Hans Barschdorf

Modeling atrial fiber orientation in patient-specific geometries: a semi-automatic rule-based approach

Atrial myofiber orientation is complex and has multiple discrete layers and bundles. A novel robust semi-automatic method to incorporate atrial anisotropy and heterogeneities into patient-specific models is introduced. The user needs to provide 22 distinct seed-points from which a network of auxiliary lines is constructed. These are used to define fiber orientation and myocardial bundles. The method was applied to 14 patient-specific volumetric models derived from CT, MRI and photographic data. Initial electrophysiological simulations show a significant influence of anisotropy and heterogeneity on the excitation pattern and P-wave duration (20.7% shortening). Fiber modeling results show good overall correspondence with anatomical data. Minor modeling errors are observed if more than four pulmonary veins exist in the model. The method is an important step towards creating realistic patient-specific atrial models for clinical applications.

Surface based cardiac and respiratory motion extraction for pulmonary structures from multi-phase CT

During medical imaging and therapeutic interventions, pulmonary structures are in general subject to cardiac and respiratory motion. This motion leads potentially to artefacts and blurring in the resulting image material and to uncertainties during interventions. This paper presents a new automatic approach for surface based motion tracking of pulmonary structures and reports on the results for cardiac and respiratory induced motion. The method applies an active shape approach to ad-hoc generated surface representations of the pulmonary structures for phase to phase surface tracking. Input of the method are multi-phase CT data, either cardiac or respiratory gated. The iso-surface representing the transition between air or lung parenchyma to soft tissue, is triangulated for a selected phase p0. An active shape procedure is initialised in the image of phase p1 using the generated surface in p0. The used internal energy term penalizes shape deformation as compared to p0. The process is iterated for all phases pi to pi+1 of the complete cycle. Since the mesh topology is the same for all phases, the vertices of the triangular mesh can be treated as pseudo-landmarks defining tissue trajectories. A dense motion field is interpolated. The motion field was especially designed to estimate the error margins for radiotherapy. In the case of respiratory motion extraction, a validation on ten biphasic thorax CT images (2.5mm slice distance) was performed with expert landmarks placed at vessel bifurcations. The mean error on landmark position was below 2.6mm. We further applied the method to ECG gated images and estimated the influence of the heart beat on lung tissue displacement.

Lung lobe modeling and segmentation with individualized surface meshes

An automated segmentation of lung lobes in thoracic CT images is of interest for various diagnostic purposes like the quantification of emphysema or the localization of tumors within the lung. Although the separating lung fissures are visible in modern multi-slice CT-scanners, their contrast in the CT-image often does not separate the lobes completely. This makes it impossible to build a reliable segmentation algorithm without additional information. Our approach uses general anatomical knowledge represented in a geometrical mesh model to construct a robust lobe segmentation, which even gives reasonable estimates of lobe volumes if fissures are not visible at all. The paper describes the generation of the lung model mesh including lobes by an average volume model, its adaptation to individual patient data using a special fissure feature image, and a performance evaluation over a test data set showing an average segmentation accuracy of 1 to 3 mm.

Comparing measured and simulated wave directions in the left atrium - a workflow for model personalization and validation.

Abstract Atrial arrhythmias are frequently treated using catheter ablation during electrophysiological (EP) studies. However, success rates are only moderate and could be improved with the help of personalized simulation models of the atria. In this work, we present a workflow to generate and validate personalized EP simulation models based on routine clinical computed tomography (CT) scans and intracardiac electrograms. From four patient data sets, we created anatomical models from angiographic CT data with an automatic segmentation algorithm. From clinical intracardiac catheter recordings, individual conduction velocities were calculated. In these subject-specific EP models, we simulated different pacing maneuvers and measurements with circular mapping catheters that were applied in the respective patients. This way, normal sinus rhythm and pacing from a coronary sinus catheter were simulated. Wave directions and conduction velocities were quantitatively analyzed in both clinical measurements and simulated data and were compared. On average, the overall difference of wave directions was 15° (8%), and the difference of conduction velocities was 16 cm/s (17%). The method is based on routine clinical measurements and is thus easy to integrate into clinical practice. In the long run, such personalized simulations could therefore assist treatment planning and increase success rates for atrial arrhythmias.

Automatic Segmentation of Cardiac CTs - Personalized Atrial Models Augmented with Electrophysiological Structures

Electrophysiological simulations of the atria could improve diagnosis and treatment of cardiac arrhythmia, like atrial fibrillation or flutter. For this purpose, a precise segmentation of both atria is needed. However, the atrial epicardium and the electrophysiological structures needed for electrophysiological simulations are barely or not at all detectable in CT-images. Therefore, a model based segmentation of only the atrial endocardium was developed as a landmark generator to facilitate the registration of a finite wall thickness model of the right and left atrial myocardium. It further incorporates atlas information about tissue structures relevant for simulation purposes like Bachmanns bundle, terminal crest, sinus node and the pectinate muscles. The correct model based segmentation of the atrial endocardium was achieved with a mean vertex to surface error of 0.53 mm for the left and 0.18 mm for the right atrium respectively. The atlas based myocardium segmentation yields physiologically correct results well suited for electrophysiological simulations.

Experimental feasibility study of energy-resolved fan-beam coherent scatter computed tomography

Energy-resolved fan beam coherent scatter computed tomography (CSCT) is a novel X-ray based imaging method revealing structural information on the molecular level of tissue or other material under investigation with high resolution of the momentum-transfer dependent coherent scatter cross-section. Since the molecular structure is the source of contrast a very good material discrimination and possibly also medical diagnosis of structural changes of tissue can be achieved with this technique. Poor spectral resolution as found in previous work due to the application of a polychromatic X-ray source can be overcome when energy-resolved detection is used. In this paper experimental results on phantoms using an energy-resolving CdTe-detector are shown. With the present setup the spatial resolution was found to be 4.5 mm (FWHM) and a spectral resolution of 6% was achieved. Applications of this technique can be found in medical imaging, material analysis and baggage inspection.

A hybrid method for automatic anatomical variant detection and segmentation

The delineation of anatomical structures in medical images can be achieved in an efficient and robust manner using statistical anatomical organ models, which has been demonstrated for an already considerable set of organs, including the heart. While it is possible to provide models with sufficient shape variability to cope, to a large extent, with inter-patient variability, as long as object topology is conserved, it is a fundamental problem to cope with topological organ variability. We address this by creating a set of model variants and selecting the most appropriate model variant for the patient at hand. We propose a hybrid method combining model-based image analysis with a guided region growing approach for automated anatomical variant selection and apply it to the left atrium in cardiac CT images. Concerning the human heart, the left atrium is the most variable sub-structure with a variable number of pulmonary veins drainng into it. It is of large clinical interest in the context of atrial fibrillation and related interventions.

Liquid-metal anode x-ray tube

A novel type of electron-impact x-ray source based on the interaction of energetic electrons with a turbulently flowing liquid metal target is presented. The electrons enter the liquid through a thin (several microns thick) window, separating the liquid from the vacuum region in which the cathode is situated. Several electron window materials including diamond, tungsten and molybdenum were tested in combination with the liquid metal GaInSn. Satisfactory agreement has been obtained between the predictions of thermal transport models and the measured dependence of the loadability on fluid velocity. The liquid metal technology appears to represent a significant improvement in continuous loadability relative to stationary anode x-ray tubes.

The generation of patient-specific heart models for diagnosis and interventions

A framework for the automatic extraction and generation of patient-specific organ models from different image modalities is presented. These models can be used to extract and represent diagnostic information about the heart and its function. Furthermore, the models can be used for treatment planning and an overlay of the models onto X-ray fluoroscopy images can support navigation when performing an intervention in the CathLab.

From image to personalized cardiac simulation: encoding anatomical structures into a model-based segmentation framework

Whole organ scale patient specific biophysical simulations contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmia. However, many individual steps are required to bridge the gap from an anatomical scan to a personalized biophysical model. In biophysical modeling, differential equations are solved on spatial domains represented by volumetric meshes of high resolution and in model-based segmentation, surface or volume meshes represent the patients geometry. We simplify the personalization process by representing the simulation mesh and additional relevant structures relative to the segmentation mesh. Using a surface correspondence preserving model-based segmentation algorithm, we facilitate the integration of anatomical information into biophysical models avoiding a complex processing pipeline. In a simulation study, we observe surface correspondence of up to 1.6 mm accuracy for the four heart chambers. We compare isotropic and anisotropic atrial excitation propagation in a personalized simulation.