Showing papers by "Heinz-Otto Peitgen published in 2008"

Interactive visualization of multimodal volume data for neurosurgical tumor treatment

Abstract: We present novel interactive methods for the visualization of multimodal volume data as used in neurosurgical therapy planning. These methods allow surgeons to explore multimodal volumes and focus on functional data and lesions. Computer graphics techniques are proposed to create expressive visualizations at interactive frame rates to reduce time-consuming and complex interaction with the medical data. Contributions of our work are the distance-based enhancements of functional data and lesions which allows the surgeon to perceive functional and anatomical structures at once and relate them directly to the intervention. In addition we propose methods for the visual exploration of the path to the structures of interest, to enhance anatomical landmarks, and to provide additional depth indicators. These techniques have been integrated in a visualization prototype that provides interaction capabilities for finding the optimal therapeutic strategy for the neurosurgeon.

Implicit vessel surface reconstruction for visualization and CFD simulation

Abstract: Objective: Accurate and high-quality reconstructions of vascular structures are essential for vascular disease diagnosis and blood flow simulations.These applications necessitate a trade-off between accuracy and smoothness. An additional requirement for the volume grid generation for Computational Fluid Dynamics (CFD) simulations is a high triangle quality. We propose a method that produces an accurate reconstruction of the vessel surface with satisfactory surface quality.

Robust vessel segmentation

Abstract: In the context of cardiac applications, the primary goal of coronary vessel analysis often consists in supporting the diagnosis of vessel wall anomalies, such as coronary plaque and stenosis. Therefore, a fast and robust segmentation of the coronary tree is a very important but challenging task. We propose a new approach for coronary artery segmentation. Our method is based on an earlier proposed progressive region growing. A new growth front monitoring technique controls the segmentation and corrects local leakage by retrospective detection and removal of leakage artifacts. While progressively reducing the region growing threshold for the whole image, the growing process is locally analyzed using criteria based on the assumption of tubular, gradually narrowing vessels. If a voxel volume limit or a certain shape constraint is exceeded, the growing process is interrupted. Voxels affected by a failed segmentation are detected and deleted from the result. To avoid further processing at these positions, a large neighborhood is blocked for growing. Compared to a global region growing without local correction, our new local growth control and the adapted correction can deal with contrast decrease even in very small coronary arteries. Furthermore, our algorithm can efficiently handle noise artifacts and partial volume effects near the myocardium. The enhanced segmentation of more distal vessel parts was tested on 150 CT datasets. Furthermore, a comparison between the pure progressive region growing and our new approach was conducted.

Clinical relevance of model based computer-assisted diagnosis and therapy

Abstract: The ability to acquire and store radiological images digitally has made this data available to mathematical and scientific methods With the step from subjective interpretation to reproducible measurements and knowledge, it is also possible to develop and apply models that give additional information which is not directly visible in the data In this context, it is important to know the characteristics and limitations of each model Four characteristics assure the clinical relevance of models for computer-assisted diagnosis and therapy: ability of patient individual adaptation, treatment of errors and uncertainty, dynamic behavior, and in-depth evaluation We demonstrate the development and clinical application of a model in the context of liver surgery Here, a model for intrahepatic vascular structures is combined with individual, but in the degree of vascular details limited anatomical information from radiological images As a result, the model allows for a dedicated risk analysis and preoperative planning of oncologic resections as well as for living donor liver transplantations The clinical relevance of the method was approved in several evaluation studies of our medical partners and more than 2900 complex surgical cases have been analyzed since 2002

Intraoperative adaptation and visualization of preoperative risk analyses for oncologic liver surgery

Abstract: Tumor resections from the liver are complex surgical interventions. With recent planning software, risk analyses based on individual liver anatomy can be carried out preoperatively. However, additional tumors within the liver are frequently detected during oncological interventions using intraoperative ultrasound. These tumors are not visible in preoperative data and their existence may require changes to the resection strategy. We propose a novel method that allows an intraoperative risk analysis adaptation by merging newly detected tumors with a preoperative risk analysis. To determine the exact positions and sizes of these tumors we make use of a navigated ultrasound-system. A fast communication protocol enables our application to exchange crucial data with this navigation system during an intervention. A further motivation for our work is to improve the visual presentation of a moving ultrasound plane within a complex 3D planning model including vascular systems, tumors, and organ surfaces. In case the ultrasound plane is located inside the liver, occlusion of the ultrasound plane by the planning model is an inevitable problem for the applied visualization technique. Our system allows the surgeon to focus on the ultrasound image while perceiving context-relevant planning information. To improve orientation ability and distance perception, we include additional depth cues by applying new illustrative visualization algorithms. Preliminary evaluations confirm that in case of intraoperatively detected tumors a risk analysis adaptation is beneficial for precise liver surgery. Our new GPU-based visualization approach provides the surgeon with a simultaneous visualization of planning models and navigated 2D ultrasound data while minimizing occlusion problems.

Intraoperative modification of resection plans for liver surgery

Abstract: Objective Recent surgical planning software provides valuable tools for evaluating different resection strategies preoperatively. With such virtual resections, predictions and quantitative analyses may be carried out to assess the resection feasibility with respect to tumors and risk structures. In oncologic liver surgery, additional tumors that were not seen in the preoperative images are often found during the intervention using intraoperative ultrasound (IOUS). Due to such findings, the resection strategy must be updated or completely revised.

Hierarchical matching of anatomical trees for medical image registration

Abstract: Today, tomographic images are used in medical applications more and more. To support physicians in diagnosis and treatment, a registration of two images taken at different points in time or under different conditions is needed. As the structure of the vessel or airway trees is relatively stable between two image acquisitions, they provide a good basis for the automatic determination of landmarks. In this work, a hierarchical tree search algorithm is proposed, which efficiently computes a matching between branchpoints of anatomical trees, which can be used as landmarks for an elastic registration. The algorithm is designed to be general and robust in order to be applicable to a variety of different datasets, which are acquired by different sensors or under different conditions. The validation of the algorithm against manually created ground truth data leads to a 80.9% rate of correctly matched branchpoints. Allowing a tolerance of 5 mm, the rate increases to 89.9%. The runtime for 50-700 vertices is about 1-45 seconds.

Reducing Motion Artifacts in 3-D Breast Ultrasound Using Non-linear Registration

Abstract: Automated full-field 3-D breast ultrasound (3DBUS) has a high potential as a reproducible method for screening and intervention. Consecutive linear transducer scans yield a consistent breast ultrasound volume, yet individual slices are prone to tissue deformation and motion. To compensate resulting image distortions, we propose an efficient non-rigid registration method applied sequentially to pairs of 3DBUS volume slices, optionally either on-line or in post-processing. A quantitative evaluation of the method on synthetic deformations shows subvoxel registration accuracy. First application to clinical breast US images and preliminary results confirmed effectiveness and accuracy of the method.

High-quality multimodal volume visualization of intracerebral pathological tissue

Abstract: Parallel visualization of multiple MRI sequences in 2D is a standard method for exploration of pathological structures for neurosurgery planning In this work our aim is to support visualization techniques that allow medical experts a fast and comprehensive combined exploration of anatomical structures with inhomogeneous pathological tissue in the three-dimensional volume rendering The prototypical software solution presented in this paper addresses the issue that a high amount of interaction is commonly needed to merge different MRI sequences and that the resulting visualization does not allow to recognize anatomical details of the brain and pathological tissue at the same time without loss of information We also present novel clipping methods for neurological volume exploration and emphasize important structures as well as suspicious high intensity signals from multiple sequences in the volume rendering We demonstrate that our methods facilitate comprehensive volume visualization for neurosurgery

Contrast Sensitivity in Mammographic Softcopy Reading --- Determination with Psychophysical Procedures

Abstract: We introduce a new approach for determining the contrast sensitivity function (CSF) in front of the anatomical noise of mammograms. This permits fundamental investigations on the influence of viewing conditions and image presentation parameters on the perception of contrast. A Gabor pattern and digits are used as target items. The approximation to contrast thresholds is done by a psychophysical staircase procedure and is performed for a selection of spatial frequencies. The results of an observer study with different mammographic cases show, that the approach is applicable independent of the chosen images. For conditions with a duration of at least 3 to 4 minutes the CSF can be determined for a set of spatial frequencies.

Towards user-independent DTI quantification

Abstract: Quantification of diffusion tensor imaging (DTI) parameters has become an important role in the neuroimaging, neurosurgical, and neurological community as a method to identify major white matter tracts afflicted by pathology or tracts at risk for a given surgical approach. We introduce a novel framework for a reliable and robust quantification of DTI parameters, which overcomes problems of existing techniques introduced by necessary user inputs. In a first step, a hybrid clustering method is proposed that allows for extracting specific fiber bundles in a robust way. Compared to previous methods, our approach considers only local proximities of fibers and is insensitive to their global geometry. This is very useful in cases where a fiber tracking of the whole brain is not available. Our technique determines the overall number of clusters iteratively using a eigenvalue thresholding technique to detect disjoint clusters of independent fiber bundles. Afterwards, possible finer substructures based on an eigenvalue regression are determined within each bundle. In a second step, a quantification of DTI parameters of the extracted bundle is performed. We propose a method that automatically determines a 3D image where the voxel values encode the minimum distance to a reconstructed fiber. This image allows for calculating a 3D mask where each voxel within the mask corresponds to a voxel that lies in an isosurface around the fibers. The mask is used for an automatic classification between tissue classes (fiber, background, and partial volume) so that the quantification can be performed on one or more of such classes. This can be done per slice or a single DTI parameter can be determined for the whole volume which is covered by the isosurface. Our experimental tests confirm that major white matter fiber tracts may be robustly determined and can be quantified automatically. A great advantage of our framework is its easy integration into existing quantification applications so that uncertainties can be reduced, and higher intrarater- as well as interrater reliabilities can be achieved.

A novel software assistant for the clinical analysis of MR spectroscopy with MeVisLab

Abstract: We present a novel software assistant for the analysis of multi-voxel 2D or 3D in-vivo-spectroscopy signals based on the rapid-prototyping platform MeVisLab. Magnetic Resonance Spectroscopy (MRS) is a valuable in-vivo metabolic window into tissue regions of interest, such as the brain, breast or prostate. With this method, the metabolic state can be investigated non-invasively. Different pathologies evoke characteristically different MRS signals, e.g., in prostate cancer, choline levels increase while citrate levels decrease compared to benign tissue. Concerning the majority of processing steps, available MRS tools lack performance in terms of speed. Our goal is to support clinicians in a fast and robust interpretation of MRS signals and to enable them to interactively work with large volumetric data sets. These data sets consist of 3D spatially resolved measurements of metabolite signals. The software assistant provides standard analysis methods for MRS data including data import and filtering, spatio-temporal Fourier transformation, and basic calculation of peak areas and spectroscopic metabolic maps. Visualization relies on the facilities of MeVisLab, a platform for developing clinically applicable software assistants. It is augmented by special-purpose viewing extensions and offers synchronized 1D, 2D, and 3D views of spectra and metabolic maps. A novelty in MRS processing tools is the side-by-side viewing ability of standard FT processed spectra with the results of time-domain frequency analysis algorithms like Linear Prediction and the Matrix Pencil Method. This enables research into the optimal toolset and workflow required to avoid misinterpretation and misapplication.

Inverse Problems and Parameter Identification in Image Processing

Abstract: Many problems in imaging are actually inverse problems. One reason for this is that conditions and parameters of the physical processes underlying the actual image acquisition are usually not known. Examples for this are the inhomogeneities of the magnetic field in magnetic resonance imaging (MRI) leading to nonlinear deformations of the anatomic structures in the recorded images, material parameters in geological structures as unknown parameters for the simulation of seismic wave propagation with sparse measurement on the surface, or temporal changes in movie sequences given by intensity changes or moving image edges and resulting from deformation, growth and transport processes with unknown fluxes. The underlying physics is mathematically described in terms of variational problems or evolution processes. Hence, solutions of the forward problem are naturally described by partial differential equations. These forward models are reflected by the corresponding inverse problems as well. Beyond these concrete, direct modeling links to continuum mechanics abstract concepts from physical modeling are successfully picked up to solve general perceptual problems in imaging. Examples are visually intuitive methods to blend between images showing multiscale structures at different resolution or methods for the analysis of flow fields.

Determination of Contrast Sensitivity for Mammographic Softcopy Reading

Abstract: Research of visual perception of luminance differences is an important basis for understanding the perception of complex patterns. We introduce a new approach for determining the contrast sensitivity function (CSF) in front of the complex anatomical structures of mammograms. For this purpose a sinusoidal pattern and digits are used as target items. The approximation to contrast thresholds is done by a psychophysical staircase procedure and is performed for a selection of spatial frequencies.

Einfache Grauwert-Transferfunktion für die Berechnung von digital rekonstruierten Röntgenbildern

Abstract: Das Raycasting ist ein etabliertes Verfahren zur Berechnung von Digital Rekonstruierten Rontgenbildern (DRRs), also auf Basis von CT-Daten approximierten Radiographien, welche die Projektion eines Summationsbilds des 3D-CT-Volumens auf eine zweidimensionale Bildebene darstellen. Die Wirkung eines von einem Raycasting-Strahl durchquerten Voxels auf das Ergebnispixel im DRR kann unter anderem mit Hilfe von Transferfunktionen berechnet werden, die den Grauwert des Voxels in einen Schwachungskoeffizienten fur den Strahl umrechnen. Es wird ein einfaches mathematisches Modell fur eine solche Transferfunktion vorgestellt, dessen Parametrisierung eng an die klinische Praxis angelehnt ist.

Schnelles Voxel-Resampling für DRR-Raycasting-Verfahren in der 2D/3D-Registrierung

Abstract: Das Raycasting ist ein etabliertes Verfahren zur Berechnung von Digital Rekonstruierten Rontgenbildern (DRRs), auf Basis von CT-Daten approximierten Radiographien, die vorwiegend fur die 2D/3D Registrierung und die Strahlentherapieplanung eingesetzt werden. Das Strahlresampling ist der mit Abstand langwierigste Schritt des Raycastings. Es wird ein Algorithmus vorgestellt, der diesen Vorgang auf das absolute Minimum reduziert, aber trotzdem die Berechnung von DRRs ermoglicht, die qualitativ fur die meisten Anwendungen in der 2D/3D-Registrierung ausreichen.