Alexander Schmidt-Richberg

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).

Integrated segmentation and non-linear registration for organ segmentation and motion field estimation in 4D CT data.

OBJECTIVESnThe development of spatiotemporal tomographic imaging techniques allows the application of novel techniques for diagnosis and therapy in the medical routine. However, in consequence to the increasing amount of image data automatic methods for segmentation and motion estimation are required. In adaptive radiation therapy, registration techniques are used for the estimation of respiration-induced motion of pre-segmented organs. In this paper, a variational approach for the simultaneous computation of segmentations and a dense non-linear registration of the 3D images of the sequence is presented.nnnMETHODSnIn the presented approach, a variational region-based level set segmentation of the structures of interest is combined with a diffusive registration of the spatial images of the sequence. We integrate both parts by defining a new energy term, which allows us to incorporate mutual prior information in order to improve the segmentation as well as the registration quality.nnnRESULTSnThe presented approach was utilized for the segmentation of the liver and the simultaneous estimation of its respiration-induced motion based on four-dimensional thoracic CT images. For the considered patients, we were able to improve the results of the segmentation and the motion estimation, compared to the conventional uncoupled methods.nnnCONCLUSIONSnApplied in the field of radiation therapy of thoracic tumors, the presented integrated approach turns out to be useful for simultaneous segmentation and registration by improving the results compared to the application of the methods independently.

Generation of a mean motion model of the lung using 4D-CT image data

Modeling of respiratory motion gains in importance within the field of radiation therapy of lung cancer patients. 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 CT data of different patients to extend motion modeling capabilities. Our modeling process consists of two main parts: an intra - subject registration to generate subject - specific motion models and an inter - subject registration to combine these subject - specific motion models into a mean motion model. Further, we present methods to adapt the mean motion model to a patient-specific lung geometry. n nA first evaluation of the model was done by using the generated mean motion model to predict lung and tumor motion of individual patients and comparing the prediction quality to non - linear registration. Our results show that the average difference in prediction quality (measured by overlap coefficients) between non - linear registration and model - based prediction is approx. 10%. However, the patient - specific registration relies on individual 4D image data, whereas the model - based prediction was obtained without knowledge of the individual breathing dynamics. Results show that the model predicts motion patterns of individual patients generally well and we conclude from our results that such a model has the capability to provide valuable a-priori knowledge in many fields of applications.

Validation and comparison of a biophysical modeling approach and non-linear registration for estimation of lung motion fields in thoracic 4D CT data

Spatiotemporal image data allow analyzing respiratory dynamics and its impact on radiation therapy. A key feature within this field of research is the process of lung motion field estimation. For a multitude of applications feasible and realistic motion field estimates are required. Widely non-linear registration methods are applied to estimate motion fields; in this case physiology is not taken into account. Using Finite Element Methods we implemented a biophysical approach to model respiratory lung motion starting with the physiology of breathing. Resulting motion models are compared to motion field estimates of a non-linear non-parametric intensity-based registration approach. Additionally, we extended the registration approach to cope with discontinuities in pleura and chest wall motion as motivated by the biophysical model. Accuracy of the different modeling approaches is evaluated using a total of 800 user-defined landmarks in 4D(=3D+t) CT data of 10 lung tumor patients (between 70 and 90 landmarks each patient). Mean registration residuals (= difference between landmark motion as predicted model-based and as observed by an expert) are 3.2±2.0 mm (biophysical model), 3.4±2.4 mm (registration of segmented lung data), 2.1±2.3 mm (registration of CT data), and 1.6±1.3 mm (extended registration of CT data); intraobserver variability of landmark identification is 0.9±0.8 mm, mean landmark motion is 6.8±5.4 mm. Thus, prediction accuracy is higher for non-linear registration of the CT data, but it is shown that explicit modeling of boundary conditions motivated by the physiology of breathing and the biophysical modeling approach, respectively, improves registration accuracy significantly.

Estimation of motion fields by non-linear registration for local lung motion analysis in 4D CT image data

PurposeMotivated by radiotherapy of lung cancer non- linear registration is applied to estimate 3D motion fields for local lung motion analysis in thoracic 4D CT images. Reliability of analysis results depends on the registration accuracy. Therefore, our study consists of two parts: optimization and evaluation of a non-linear registration scheme for motion field estimation, followed by a registration-based analysis of lung motion patterns.MethodsThe study is based on 4D CT data of 17 patients. Different distance measures and force terms for thoracic CT registration are implemented and compared: sum of squared differences versus a force term related to Thirion’s demons registration; masked versus unmasked force computation. The most accurate approach is applied to local lung motion analysis.ResultsMasked Thirion forces outperform the other force terms. The mean target registration error is 1.3 ± 0.2 mm, which is in the order of voxel size. Based on resulting motion fields and inter-patient normalization of inner lung coordinates and breathing depths a non-linear dependency between inner lung position and corresponding strength of motion is identified. The dependency is observed for all patients without or with only small tumors.ConclusionsQuantitative evaluation of the estimated motion fields indicates high spatial registration accuracy. It allows for reliable registration-based local lung motion analysis. The large amount of information encoded in the motion fields makes it possible to draw detailed conclusions, e.g., to identify the dependency of inner lung localization and motion. Our examinations illustrate the potential of registration-based motion analysis.

A Variational Approach for Combined Segmentation and Estimation of Respiratory Motion in Temporal Image Sequences

In this paper a variational approach for the combined segmentation and registration of temporal image sequences is presented. The purpose of the proposed method is to estimate respiratory-induced organ motion in temporal CT image sequences and to segment a structure of interest simultaneously. In this model the segmentation of all images in the sequences is obtained by finding a non-linear registration to an initial segmentation in a reference image. A dense non-linear displacement field is estimated using image intensities and segmentation information in the images. Both problems (registration and segmentation) are formulated in a joint variational approach and solved simultaneously. A validation of the combined registration and segmentation approach is presented and demonstrates that the simultaneous solution of both problems improves the segmentation performance over a sequential application of the registration and segmentation steps.

A Statistical Shape and Motion Model for the Prediction of Respiratory Lung Motion

We propose a method to compute a 4D statistical model of respiratory lung motion which consists of a 3D shape atlas, a 4D mean motion model and a 4D motion variability model. Symmetric diffeomorphic image registration is used to estimate subject-specific motion models, to generate an average shape and intensity atlas of the lung as anatomical reference frame and to establish inter-subject correspondence. The Log-Euclidean framework allows to perform statistics on diffeomorphic transformations via vectorial statistics on their logarithms. We apply this framework to compute the mean motion and motion variations by performing a Principal Component Analysis (PCA) on diffeomorphisms. Furthermore, we present methods to adapt the generated statistical 4D motion model to a patient-specific lung geometry and the individual organ motion. The prediction performance is evaluated with respect to motion field differences and with respect to landmark- based target registration errors. The quantitative analysis results in a mean target registration error of 3,2 ± 1,8 mm. The results show that the new method is able to provide valuable knowledge in many fields of application.

Landmark-driven Parameter Optimization for non-linear Image Registration

Image registration is one of the most common research areas in medical image processing. It is required for example for image fusion, motion estimation, patient positioning, or generation of medical atlases. In most intensity-based registration approaches, parameters have to be determined, most commonly a parameter indicating to which extend the transformation is required to be smooth. Its optimal value depends on multiple factors like the application and the occurrence of noise in the images, and may therefore vary from case to case. Moreover, multi-scale approaches are commonly applied on registration problems and demand for further adjustment of the parameters. In this paper, we present a landmark-based approach for automatic parameter optimization in non-linear intensity-based image registration. In a first step, corresponding landmarks are automatically detected in the images to match. The landmark-based target registration error (TRE), which is shown to be a valid metric for quantifying registration accuracy, is then used to optimize the parameter choice during the registration process. The approach is evaluated for the registration of lungs based on 22 thoracic 4D CT data sets. Experiments show that the TRE can be reduced on average by 0.07 mm using automatic parameter optimization.

Simultaneous Segmentation and Motion Estimation in 4D-CT Data Using a Variational Approach

Spatiotemporal image data sets, like 4D CT or dynamic MRI, open up the possibility to estimate respiratory induced tumor and organ motion and to generate four-dimensional models that describe the temporal change in position and shape of structures of interest. However, two main problems arise: the structures of interest have to be segmented in the 4D data set and and the organ motion has to be estimated in the temporal image sequence. This paper presents a variational approach for simultaneous segmentation and registration applied to temporal image sequences. The proposed method assumes a known segmentation in one frame and then recovers nonlinear registration and segmentation in other frames by minimizing a cost function that combines intensity-based registration, level-set segmentation as well as prior shape and intensity knowledge. The purpose of the presented method is to estimate respiration induced organ motion in spatiotemporal CT image sequences and to segment a structure of interest simultaneously. A validation of the combined registration and segmentation approach is presented using low dose 4D CT data sets of the liver. The results demonstrate that the simultaneous solution of both problems improves the segmentation performance over a sequential application of the registration and segmentation steps.

Direction-dependent regularization for improved estimation of liver and lung motion in 4D image data

The estimation of respiratory motion is a fundamental requisite for many applications in the field of 4D medical imaging, for example for radiotherapy of thoracic and abdominal tumors. It is usually done using non-linear registration of time frames of the sequence without further modelling of physiological motion properties. In this context, the accurate calculation of liver und lung motion is especially challenging because the organs are slipping along the surrounding tissue (i.e. the rib cage) during the respiratory cycle, which leads to discontinuities in the motion field. Without incorporating this specific physiological characteristic, common smoothing mechanisms cause an incorrect estimation along the object borders. In this paper, we present an extended 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 preventing gaps and ensuring smooth motion fields inside. We evaluate our model for the estimation of lung and liver motion on the basis of publicly accessible 4D CT and 4D MRI data. The results show a considerable increase of registration accuracy with respect to the target registration error and a more plausible motion estimation.