Jan Ehrhardt

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.

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.

Atlas-based segmentation of bone structures to support the virtual planning of hip operations

Two 3-D digitised atlases of a female and a male pelvis were generated to support the virtual 3-D planning of hip operations. The anatomical atlases were designed to replace the interactive, time-consuming pre-processing steps for the virtual operation planning. Each atlas consists of a labelled reference CT data set and a set of anatomical point landmarks. The paper presents methods for the automatic transfer of these anatomical labels to an individual patient data set. The labelled patient data are used to generate 3-D models of the patients bone structures. Besides the anatomical labelling, the determination of measures, like angles, distances or sizes of contact areas, is important for the planning of hip operations. Thus, algorithms for the automatic computation of orthopaedic parameters were implemented. A first evaluation of the presented atlas-based segmentation method shows a correct labelling of 98.5% of the bony voxels.

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.

3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights

The aim of this work is to present and evaluate a level-set segmentation approach with vesselness-dependent anisotropic energy weights, which focuses on the exact segmentation of malformed as well as small vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) datasets. In a first step, a vesselness filter is used to calculate the vesselness dataset, which quantifies the likeliness of each voxel to belong to a bright tubular-shaped structure and estimate the corresponding vessel directions from a given TOF dataset. The vesselness and TOF datasets are then combined using fuzzy-logic and used for initialization of a variational level-set method. The proposed level-set model has been extended in a way that the weight of the internal energy is locally adapted based on the vessel direction information. Here, the main idea is to weight the internal energy lower if the gradient direction of the level-set is similar to the direction of the eigenvector extracted by the vesselness filter. Furthermore, an additional vesselness force has been integrated in the level-set formulation. The proposed method was evaluated based on ten TOF MRA datasets from patients with an arteriovenous malformation. Manual segmentations from two observers were available for each dataset and used for quantitative comparison. The evaluation revealed that the proposed method yields significantly better segmentation results than four other state-of-the-art segmentation methods tested. Furthermore, the segmentation results are within the range of the inter-observer variation. In conclusion, the proposed method allows an improved delineation of small vessels, especially of those represented by low intensities and high surface curvatures.

Motion Artifact Reducing Reconstruction of 4D CT Image Data for the Analysis of Respiratory Dynamics

OBJECTIVES Respiratory motion represents a major problem in radiotherapy of thoracic and abdominal tumors. Methods for compensation require comprehensive knowledge of underlying dynamics. Therefore, 4D (= 3D + t) CT data can be helpful. But modern CT scanners cannot scan a large region of interest simultaneously. So patients have to be scanned in segments. Commonly used approaches for reconstructing the data segments into 4D CT images cause motion artifacts. In order to reduce the artifacts, a new method for 4D CT reconstruction is presented. The resulting data sets are used to analyze respiratory motion. METHODS Spatiotemporal CT image sequences of lung cancer patients were acquired using a multi-slice CT in cine mode during free breathing. 4D CT reconstruction was done by optical flow based temporal interpolation. The resulting 4D image data were compared with data generated by the commonly used nearest neighbor reconstruction. Subsequent motion analysis is mainly concerned with tumor mobility. RESULTS The presented optical flow-based method enables the reconstruction of 3D CT images at arbitrarily chosen points of the patients breathing cycle. A considerable reduction of motion artifacts has been proven in eight patient data sets. Motion analysis showed that tumor mobility differs strongly between the patients. CONCLUSIONS Due to the proved reduction of motion artifacts, the optical flow-based 4D CT reconstruction offers the possibility of high-quality motion analysis. Because the method is based on an interpolation scheme, it additionally has the potential to enable the reconstruction of 4D CT data from a lesser number of scans.

4D Modeling and Estimation of Respiratory Motion for Radiation Therapy

4D Image Acquisition.- Motion Estimation and Modeling.- Modeling of Motion Variability.- Applications of Motion Estimation Algorithms.- Outlook.

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.

Virtual planning of hip operations and individual adaption of endoprostheses in orthopaedic surgery

The introduction of virtual reality techniques in medicine opens up new possibilities for the planning of interventions. The presented software system for virtual operation planning in orthopaedic surgery (VIRTOPS) enables the virtual preoperative 3D planning and simulation of pelvis and hip operations. It is used to plan operations of bone tumours with endoprosthetic reconstruction of the hip based on multimodal image information. The operation and the endosprothetic reconstruction of the pelvis are simulated using virtual reality techniques. Stereoscopic visualisation techniques and 3D input devices support the 3D interaction with the virtual 3D models. The main task of the preoperative planning process is the individual design of an anatomically adaptable modular prosthesis. The placement and the design of the endoprosthesis are supported by different functions and visualisation techniques. The resulting 3D images and movies can be used for the documentation of the operation planning procedure, as well as, for the preoperative information of the patient.

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.