MR imaging. Given that motion cycles caused by breathing are only approximately repeatable, an important quality of such models is their ability to capture and estimate the intra- and inter-cycle variability of the motion. In this paper we propose and describe a technique for free-form nonrigid respiratory motion correction in the thorax. Our model is based on a principal component analysis of the motion states encountered during different breathing patterns, and is formed from motion estimates made from dynamic 3-D MRI data. We apply our model using a data-driven technique based on a 2-D MRI image navigator. Unlike most previously reported work in the literature, our approach is able to capture both intra- and inter-cycle motion variability. In addition, the 2-D image navigator can be used to estimate how applicable the current motion model is, and hence report when more imaging data is required to update the model. We also use the motion model to decide on the best positioning for the image navigator. We validate our approach using MRI data acquired from 10 volunteers and demonstrate improvements of up to 40.5% over other reported motion modelling approaches, which corresponds to 61% of the overall respiratory motion present. Finally we demonstrate one potential application of our technique: MRI-based motion correction of real-time PET data for simultaneous PET-MRI acquisition.

On a PCA-based lung motion model

Respiration-induced organ motion is one of the major uncertainties in lung cancer radiotherapy and is crucial to be able to accurately model the lung motion. Most work so far has focused on the study of the motion of a single point (usually the tumor center of mass), and much less work has been done to model the motion of the entire lung. Inspired by the work of Zhang et al (2007 Med. Phys. 34 4772-81), we believe that the spatiotemporal relationship of the entire lung motion can be accurately modeled based on principle component analysis (PCA) and then a sparse subset of the entire lung, such as an implanted marker, can be used to drive the motion of the entire lung (including the tumor). The goal of this work is twofold. First, we aim to understand the underlying reason why PCA is effective for modeling lung motion and find the optimal number of PCA coefficients for accurate lung motion modeling. We attempt to address the above important problems both in a theoretical framework and in the context of real clinical data. Second, we propose a new method to derive the entire lung motion using a single internal marker based on the PCA model. The main results of this work are as follows. We derived an important property which reveals the implicit regularization imposed by the PCA model. We then studied the model using two mathematical respiratory phantoms and 11 clinical 4DCT scans for eight lung cancer patients. For the mathematical phantoms with cosine and an even power (2n) of cosine motion, we proved that 2 and 2n PCA coefficients and eigenvectors will completely represent the lung motion, respectively. Moreover, for the cosine phantom, we derived the equivalence conditions for the PCA motion model and the physiological 5D lung motion model (Low et al 2005 Int. J. Radiat. Oncol. Biol. Phys. 63 921-9). For the clinical 4DCT data, we demonstrated the modeling power and generalization performance of the PCA model. The average 3D modeling error using PCA was within 1 mm (0.7 ± 0.1 mm). When a single artificial internal marker was used to derive the lung motion, the average 3D error was found to be within 2 mm (1.8 ± 0.3 mm) through comprehensive statistical analysis. The optimal number of PCA coefficients needs to be determined on a patient-by-patient basis and two PCA coefficients seem to be sufficient for accurate modeling of the lung motion for most patients. In conclusion, we have presented thorough theoretical analysis and clinical validation of the PCA lung motion model. The feasibility of deriving the entire lung motion using a single marker has also been demonstrated on clinical data using a simulation approach.

Estimating the 4D respiratory lung motion by spatiotemporal registration and building super-resolution image

The estimation of lung motion in 4D-CT with respect to the respiratory phase becomes more and more important for radiation therapy of lung cancer. Modem CT scanner can only scan a limited region of body at each couch table position. Thus, motion artifacts due to the patient's free breathing during scan are often observable in 4D-CT, which could undermine the procedure of correspondence detection in the registration. Another challenge of motion estimation in 4D-CT is how to keep the lung motion consistent over time. However, the current approaches fail to meet this requirement since they usually register each phase image to a pre-defined phase image independently, without considering the temporal coherence in 4D-CT. To overcome these limitations, we present a unified approach to estimate the respiratory lung motion with two iterative steps. First, we propose a new spatiotemporal registration algorithm to align all phase images of 4D-CT (in low-resolution) onto a high-resolution group-mean image in the common space. The temporal consistency is persevered by introducing the concept of temporal fibers for delineating the spatiotemporal behavior of lung motion along the respiratory phase. Second, the idea of super resolution is utilized to build the group-mean image with more details, by integrating the highly-redundant image information contained in the multiple respiratory phases. Accordingly, by establishing the correspondence of each phase image w.r.t. the high-resolution group-mean image, the difficulty of detecting correspondences between original phase images with missing structures is greatly alleviated, thus more accurate registration results can be achieved. The performance of our proposed 4D motion estimation method has been extensively evaluated on a public lung dataset. In all experiments, our method achieves more accurate and consistent results in lung motion estimation than all other state-of-the-art approaches.