Stephan Zidowitz

Interactive determination of robust safety margins for oncologic liver surgery

ObjectiveComplex oncologic interventions in the liver require an extensive and careful preoperative analysis. Particularly the achievement of an optimal safety margin around tumors remains a difficult task for surgeons.MethodsWe present new methods for evaluating different safety margins and their effect on the associated interruption of vascular supply or drainage. The characteristic of vascular risk distributions can be evaluated in real-time by exploiting precomputed safety maps that provide a volume curve for each vascular system. By applying fast visualization methods in 3D it is possible to assist the surgeon in the determination of a tumor-free safety margin while preserving sufficient vital hepatic parenchyma. The combination of risk analysis from different vascular systems and their sensitivity is considered.ResultsWe provide physicians with a novel computer-aided planning tool that allows for interactive determination of safety margins in real-time. The planning tool integrates smoothly into the preoperative workflow. Preliminary evaluations confirm that the width of safety margins can be determined more precisely, which may affect the proposed resection strategy.ConclusionOur new methods provide interactive feedback and support for decision making during the preoperative planning stage and thus might potentially improve the outcome of surgical interventions.

Matching of anatomical tree structures for registration of medical images

Many medical applications require a registration of different images of the same organ. In many cases, such a registration is accomplished by manual placement of landmarks in the images. In this paper, we propose a method which is able to find reasonable landmarks automatically. To achieve this, bifurcations of the vessel systems, which have been extracted from the images by a segmentation algorithm, are assigned by the so-called association graph method and the coordinates of these matched bifurcations can be used as landmarks for a non-rigid registration algorithm. Several constraints to be used in combination with the association graph method are proposed and evaluated on a ground truth consisting of anatomical trees from liver and lung. Furthermore, a method for preprocessing (tree pruning) as well as for postprocessing (clique augmentation) are proposed and evaluated on this ground truth. The proposed method achieves promising results for anatomical trees of liver and lung and for medical images obtained with different modalities and at different points in time.

Visual support for interactive post-interventional assessment of radiofrequency ablation therapy

Percutaneous radiofrequency (RF) ablation is a minimally invasive, image‐guided therapy for the treatment of liver tumors. The assessment of the ablation area (coagulation) is performed to verify the treatment success as an essential part of the therapy. Traditionally, pre‐ and post‐interventional CT images are used to visually compare the shape, size, and position of tumor and coagulation.

Visualization of risk structures for interactive planning of image guided radiofrequency ablation of liver tumors

Image guided radiofrequency ablation (RFA) is becoming a standard procedure as a minimally invasive method for tumor treatment in the clinical routine. The visualization of pathological tissue and potential risk structures like vessels or important organs gives essential support in image guided pre-interventional RFA planning. In this work our aim is to present novel visualization techniques for interactive RFA planning to support the physician with spatial information of pathological structures as well as the finding of trajectories without harming vitally important tissue. Furthermore, we illustrate three-dimensional applicator models of different manufactures combined with corresponding ablation areas in homogenous tissue, as specified by the manufacturers, to enhance the estimated amount of cell destruction caused by ablation. The visualization techniques are embedded in a workflow oriented application, designed for the use in the clinical routine. To allow a high-quality volume rendering we integrated a visualization method using the fuzzy c-means algorithm. This method automatically defines a transfer function for volume visualization of vessels without the need of a segmentation mask. However, insufficient visualization results of the displayed vessels caused by low data quality can be improved using local vessel segmentation in the vicinity of the lesion. We also provide an interactive segmentation technique of liver tumors for the volumetric measurement and for the visualization of pathological tissue combined with anatomical structures. In order to support coagulation estimation with respect to the heat-sink effect of the cooling blood flow which decreases thermal ablation, a numerical simulation of the heat distribution is provided.

Clinical relevance of model based computer-assisted diagnosis and therapy

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

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.

Workflow oriented software support for image guided radiofrequency ablation of focal liver malignancies

Image guided radiofrequency (RF) ablation has taken a significant part in the clinical routine as a minimally invasive method for the treatment of focal liver malignancies. Medical imaging is used in all parts of the clinical workflow of an RF ablation, incorporating treatment planning, interventional targeting and result assessment. This paper describes a software application, which has been designed to support the RF ablation workflow under consideration of the requirements of clinical routine, such as easy user interaction and a high degree of robust and fast automatic procedures, in order to keep the physician from spending too much time at the computer. The application therefore provides a collection of specialized image processing and visualization methods for treatment planning and result assessment. The algorithms are adapted to CT as well as to MR imaging. The planning support contains semi-automatic methods for the segmentation of liver tumors and the surrounding vascular system as well as an interactive virtual positioning of RF applicators and a concluding numerical estimation of the achievable heat distribution. The assessment of the ablation result is supported by the segmentation of the coagulative necrosis and an interactive registration of pre- and post-interventional image data for the comparison of tumor and necrosis segmentation masks. An automatic quantification of surface distances is performed to verify the embedding of the tumor area into the thermal lesion area. The visualization methods support representations in the commonly used orthogonal 2D view as well as in 3D scenes.

Matching of tree structures for registration of medical images

Many medical applications require a registration of different images of the same organ. In many cases, such a registration is accomplished by manually placing landmarks in the images. In this paper we propose a method which is able to find reasonable landmarks automatically. To achieve this, nodes of the vessel systems, which have been extracted from the images by a segmentation algorithm, will be assigned by the so-called association graph method and the coordinates of these matched nodes can be used as landmarks for a non-rigid registration algorithm.

Risk maps for liver surgery

AbstractPurpose Optimal display of surgical planning data in the operating room is challenging. In liver surgery, an expressive and effective intraoperative visualization of 3D planning models is still a pressing need. The objective of this work is to visualize surgical planning information using a map display. Methods An approach for risk analysis and visualization of planning models is presented which provides relevant information at a glance without the need for user interaction. Therefore, we present methods for the identification and classification of critical anatomical structures in the proximity of a preoperatively planned resection surface. Shadow-like distance indicators are introduced to encode the distance from the resection surface to these critical structures on a risk map. The work is demonstrated with examples in liver resection surgery and evaluated within two user studies. Results The results of the performed user studies show that the proposed visualization techniques facilitate the process of risk assessment in liver resection surgery and might be a valuable extension to surgical navigations system. Conclusion The approach provides a new and objective basis for the assessment of risks during liver surgery and has the potential to improve the outcome of surgical interventions.

Risk maps for navigation in liver surgery

The optimal transfer of preoperative planning data and risk evaluations to the operative site is challenging. A common practice is to use preoperative 3D planning models as a printout or as a presentation on a display. One important aspect is that these models were not developed to provide information in complex workspaces like the operating room. Our aim is to reduce the visual complexity of 3D planning models by mapping surgically relevant information onto a risk map. Therefore, we present methods for the identification and classification of critical anatomical structures in the proximity of a preoperatively planned resection surface. Shadow-like distance indicators are introduced to encode the distance from the resection surface to these critical structures on the risk map. In addition, contour lines are used to accentuate shape and spatial depth. The resulting visualization is clear and intuitive, allowing for a fast mental mapping of the current resection surface to the risk map. Preliminary evaluations by liver surgeons indicate that damage to risk structures may be prevented and patient safety may be enhanced using the proposed methods.