Michael Teistler

virtX – Evaluation of a Computer-based Training System for Mobile C-arm Systems in Trauma and Orthopedic Surgery

OBJECTIVES Operating room personnel (ORP) operating mobile image intensifier systems (C-arms) need training to produce high quality radiographs with a minimum of time and X-ray exposure. Our study aims at evaluating acceptance, usability and learning effect of the CBT system virtX that simulates C-arm based X-ray imaging in the context of surgical case scenarios. METHODS Prospective, interventional study conducted during an ORP course with three groups: intervention group 1 (training on a PC using virtX), and 2 (virtX with a C-arm as input device), and a control group (training without virtX) - IV1, IV2 and CG. All participants finished training with the same exercise. Time needed to produce an image of sufficient quality was recorded and analyzed using One-Way-ANOVA and Dunnett post hoc test (alpha = .05). Acceptance and usability of virtX have been evaluated using a questionnaire. RESULTS CG members (n = 21) needed more time for the exercise than those of IV2 (n = 20): 133 +/- 55 vs. 101 +/- 37 sec. (p = .03). IV1 (n = 12) also performed better than CG (128 +/- 48 sec.), but this was not statistically significant. Seventy-nine participants returned a questionnaire (81% female, age 34 +/- 9 years, professional experience 8.3 +/- 7.6 years; 77% regularly used a C-arm). 83% considered virtX a useful addition to conventional C-arm training. 91% assessed virtual radiography as helpful for understanding C-arm operation. CONCLUSIONS Trainees experienced virtX as substantial enhancement of C-arm training. Training with virtX can reduce the time needed to perform an imaging task.

Virtual tomography: a new approach to efficient human-computer interaction for medical imaging

By utilizing virtual reality (VR) technologies the computer system virtusMED implements the concept of virtual tomography for exploring medical volumetric image data. Photographic data from a virtual patient as well as CT or MRI data from real patients are visualized within a virtual scene. The view of this scene is determined either by a conventional computer mouse, a head-mounted display or a freely movable flat panel. A virtual examination probe is used to generate oblique tomographic images which are computed from the given volume data. In addition, virtual models can be integrated into the scene such as anatomical models of bones and inner organs. virtusMED has shown to be a valuable tool to learn human anaotomy and to udnerstand the principles of medical imaging such as sonography. Furthermore its utilization to improve CT and MRI based diagnosis is very promising. Compared to VR systems of the past, the standard PC-based system virtusMED is a cost-efficient and easily maintained solution providing a highly intuitive time-saving user interface for medical imaging.

Elastodynamic shape modeling in virtual medicine

Surgical simulation is the coming training method for medical education. The main reasons for this are the reduced risk for the patients and the easy repeatability of complicated surgical procedures. Therefore, an improved impression of reality during the simulated training must be obtained. For this, a complex model of the humans anatomy and physiology is needed. With regards to pathological conditions, which should be considered, it is necessary to build more general anatomical models. Simple static models are unsuitable for surgical simulation because convincing interactivity is only possible with deformable organs and elastic tissues. Traditional models of tissue deformation have difficulties to simulate the appearance of deformation because of the unknown physical parameters of the tissues elasticity. Hence the paper describes a method for elastodynamic shape modeling with neuro fuzzy systems, which are able to adapt the necessary parameters from real tissues.

Automatic and adaptive brain morphometry on MR images

Automatic segmentation of brain tissue on magnetic resonance images remains a challenge due to variations in brain shape and size, use of different pulse sequences, overlapping signal intensities, and imaging artifacts. An image analysis system that combines robust image processing techniques with anatomic knowledge was developed to meet this challenge. The system is fast, accurate, and robust to the variability of brain anatomy and imaging conditions and is useful for studying the brain in healthy adults, patients with a shrunken brain due to brain atrophy, and children. With this new thresholding method, the range of the proportion of brain tissue can be determined, thereby making good segmentation possible even in the presence of intrasectional inhomogeneity and noise. The system can adaptively adjust the morphologic processing to break the connection between brain and nonbrain tissue while preserving small brain fragments. It can also segment the white matter and gray matter of the two hemispheres separated by the midsagittal plane. The segmentation results can be visualized in either two or three dimensions. The system has been validated against 53 public data sets and qualitatively tested on 47 clinical data sets, yielding a better accuracy than that of the four most popular methods of brain segmentation.

Computergestützte Segmentierung des frakturierten Acetabulums in CT-Aufnahmen mit Hilfe aktiver Konturen zur Klassifikation und Operationsplanung in der Unfallchirurgie

Fur Diagnose und Operationsplanung in der Unfallchirurgie ist die computergestutzte Segmentierung von zentraler Bedeutung. Hier wird die Anwendung eines auf aktiven Konturen basierenden Algorithmus zur Segmentierung des frakturierten Acetabulums in CT-Aufnahmen beschrieben. Der Algorithmus erganzt eine Software, die im Rahmen eines Projekts zur CT-basierten Klassifikation von Acetabulumfrakturen entwickelt worden ist. Die Segmentierung erfolgt primar schichtbildbasiert und unterstutzt die vom Chirurgen durchgefuhrte Bearbeitung einzelner CT-Bilder.

A new 3D Interaction Concept for Radiological Image Analysis

In radiology, the interaction with medical image data is limited due to restricted degrees of freedom (DOF) provided by the common user interfaces that are based on the conventional computer mouse as main input device. This work presents an approach with additional DOF for the navigation through three-dimensional image data sets. The initial results show that more DOF can improve learnability and stimulation by providing a more direct interaction, but the developed prototype needs improvement with regard to input precision.

Selbstbeschreibungsfähigkeit von Touch-Gesten mobiler Geräte.

Mobile Geräte haben PCs hinsichtlich der Nutzungshäufigkeit prozentual überholt. Die Selbstbeschreibungsfähigkeit laut ISO 9241-110 besagt, dass zu jeder Zeit offensichtlich sein muss, welche Aktionen in einer Software durchgeführt werden können. Laut iOS Human Interface Guidelines wird davon ausgegangen, dass Benutzer Touch-Gesten kennen und ausprobieren werden. Dies widerspricht der Definition der Selbstbeschreibungsfähigkeit. Ein von uns durchgeführter Usability-Test der mobilen Apple Mail-Anwendung hat ergeben, dass Swipe-Gesten und Doubletap-Aktionen von der überwiegenden Zahl der Nutzer nicht gefunden werden. Ein vergleichendes Experiment mit einem von uns entwickelten Prototypen zeigt, dass Animationen und visuelle Hinweise helfen können, die Selbstbeschreibungsfähigkeit zu erhöhen und so „versteckte“ Funktionen schneller zu finden. Dafür müssen Hinweise gut sichtbar und eindeutig zuzuordnen sein. Die bessere Selbstbeschreibungsfähigkeit kann zu erhöhter Benutzerfreundlichkeit mobiler Touch-Anwendungen beitragen.

Virtuelle 3D-Szenen zur Exploration radiologischer Volumendaten am Beispiel der CT-basierten Diagnostik von Kugelgelenken

Die exakte Klassifikation einer Fraktur ist Vorraussetzung fur die korrekte Wahl einer optimalen Therapieform. Konventionelle Klassifikationsmethoden von Acetabulumfrakturen basieren auf der Analyse von Rontgenaufnahmen, in denen nicht alle Parameter einer solchen Fraktur eindeutig klassifizierbar sind. Hier wird eine CT-basierte Methode vorgestellt, bei der die raumlichen Eigenschaften von Kugelgelenken ausgenutzt werden. Aus den CT-Daten wird ein 3D-Modell der Fraktur erstellt, indem die einzelnen Fraktursegmente durch Kugeloberflachenbereiche approximiert werden. Das erzeugte 3D-Modell der Fraktur ermoglicht es dem Anwender einen raumlichen Eindruck uber Frakturverlauf und Zusatzverletzungen zu erhalten. Tests mit dem entwickelten Prototypen haben die Eignung dieser Methode zur Klassifikation von Acetabulumfrakturen gezeigt.