René Werner

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.

An optical flow based method for improved reconstruction of 4D CT data sets acquired during free breathing

Respiratory motion degrades anatomic position reproducibility and leads to issues affecting image acquisition, treatment planning, and radiation delivery. Four-dimensional (4D) computer tomography (CT) image acquisition can be used to measure the impact of organ motion and to explicitly account for respiratory motion during treatment planning and radiation delivery. Modern CT scanners can only scan a limited region of the body simultaneously and patients have to be scanned in segments consisting of multiple slices. A respiratory signal (spirometer signal or surface tracking) is used to reconstruct a 4D data set by sorting the CT scans according to the couch position and signal coherence with predefined respiratory phases. But artifacts can occur if there are no acquired data segments for exactly the same respiratory state for all couch positions. These artifacts are caused by device-dependent limitations of gantry rotation, image reconstruction times and by the variability of the patients respiratory pattern. In this paper an optical flow based method for improved reconstruction of 4D CT data sets from multislice CT scans is presented. The optical flow between scans at neighboring respiratory states is estimated by a non-linear registration method. The calculated velocity field is then used to reconstruct a 4D CT data set by interpolating data at exactly the predefined respiratory phase. Our reconstruction method is compared with the usually used reconstruction based on amplitude sorting. The procedures described were applied to reconstruct 4D CT data sets for four cancer patients and a qualitative and quantitative evaluation of the optical flow based reconstruction method was performed. Evaluation results show a relevant reduction of reconstruction artifacts by our technique. The reconstructed 4D data sets were used to quantify organ displacements and to visualize the abdominothoracic organ motion.

Statistical Modeling of 4D Respiratory Lung Motion Using Diffeomorphic Image Registration

Modeling of respiratory motion has become increasingly important in various applications of medical imaging (e.g., radiation therapy of lung cancer). Current modeling approaches are usually confined to intra-patient registration of 3D image data representing the individual patients anatomy at different breathing phases. We propose an approach to generate a mean motion model of the lung based on thoracic 4D computed tomography (CT) data of different patients to extend the motion modeling capabilities. Our modeling process consists of three steps: an intra-subject registration to generate subject-specific motion models, the generation of an average shape and intensity atlas of the lung as anatomical reference frame, and the registration of the subject-specific motion models to the atlas in order to build a statistical 4D mean motion model (4D-MMM). Furthermore, we present methods to adapt the 4D mean motion model to a patient-specific lung geometry. In all steps, a symmetric diffeomorphic nonlinear intensity-based registration method was employed. The Log-Euclidean framework was used to compute statistics on the diffeomorphic transformations. The presented methods are then used to build a mean motion model of respiratory lung motion using thoracic 4D CT data sets of 17 patients. We evaluate the model by applying it for estimating respiratory motion of ten lung cancer patients. The prediction is evaluated with respect to landmark and tumor motion, and the quantitative analysis results in a mean target registration error (TRE) of 3.3 ±1.6 mm if lung dynamics are not impaired by large lung tumors or other lung disorders (e.g., emphysema). With regard to lung tumor motion, we show that prediction accuracy is independent of tumor size and tumor motion amplitude in the considered data set. However, tumors adhering to non-lung structures degrade local lung dynamics significantly and the model-based prediction accuracy is lower in these cases. The statistical respiratory motion model is capable of providing valuable prior knowledge in many fields of applications. We present two examples of possible applications in radiation therapy and image guided diagnosis.

Patient-specific finite element modeling of respiratory lung motion using 4D CT image data.

Development and optimization of methods for adequately accounting for respiratory motion in radiation therapy of thoracic tumors require detailed knowledge of respiratory dynamics and its impact on corresponding dose distributions. Thus, computer aided modeling and simulation of respiratory motion have become increasingly important. In this article a biophysical approach for modeling respiratory lung motion is described: Major aspects of the process of lung ventilation are formulated as a contact problem of elasticity theory which is solved by finite element methods; lung tissue is assumed to be isotropic, homogeneous, and linearly elastic. A main focus of the article is to assess the impact of biomechanical parameters (values of elastic constants) on the modeling process and to evaluate modeling accuracy. Patient-specific models are generated based on 4D CT data of 12 lung tumor patients. Simulated motion patterns of inner lung landmarks are compared with corresponding motion patterns observed in the 4D CT data. Mean absolute differences between model-based predicted landmark motion and corresponding breathing-induced landmark displacements as observed in the CT data sets are in the order of 3 mm (end expiration to end inspiration) and 2 mm (end expiration to midrespiration). Modeling accuracy decreases with increasing tumor size both locally (landmarks close to tumor) and globally (landmarks in other parts of the lung). The impact of the values of the elastic constants appears to be small. Outcomes show that the modeling approach is an adequate strategy in predicting lung dynamics due to lung ventilation. Nevertheless, the decreased prediction quality in cases of large tumors demands further study of the influence of lung tumors on global and local lung elasticity properties.

Estimation of slipping organ motion by registration with direction-dependent regularization

Accurate estimation of respiratory motion is essential for many applications in medical 4D imaging, for example for radiotherapy of thoracic and abdominal tumors. It is usually done by non-linear registration of image scans at different states of the breathing cycle but without further modeling of specific physiological motion properties. In this context, the accurate computation of respiration-driven lung motion is especially challenging because this organ is sliding along the surrounding tissue during the breathing cycle, leading to discontinuities in the motion field. Without considering this property in the registration model, common intensity-based algorithms cause incorrect estimation along the object boundaries. In this paper, we present a model for incorporating slipping motion in image registration. Extending the common diffusion registration by distinguishing between normal- and tangential-directed motion, we are able to estimate slipping motion at the organ boundaries while preventing gaps and ensuring smooth motion fields inside and outside. We further present an algorithm for a fully automatic detection of discontinuities in the motion field, which does not rely on a prior segmentation of the organ. We evaluate the approach for the estimation of lung motion based on 23 inspiration/expiration pairs of thoracic CT images. The results show a visually more plausible motion estimation. Moreover, the target registration error is quantified using manually defined landmarks and a significant improvement over the standard diffusion regularization is shown.

4D medical image computing and visualization of lung tumor mobility in spatio-temporal CT image data

The development of 4D CT imaging has introduced the possibility of measuring breathing motion of tumors and inner organs. Conformal thoracic radiation therapy relies on a quantitative understanding of the position of lungs, lung tumors, and other organs during radiation delivery. Using 4D CT data sets, medical image computing and visualization methods were developed to visualize different aspects of lung and lung tumor mobility during the breathing cycle and to extract quantitative motion parameters. A non-linear registration method was applied to estimate the three-dimensional motion field and to compute 3D point trajectories. Specific visualization techniques were used to display the resulting motion field, the tumors appearance probabilities during a breathing cycle as well as the volume covered by the moving tumor. Furthermore, trajectories of the tumor center-of-mass and organ specific landmarks were computed for the quantitative analysis of tumor and organ motion. The analysis of 4D data sets of seven patients showed that tumor mobility differs significantly between the patients depending on the individual breathing pattern and tumor location.

Slipping Objects in Image Registration: Improved Motion Field Estimation with Direction-Dependent Regularization

The computation of accurate motion fields is a crucial aspect in 4D medical imaging. It is usually done using a non-linear registration without further modeling of physiological motion properties. However, a globally homogeneous smoothing (regularization) of the motion field during the registration process can contradict the characteristics of motion dynamics. This is particularly the case when two organs slip along each other which leads to discontinuities in the motion field. In this paper, we present a diffusion-based model for incorporating physiological knowledge in image registration. By decoupling normal- and tangential-directed smoothing, we are able to estimate slipping motion at the organ borders while ensuring smooth motion fields in the inside and preventing gaps to arise in the field. We evaluate our model focusing on the estimation of respiratory lung motion. By accounting for the discontinuous motion of visceral and parietal pleurae, we are able to show a significant increase of registration accuracy with respect to the target registration error (TRE).

Technical Note: Development of a tidal volume surrogate that replaces spirometry for physiological breathing monitoring in 4D CT

PURPOSE Spirometry exhibits baseline drift and frequent measurement errors so it cannot be used by itself to provide tidal volume-based image sorting or breathing motion modeling. Other breathing surrogates, in this study an abdominal bellows system, are drift free but do not measure tidal volume. Simultaneously using spirometry and the bellows system allows the user to convert the recorded bellows signal to tidal volume but still relies on spirometry measurements. The authors therefore propose to use CT-based air content, rather than a spirometer, to convert the bellows signal to tidal volume. METHODS 41 4D CT data sets are acquired, while the breathing cycle is simultaneously measured using spirometry and an abdominal pressure bellows system. The assumptions underlying the conversion of the bellows measurement to tidal volume by CT-based air content are analyzed. This comprises of detailed correlation studies of the spirometry-measured tidal volume, the bellows signal, and CT-based air content. RESULTS For 15/41 patients, the spirometry signals are not consistently acquired during the 4D CT session, so correlating spirometry to bellows measurements and CT-based air content leads to erroneous conversion coefficients. After introducing a minimum correlation threshold to remove these data, good correlations are obtained between the remaining breathing signals. The ratio of CT-based air content to tidal volume is measured to be 1.11 +/- 0.08; the expected value is 1.11 because room air is 11% more dense than air in the lungs. CONCLUSIONS The observed problems of spirometry recording illustrate the challenges encountered when using spirometers as breathing surrogate for 4D CT acquisition. The high correlation between spirometry and bellows breathing signals and the verified factor of 1.11 between CT-based air content and tidal volume mean that the bellows measurement (or other equivalent surrogates) can be reliably converted to tidal volume using the CT-based air content, avoiding the need for a spirometer.

Modeling respiratory lung motion: a biophysical approach using finite element methods

Respiratory dynamics poses a main source of error in radiotherapy of thoracic tumors. Development and optimization of methods to adequately account for breathing motion require detailed knowledge of the dynamics and its impact on e. g. the dose delivered by radiation. Thus, computer aided modeling and model based simulation of respiratory motion gains in importance. In this paper a biophysical approach for modeling lung motion is described. Main aspects of the process of lung ventilation are identified and outlined as the starting point of modeling. They are formulated as a contact problem of linear elasticity theory. The resulting boundary value problem is solved using Finite Element Methods (FEM). 4D (= 3D+t) CT image data are used to evaluate the modeling approach. Model based three-dimensional vector fields representing respiratory motion are computed for different patients. Simulated motion patterns of inner lung landmarks like prominent bifurcations of the bronchial tree and the tumor mass center are compared with corresponding motion patterns observed in the 4D CT data. The influence of geometrical and biomechanical parameters like mesh quality and values of elasticity constants on the modeling process is investigated. Differences between model based predicted landmark positions and corresponding landmark positions identified interactively are mostly within the variability of interactive landmark positioning across multiple observers (interobserver variability). The impact of geometrical and biomechanical parameters on resulting vector fields is fairly small. Outcomes suggest that FEM state an adequate strategy to model aspects of the physiology of breathing.

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.