Showing papers by "Heinz-Otto Peitgen published in 2010"

Illustrative visualization of 3D planning models for augmented reality in liver surgery.

Abstract: Purpose Augmented reality (AR) obtains increasing acceptance in the operating room. However, a meaningful augmentation of the surgical view with a 3D visualization of planning data which allows reliable comparisons of distances and spatial relations is still an open request.

Assessment of Intraoperative Liver Deformation During Hepatic Resection: Prospective Clinical Study

Abstract: The implementation of intraoperative navigation in liver surgery is handicapped by intraoperative organ shift, tissue deformation, the absence of external landmarks, and anatomical differences in the vascular tree. To investigate the impact of surgical manipulation on the liver surface and intrahepatic structures, we conducted a prospective clinical trial. Eleven consecutive patients [4 female and 7 male, median age = 67 years (range = 54–80)] with malignant liver disease [colorectal metastasis (n = 9) and hepatocellular cancer (n = 2)] underwent hepatic resection. Pre- and intraoperatively, all patients were studied by CT-based 3D imaging and assessed for the potential value of computer-assisted planning. The degree of liver deformation was demonstrated by comparing pre- and intraoperative imaging. Intraoperative CT imaging was successful in all patients. We found significant deformation of the liver. The deformation of the segmental structures is reflected by the observed variation of the displacements. There is no rigid alignment of the pre- and intraoperative organ positions due to overall deflection of the liver. Locally, a rigid alignment of the anatomical structure can be achieved with less than 0.5 cm discrepancy relative to a segmental unit of the liver. Changes in total liver volume range from −13 to +24%, with an average absolute difference of 7%. These findings are fundamental for further development and optimization of intraoperative navigation in liver surgery. In particular, these data will play an important role in developing automation of intraoperative continuous registration. This automation compensates for liver shift during surgery and permits real-time 3D visualization of navigation imaging.

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

Abstract: 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. In this work, we present a novel visualization as well as a navigation tool, the so-called tumor map. The tumor map is a pseudo-cylindrical mapping of the tumor surface onto a 2D image. It is used for a combined visualization of all ablation zones of the tumor to allow a reliable therapy assessment. Additionally, the tumor map serves as an interactive tool for intuitive navigation within the 3D volume rendering of the tumor vicinity as well as with familiar 2D viewers.

Fast Automatic Path Proposal Computation for Hepatic Needle Placement

Abstract: Percutaneous image-guided interventions, such as radiofrequency ablation (RFA), biopsy, seed implantation, andseveral types of drainage, employ needle shaped instruments which have to be inserted into the patients body.Precise planning of needle placement is a key to a successful intervention. The planning of the access path hasto be carried out with respect to a variety of criteria for all possible trajectories to the selected target. Sincethe planning is performed in 2D slices, it demands considerable experience and constitutes a signi“cant mentaltask. To support the process of “nding a suitable path for hepatic interventions, we propose a fast automaticmethod that computes a list of path proposals for a given target point inside the liver with respect to multiplecriteria that aect safety and practicability. Prerequisites include segmentation masks of the liver, of all relevantrisk structures and, depending on the kind of procedure, of the tumor. The path proposals are computedbased on a weighted combination of cylindrical projections. Each projection represents one path criterion andis generated using the graphics hardware of the workstation. The list of path proposals is generated in lessthan one second. Hence, updates of the proposals upon changes of the target point and other relevant inputparameters can be carried out interactively. The results of a preliminary evaluation indicate that the proposedpaths are comparable to those chosen by experienced radiologists and therefore are suited to support planningin the clinical environment. Our implementation focuses on RFA and biopsy in the liver but may be adapted toother types of interventions.Keywords: minimally invasive intervention, radiofrequency ablation, therapy planning, access path planning

Automatic segmentation of lung lobes in ct images based on fissures, vessels, and bronchi

Abstract: Lobewise analysis of the pulmonary parenchyma is of clinical relevance for diagnosing and monitoring pathologies. In this work, a fully automatic lobe segmentation approach is presented, which is based on a previously proposed watershed transformation approach. The proposed extension explicitly considers the pulmonary fissures by including them in the cost image for the watershed segmentation. The fissure structures are found through a tailored feature analysis of the Hessian matrix. The method is evaluated using 42 data sets, and a comparison with manual segmentations yields an average volumetric agreement of 96.8%. In comparison to the previously proposed approach, this method increases segmentation accuracy where the fissures are visible.

Reproducibility of airway wall thickness measurements

Abstract: Airway remodeling and accompanying changes in wall thickness are known to be a major symptom of chronic obstructive pulmonary disease (COPD), associated with reduced lung function in diseased individuals. Further investigation of this disease as well as monitoring of disease progression and treatment effect demand for accurate and reproducible assessment of airway wall thickness in CT datasets. With wall thicknesses in the sub-millimeter range, this task remains challenging even with today's high resolution CT datasets. To provide accurate measurements, taking partial volume effects into account is mandatory. The Full-Width-at-Half-Maximum (FWHM) method has been shown to be inappropriate for small airways1,2 and several improved algorithms for objective quantification of airway wall thickness have been proposed.1-8 In this paper, we describe an algorithm based on a closed form solution proposed by Weinheimer et al.7 We locally estimate the lung density parameter required for the closed form solution to account for possible variations of parenchyma density between different lung regions, inspiration states and contrast agent concentrations. The general accuracy of the algorithm is evaluated using basic tubular software and hardware phantoms. Furthermore, we present results on the reproducibility of the algorithm with respect to clinical CT scans, varying reconstruction kernels, and repeated acquisitions, which is crucial for longitudinal observations.

Probabilistic 4D blood flow mapping

Abstract: Blood flow and tissue velocity can be measured using phase-contrast MRI. In this work, the statistical properties of 4D phase-contrast images are derived, and a novel probabilistic blood flow mapping method based on sequential Monte Carlo sampling is presented. The resulting flow maps visualize and quantify the uncertainty in conventional flow visualization techniques such as streamlines and particle traces.

Risk maps for navigation in liver surgery

Abstract: 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.

Rapid image recognition of body parts scanned in computed tomography datasets.

Abstract: Aim Automatic CT dataset classification is important to efficiently create reliable database annotations, especially when large collections of scans must be analyzed.

Dreidimensionale Rekonstruktion von zentralen Lungentumoren basierend auf CT-Daten

Abstract: Als Standardverfahren zur praoperativen bildmorphologischen Diagnostik bei Patienten mit zentralen malignen Lungentumoren ist das axiale 2-D-Computertomogramm des Thorax akzeptiert und etabliert. Tumorausdehnung, Infiltration von zentralen Strukturen oder segmentaler Bezug sind entscheidende Parameter zur Abklarung der technischen Operabilitat und des Resektionsausmases. Durch computergestutzte Methoden der Segmentierung anatomischer Strukturen in CT-Daten (Fraunhofer MeVis, Bremen) ist erstmals eine selektive dreidimensionale Visualisierung moglich. Im Zeitraum zwischen 08/07 und 06/09 wurden 22 Patienten mit zentralem Lungentumor in der Thoraxchirurgischen Abteilung der Universitat Schleswig-Holstein, Campus Lubeck, behandelt. 15 Manner, 7 Frauen, mittleres Alter bei Diagnosestellung 60,2 Jahre (Spannweite 41–74 Jahre). Lediglich 4 von 22 Patienten waren Nichtraucher gewesen, die ubrigen 18 Patienten hatten eine zum Teil erhebliche Raucheranamnese. 20 Patienten hatten ein primares Bronchialkarzinom, je ein Patient hatte ein Lokalrezidiv nach Tumorlobektomie bzw. eine zentrale Lungenmetastase eines extrapulmonalen Primarius. In allen Fallen wurde praoperativ ein Dunnschicht-MSDCT (Multi-slice detector computer tomogramm) angefertigt. Die Daten wurden mittels computerassistierter Software dreidimensional rekonstruiert und visualisiert. Lungenfunktionstests, geschatzte postoperative Lungenrestfunktion, Bronchoskopiebefund, Allgemeinzustand des Patienten sowie die 3-D-rekonstruierten CT-Datensatze wurden zur individuellen praoperativen Risikoanalyse und Auswahl der Operationstaktik verwendet. Vierzehn der 22 Patienten wurden aufgrund der durchgefuhrten Risikoanalyse operiert, 7 Patienten wurden als funktionell inoperabel eingestuft, ein Patient war technisch inoperabel. Insgesamt wurden 5 Pneumonektomien, 4 Bi-/Lobektomien, 3 erweiterte Lobektomien und jeweils eine atypische Resektion und eine Manschettenresektion durchgefuhrt. 2 Patienten wurden im Stadium I operiert, 7 Patienten im Stadium II und 3 Patienten im Stadium IIIa. Der mediane stationare Aufenthalt betrug 8,5±33,0 Tage. Die Hospitalletalitat betrug bei 0%. Die 3-D-Visualisierung des Tumors sowie der anatomische Bezug zu Nachbarstrukturen waren in allen Fallen exakt moglich. Intraoperativ konnte die 3-D-Rekonstruktion zu 100% validiert werden. Die computerassistierte 3-D-Rekonstruktion eines Thorax-CT ist eine neuartige und vielversprechende Methode zur praoperativen Darstellung und Risikoanalyse von zentralen Lungentumoren. Die 3-D-Visualisierung des Tumors in Kombination mit farbkodierter Lappenzugehorigkeit, beweglich in allen Ebenen, ermoglicht dem Chirurgen eine differenzierte Strategieplanung bei zentralen Lungentumoren.

Patient-Specific Planning for Radio-Frequency Ablation of Tumors in the Presence of Uncertainty

Abstract: Abstract The radio frequency (RF) ablation is a promising minimally invasive form of treatment for hepatic metastases and primary tumors. Thereby a needle like applicator, which is interstitially placed into the lesion, induces an electric current that causes heating and consequent destruction of the tissue due to its Ohmic resistance. In order to be a true alternative to the standard surgical resection, RF ablation must lead to a result similar to R0 resections. Here, patient specific mathematical modeling and numerical simulation of the bio-physical processes lead to a valuable support of the therapy planning, because they allow for an a priori estimation of the success as well as an optimization of the therapy parameters. In this article we discuss a mathematical model of partial differential equations (PDEs) for the patient specific numerical simulation of RF ablation. A particular focus lies on the consideration of uncertainties in the material properties, the underlying image data as well as the computational results. We discuss a stochastic PDE model, which allows for a sensitivity analysis of the computational results with respect to perturbations in the material properties. Furthermore a method for the fast estimation of the thermal necrosis is shown, which bases on the separation of non patient specific pre-calculations and patient specific computations, leading to an interactive real-time simulation tool. Zusammenfassung Die Hochfrequenzstrom Ablation (RF Ablation) ist eine vielversprechende minimalinvasive Therapie für Tumore und Metastasen in der Leber. Dabei induziert ein nadelförmiger Applikator, der perkutan in die Läsion eingeführt wird, einen lokalen Stromfluss der durch den Ohmschen Widerstand des Gewebes zu seiner Erwärmung und Zerstörung führt. Damit die RF Ablation einen ähnlichen klinischen Stellenwert einnehmen kann, wie die chirurgische Resektion, muss eine vollständige Ablation vergleichbar der R0 Resektion erreicht werden. Hier liefern die Patienten-individuelle Modellierung und numerische Simulation der bio-physikalischen Prozesse einen wertvollen Beitrag zur Therapieplanung, weil sie eine Abschätzung des Therapieerfolges und eine Optimierung der Therapieparameter ermöglichen. In diesem Beitrag wird ein mathematisches Modell mit partiellen Differentialgleichungen (PDEs) für die Patienten-individuelle Simulation der RF Ablation diskutiert. Ein Schwerpunkt liegt auf der Berücksichtigung von Unsicherheiten in den Materialeigenschaften, den zugrunde liegenden Bilddaten und den Ergebnissen der Simulationen. Ein stochastisches PDE Modell wird diskutiert, das eine Analyse der Sensitivität der Simulationsergebnisse unter Schwankungen in den Materialeigenschaften erlaubt. Schließlich wird eine Methode zur schnellen Abschätzung der Gewebezerstörung gezeigt, die auf einer Trennung von Patienten unspezifischen Vorberechnungen und Patienten individuellen Berechnungen beruht, und somit zu einer interaktiven Echtzeit-Simulation führt.

Towards a verified simulation model for radiofrequency ablations

Abstract: The simulation of radiofrequency ablations (RFA) can predict the achievable coagulation area and thus provide useful information for treatment planning, especially in cases in which the heat distribution can be limited by vascular cooling effects. A strong reliability of the numerical simulation results is essential for clinical use In this paper, we present a novel experimental procedure for the verification of RFA simulation systems in a lifelike environment without requiring animal tests. RF ablations are performed within isolated, perfused porcine livers, the corresponding configurations are reconstructed and simulated on a computer, and the resulting pathoanatomical coagulations are compared to their simulated counterparts with consideration of vascular cooling effects. We have applied this procedure for an initial verification of an existing RFA simulation system. The results are presented and discussed in this paper.

Fast and smooth interactive segmentation of medical images using variational interpolation

Abstract: We present a fast and interactive segmentation method for medical images that allows a smooth reconstruction of an object's surface from a set of user drawn, three-dimensional, planar contours that can be arbitrarily oriented Our algorithm uses an interpolation based on variational implicit functions Because variational interpolation is computationally expensive, we show how to speed up the algorithm to achieve an interactive calculation time while preserving the overall segmentation quality The performance improvements are based on a quality preserving reduction of the number of contour points and a fast voxelization strategy for the resulting implicit function A huge speedup is achieved by the parallelization of the algorithms, utilizing modern 64-bit multi-core CPUs Finally, we discuss how to make the interpolation algorithm more robust to selfintersecting and reduced contours

Evaluation of Resection Proposals for Liver Surgery Planning

Abstract: Modern software for surgery planning allows for definition of virtual resections within the liver. Thus, surgeons can simulate different resection strategies and assess the associated surgical risk preoperatively. Until now, it was impossible to measure the quality of different resection plans objectively. The choice for the optimal resection strategy was based on subjective judgment acquired by other examinations and subsequent risk analyses. We present a fast method for quality assessment of resection proposals with respect to surgical risk factors such as safety margin, remnant volume, remnant perfusion, surface curvature, and resection area. Our new method has been integrated into planning software used in the daily routine. The results from a preliminary user study confirm that the interactive quality feedback is beneficial for precise liver surgery planning.

Benoît B. Mandelbrot (1924–2010)

Abstract: Fractal geometry was created by Benoit B. Mandelbrot nearly 40 years ago, and with the 1982 publication of his seminal book, “The Fractal Geometry of Nature,” its application took off, opening our eyes to patterns in nature on all scales and across diverse disciplines. On 14 October, he died of cancer in Cambridge, Massachusetts. He fundamentally and irrevocably changed our view of the world and left us a tool that will continue to unveil nature's most peculiar commonalities that might otherwise be left aside as insignificant. Mandelbrot was born in 1924 in Warsaw, Poland. With the rise of Nazism, his family left for France in 1936, where he pursued an education in mathematics and earned a doctorate at the University of Paris in 1952. His career path took him to prominent establishments: the California Institute of Technology, the Institute for Advanced Study in Princeton, the Centre National de la Recherche Scientifique in Paris, IBM Research in New York, Harvard University, the Massachusetts Institute of Technology, and to Yale University (where he retired in 2005). Whereas the Renaissance saw geometry in the forefront, much of the 19th and 20th century sought an algebraic representation of nearly all mathematical fields. As a result, the patterns and forms that real nature presents were increasingly neglected. Geometry in the ordinary sense was left to school children, and even school mathematics departed from geometry in favor of an algebraic and abstract underpinning of curricula. Mandelbrot always felt this view was narrow and inappropriate for understanding nature. After decades of a mathematical trend to abandon visual representation of phenomena, which was spurred by French mathematicians (the so-called Bourbaki group) in the mid-1930s, Mandelbrot gave the eye a central role again. His whole career became one long and ardent pursuit of the concept of “roughness”—the roughness of clusters in the physics of disorder, of turbulent flows, of exotic noises, of chaotic dynamical systems, of distributions of galaxies, of coastlines, of stock-price charts, and of mathematical constructions. Some describe Mandelbrot as one who chose the role of a maverick in the mainstream sciences. Quite to the contrary, his uncompromising devotion to analyze and understand the “rough” reality of nature isolated him from the mainstream. In his view, the common “smooth” representations of natural processes were entirely inappropriate and far from the essence of nature: “Clouds are not spheres and mountains are not cones.” Alone, he shaped a program of geometry based on fractals, a term he coined to refer to mathematical shapes with irregular contours, just as seen in nature. The notion of self-similarity is key in fractal geometry: geometric shapes that break into parts, each a small-scale model of the whole. ![Figure][1] CREDIT: RAMA/WILKIPEDIA His mathematical sources were deeply rooted in the entire history of mathematics, notably the work of Felix Hausdorff and Paul Levy, and the “Mandelbrot set” that bears his name would likely not have been discovered without his peculiar contact with some forgotten jewels of mathematics produced at the turn of the 19th century by Gaston Julia and Pierre Fatou. He told me that his uncle, Szolem Mandelbrojt, had almost forced him to study their papers as the best introduction to good mathematics. In fact, his uncle, a traditional mathematician, student of Jacques Hadamard, and member of the College de France, sought to eradicate Benoit's preference for a geometric approach to mathematics. Fortunately, Mandelbrot's advocacy for geometry was without compromise. The mathematical genre of Julia and Fatou has experienced a great revival through Mandelbrot, and their topic—iteration—became a guiding principle for his own discovery and work. Whereas classical geometry and many of its modern algebraic and other extensions encode objects from closed elementary formulas to differential equations, Mandelbrot made us aware of a mathematical universe yet to be harvested—the world of iterative processes. Within this framework, he developed the tools that appear ideally suited for the rough nature of the world. And “world” is meant literally, because his footprints are left in the theory of finance, linguistics, biology, medicine, chemistry, physics, earth science, cosmology, computer science, astronomy, many of the engineering disciplines, and of course, mathematics. The Mandelbrot set provides perhaps the most striking example of a mathematical object whose properties would remain undiscovered without the guiding power of the human eye used by an able mathematician. For example, the key for understanding the myriads of patterns that sprout at the boundary of the Mandelbrot set is governed by a peculiar mathematical coding scheme within the field lines of its potential. Mandelbrot earned not only the credit for its discovery but also for expressing provocative mathematical conjectures about its properties. For instance, he proposed that the boundary of the Mandelbrot set, which exhibits all the marvelous and seemingly complex images that turned it into a cult object, has a fractal dimension of only two. Now that Mandelbrot's work can be considered to belong to mainstream mathematics and the sciences, it is important to remember that there was once strong resistance and skepticism. I have often asked myself where Mandelbrot found the source of his strength, determination, and endurance in those decades when he was practically isolated in his own mathematical world. He used to claim that his geometrical view and associated gifts guided him and that he did not feel isolated at all. I would add that his pristine character as someone who sought the truth in life and nature led him as well. Moreover, I remember Benoit as a universal scientist and very conscious citizen of the world, knowledgeable and sharp in all branches of the sciences and beyond: the arts, politics, and history. It will take further generations to grasp the full significance and impact of his insight far beyond the borders of mathematics. His personal history left him as someone who was fortunate to escape the darkest periods of mankind. He chose to remain forever suspicious toward any form of establishment and mainstream. [1]: pending:yes

Partial volume correction for volume estimation of liver metastases and lymph nodes in CT scans using spatial subdivision

Abstract: In oncological therapy monitoring, the estimation of tumor growth from consecutive CT scans is an important aspect in deciding whether the given treatment is adequate for the patient. This can be done by measuring and comparing the volume of a lesion in the scans based on a segmentation. However, simply counting the voxels within the segmentation mask can lead to significant differences in the volume, if the lesion has been segmented slightly differently by various readers or in different scans, due to the limited spatial resolution of CT and due to partial volume effects. We present a novel algorithm for measuring the volume of liver metastases and lymph nodes which considers partial volume effects at the surface of a lesion. Our algorithm is based on a spatial subdivision of the segmentation. We have evaluated the algorithm on a phantom and a multi-reader study. Our evaluations have shown that our algorithm allows determining the volume more accurately even for larger slice thicknesses. Moreover, it reduces inter-observer variability of volume measurements significantly. The calculation of the volume takes 2 seconds for 50 3 voxels on a single 2.66GHz Intel Core2 CPU.