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Purpose: Little is known about the way orbital fat slides and/or deforms during eye movements. We compared two deformation algorithms from a sequence of MRI volumes to visualize this complex behavior. Methods: Time-dependent deformation data were derived from motion-MRI volumes using Lucas and Kanade Optical Flow (LK3D) and nonrigid registration (B-splines) deformation algorithms. We compared how these two algorithms performed regarding sliding and deformation in three critical areas: the sclera-fat interface, how the optic nerve moves through the fat, and how the fat is squeezed out under the tendon of a relaxing rectus muscle. The efficacy was validated using identified tissue markers such as the lens and blood vessels in the fat. Results: Fat immediately behind the eye followed eye rotation by approximately one-half. This was best visualized using the B-splines technique as it showed less ripping of tissue and less distortion. Orbital fat flowed around the optic nerve during eye rotation. In this case, LK3D provided better visualization as it allowed orbital fat tissue to split. The resolution was insufficient to visualize fat being squeezed out between tendon and sclera. Conclusion: B-splines performs better in tracking structures such as the lens, while LK3D allows fat tissue to split as should happen as the optic nerve slides through the fat. Orbital fat follows eye rotation by one-half and flows around the optic nerve during eye rotation. Translational Relevance: Visualizing orbital fat deformation and sliding offers the opportunity to accurately locate a region of cicatrization and permit an individualized surgical plan.

Background and Objectives: The goal of this study was to investigate, with magnetic resonance imaging, the human anatomic positions of the spinal canal (eg, spinal cord, thecal tissue) in various postures and identify possible implications from different patient positioning for neuraxial anesthetic practice. METHOD: Nine volunteers underwent magnetic resonance imaging in supine, laterally recumbent, and sitting (head-down) positions. Axial and sagittal slices of the thoracic and lumbar spine were measured for the relative distances between anatomic structures, including dura mater and spinal cord. Results: The posterior dura-spinal cord (midline) distance is on average greater than the anterior dura-spinal cord (midline) distance along the thoracic spinal column, irrespective of volunteer postures (P < 0.05). The separation of the dura mater and spinal cord is greatest posterior in the middle thoracic region compared with upper and lower thoracic levels for all postures of the volunteers (P < 0.05). By placing the patient in a head-down sitting posture (as commonly done in epidural and spinal anesthesia), the posterior separation of the dura mater and spinal cord is increased. Conclusions: The spinal cord follows the straightest line through the imposed geometry of the spinal canal. Accordingly, there is relatively more posterior separation of the cord and surrounding thecal tissue at midthoracic levels in the apex of the thoracic kyphosis. Placing a patient in a position that accentuates the thoracic curvature of the spine (ie, sitting head-down) increases the posterior separation of the spinal cord and dural sheath at thoracic levels.

In this paper, we present a new visual way of exploring state sequences in large observational time-series. A key advantage of our method is that it can directly visualize higher-order state transitions. A standard first order state transition is a sequence of two states that are linked by a transition. A higher-order state transition is a sequence of three or more states where the sequence of participating states are linked together by consecutive first order state transitions. Our method extends the current state-graph exploration methods by employing a two dimensional graph, in which higher-order state transitions are visualized as curved lines. All transitions are bundled into thick splines, so that the thickness of an edge represents the frequency of instances. The bundling between two states takes into account the state transitions before and after the transition. This is done in such a way that it forms a continuous representation in which any subsequence of the time series is represented by a continuous smooth line. The edge bundles in these graphs can be explored interactively through our incremental selection algorithm.We demonstrate our method with an application in exploring labeled time-series data from a biological survey, where a clustering has assigned a single label to the data at each time-point. In these sequences, a large number of cyclic patterns occur, which in turn are linked to specific activities. We demonstrate how our method helps to find these cycles, and how the interactive selection process helps to find and investigate activities.

Objectives and background: Quantitative analysis of intracoronary optical coherence tomography (OCT) image data (QOCT) is currently performed by a time-consuming manual contour tracing process in individual OCT images acquired during a pullback procedure (frame-based method). To get an efficient quantitative analysis process, we developed a fully automatic three-dimensional (3D) lumen contour detection method and evaluated the results against those derived by expert human observers. Methods: The method was developed using Matlab (The Mathworks, Natick, MA). It incorporates a graphical user interface for contour display and, in the selected cases where this might be necessary, editing. OCT image data of 20 randomly selected patients, acquired with a commercially available system (Lightlab imaging, Westford, MA), were pulled from our OCT database for validation. Results: A total of 4,137 OCT images were analyzed. There was no statistically significant difference in mean lumen areas between the two methods (5.03 +/- 2.16 vs. 5.02 +/- 2.21 mm(2); p = 0.6, human vs. automated). Regression analysis showed a good correlation with an r value of 0.99. The method requires an average 2-5 sec calculation time per OCT image. In 3% of the detected contours an observer correction was necessary. Conclusion: Fully automatic lumen contour detection in OCT images is feasible with only a select few contours showing an artifact (3%) that can be easily corrected. This QOCT method may be a valuable tool for future coronary imaging studies incorporating OCT. (C) 2009 Wiley-Liss, Inc.

The development of Medical Image Analysis (MIA) applications that can successfully be applied in clinical practice is difficult for several reasons, one of them being the large amount and variety of resources involved (people, data, methods, computing). The application goes through several phases (development, parameter optimization, evaluation and clinical deployment) usually supported by different systems. The lack of support for information flow from phase to phase puts extra logistics burden on the lifecycle of MIA applications. In this paper we describe our first efforts to develop a Problem Solving Environment (PSE) for MIA applications using the three systems available at the proof-of-concept environment of the Virtual Laboratory for e-Sciences project. The proposed PSE implements data provenance mechanisms that support information flow among systems, facilitating navigation across phases of the application lifecycle.

The cohort size required in epidemiological imaging genetics studies often mandates the pooling of data from multiple hospitals. Patient data, however, is subject to strict privacy protection regimes, and physical data storage may be legally restricted to a hospital network. To enable biomarker discovery, fast data access and interactive data exploration must be combined with high-performance computing resources, while respecting privacy regulations. We present a system using fast and inherently secure light-paths to access distributed data, thereby obviating the need for a central data repository. A secure private cloud computing framework facilitates interactive, computationally intensive exploration of this geographically distributed, privacy sensitive data. As a proof of concept, MRI brain imaging data hosted at two remote sites were processed in response to a user command at a third site. The system was able to automatically start virtual machines, run a selected processing pipeline and write results to a user accessible database, while keeping data locally stored in the hospitals. Individual tasks took approximately 50% longer compared to a locally hosted blade server but the cloud infrastructure reduced the total elapsed time by a factor of 40 using 70 virtual machines in the cloud. We demonstrated that the combination light-path and private cloud is a viable means of building an analysis infrastructure for secure data analysis. The system requires further work in the areas of error handling, load balancing and secure support of multiple users.

Computer-aided diagnosis (CAD) is a helpful addition to laborious visual inspection for preselection of suspected colonic polyps in virtual colonoscopy. Most of the previous work on automatic polyp detection makes use of indicators based on the scalar curvature of the colon wall and can result in many false-positive detections. Our work tries to reduce the number of false-positive detections in the preselection of polyp candidates. Polyp surface shape can be characterized and visualized using lines of curvature. In this paper, we describe techniques for generating and rendering lines of curvature on surfaces and we show that these lines can be used as part of a polyp detection approach. We have adapted existing approaches on explicit triangular surface meshes, and developed a new algorithm on implicit surfaces embedded in 3D volume data. The visualization of shaded colonic surfaces can be enhanced by rendering the derived lines of curvature on these surfaces. Features strongly correlated with true-positive detections were calculated on lines of curvature and used for the polyp candidate selection. We studied the performance of these features on 5 data sets that included 331 pre-detected candidates, of which 50 sites were true polyps. The winding angle had a significant discriminating power for true-positive detections, which was demonstrated by a Wilcoxon rank sum test with p < 0.001. The median winding angle and inter-quartile range (IQR) for true polyps were 7.817 and 6.770 - 9.288 compared to 2.954 and 1.995 - 3.749 for false-positive detections

Finding a correct a priori back-to-front (BTF) visibility order- ing for the perspective projection of the voxels of a rectangular volume poses interesting problems. The BTF ordering presented by Frieder et al. (6) and the permuted BTF presented by Westover (14) are correct for parallel projection but not for perspective projection (12). Swan presented a constructive proof for the correctness of the perspective BTF (PBTF) ordering (12). This was a significant improvement on the existing orderings. However, his proof assumes that voxel projections are not larger than a pixel, i.e. voxel projections do not overlap in screen space. Very often the voxel projec- tions do overlap, e.g. with splatting algorithms. In these cases, the PBTF ordering results in highly visible and characteristic rendering artefacts. In this paper we analyse the PBTF and show why it yields these ren- dering artefacts. We then present an improved visibility ordering that remedies the artefacts. Our new ordering is as good as the PBTF, but it is also valid for cases where voxel projections are larger than a single pixel, i.e. when voxel pro- jections overlap in screen space. We demonstrate why and how our ordering works at fundamental and implementation levels.

A research programme that aims to improve the state of the art in shoulder joint replacement surgery has been initiated at the Delft University of Technology. Development of improved endoprostheses for the upper extremities (DIPEX), as this effort is called, is a clinically driven multidisciplinary programme consisting of many contributory aspects. A part of this research programme focuses on the pre-operative planning and per-operative guidance issues. The ultimate goal of this part of the DIPEX project is to create a surgical support infrastructure that can be used to predict the optimal surgical protocol and can assist with the selection of the most suitable endoprosthesis for a particular patient. In the pre-operative planning phase, advanced biomechanical models of the endoprosthesis fixation and the musculo-skeletal system of the shoulder will be incorporated, which are adjusted to the individual's morphology. Subsequently, the support infrastructure must assist the surgeon during the operation in executing his surgical plan. In the per-operative phase, the chosen optimal position of the endoprosthesis can be realised using camera-assisted tools or mechanical guidance tools. In this article, the pathway towards the desired surgical support infrastructure is described. Furthermore, we discuss the pre-operative planning phase and the per-operative guidance phase, the initial work performed, and finally, possible approaches for improving prosthesis placement.

The acquisition and generation of flow data played an essential role in medical research and partially also in treatment planning. An important example is the measurement and simulation of blood flow. This is motivated by increasing evidence that blood flow changes in a characteristic way as a consequence of vascular diseases, such as aneurysms and aortic dissection. These diseases primarily occur in regions with complex (sometimes even turbulent) and instable flow that changes strongly over time. Blood flow may be measured, e.g., with Doppler ultrasound or special MRI sequences. As an alternative, blood flow may be simulated with computational fluid dynamics (CFD). Simulations not only represent the current physiological situation but also enable to predict the effect of treatment, e.g., of stenting or coiling an aneurysm. In a similar way, biomechanical simulations enable to predict the stress distribution depending on the choice and placement of an implant. Biophysical simulations are also accomplished to investigate the electrophysiology of the heart, and thus to better understand arrhythmia, and to simulate the temperature distribution caused by heat-inducing tumor therapies, such as radio-frequency ablation. For these simulations, medical image data needs to be processed to generate surface meshes of the relevant anatomy. These surface meshes are transformed in volume grids representing also a mesh of the internal structures. Finally, simulations are performed and result in multivalued and multidimensional, time-dependent flow data. These flow data represent the regional distribution of attributes, such as flow speed, flow direction, pressure, and further related attributes such as velocity or pressure gradient. The generation of simulation models and the exploration of the related results are relatively new in medicine and thus we describe primarily prototyping solutions. In real life, most flow phenomena are time-dependent, i.e., flow patterns change over time. Thus, time-dependent (unsteady) simulations are more realistic than simplified steady simulations. However, steady simulations are much faster to compute and easier to explore. As a consequence, we describe the exploration of steady and unsteady simulations.

Illustrative visualization techniques deviate from physically-based rendering. They were developed to create renditions that consider perceptual capabilities of humans. As an example, humans infer information about shape not only from realistic shading but also from hatching and from outlines that support the mental separation of nearby objects rendered in similar colors. Illustrative techniques are also motivated by human cognitive processes, such as attention and situation awareness. Thus, the additional degrees of freedom of illustrative visualization should be used to emphasize what the author the visualization considers important. Illustrative visualization techniques are inspired by the work of professional scientific and medical illustrators, who developed an impressive set of techniques that depict objects and relations in an abstract manner. Boundary emphasis, non-realistic shading models, and feature lines, such as silhouettes are employed to emphasize essential features of anatomical structures. An essential prerequisite for most of these techniques is curvature estimation. Thus, we introduce curvature-related measures and describe curvature estimation techniques for polygonal surface models. Curvature-related measures, such as Gaussian curvature, are frequently used to filter feature and to restrict the display to regions with high curvature. We explain view-dependent and view-independent feature lines as well as feature lines that emphasize discontinuities of the illumination. Specific examples include ridge and valley lines, suggestive contours, apparent ridges, and photonic extremum lines. In addition to feature lines, stippling, and hatching techniques are introduced, since feature lines are often not sufficient to convey surface shapes. Efficient image generation and frame coherence are essential aspects of computer-generated hatching and stippling. Smart visibility techniques, such as cutaway views, breakaway views, and ghosted views, adapt the transparency of objects to make important structures visible. As an example, the surface of an organ may be displayed semitransparently in regions where a tumor is behind. However, in other regions it may be rendered fully opaque.

This chapter provides an introduction into the most essential medical image acquisition processes with a focus on tomographic 3D image data. As a prerequisite sampling processes and interpolation is explained. We briefly discuss major requirements that guide the selection of imaging modalities in practice, e.g., to provide a sufficient spatial resolution for the diagnostic question, to limit the exposure to radiation and to achieve a sufficient image quality in terms of contrast and signal-to-noise ratio (SNR). Readers should understand that trade-offs between these conflicting goals are necessary. Thus, neither optimum spatial resolution nor optimum image quality are relevant goals in clinical practice. In this chapter we focus on tomographic imaging modalities, in particular on CT and MRI data. Typical artifacts that hamper image interpretation, such as inhomogeneity, partial volume effects, and streaking are explained. We consider recent developments, e.g., dual source and dual energy CT and ultra highfield MRI. Hybrid PET/CT and PET/MRI scanners are an exciting development of the last decade. We discuss them as examples for the potential of the complementary use of imaging data and the necessity to fuse the resulting information. Specific examples of diagnostic questions that benefit from certain imaging techniques are given throughout the chapter. Finally, we consider imaging techniques for intraoperative monitoring, such as fluoroscopy and intravascular ultrasound.

Labeling and measurements enhance medical visualizations with useful information for documentation, follow-up examinations and educational purposes. Labeling and measuring may be performed interactively, e.g., by dragging an annotation line or selecting points for distance measurements. An alternative is the automatic placement of labels, where labels and annotation lines are placed such that they do not overlap with each other. Automatic labeling is essential if several anatomical structures, e.g., five liver metastases should be labeled. We explain labeling techniques for 2D slice-based visualizations as well as for 3D renderings. Tumor staging, the assessment of the severity of a vascular disease, the respectability of a patient, and many other essential tasks depend on a quantitative assessment of pathological structures and the spatial relations between them and adjacent anatomical structures. The diameter and length of a stenosis and the angle between bones after fracture are just a few examples of measures directly relevant to treatment decisions. In treatment monitoring, the essential goal is to assess whether a pathology shrinked down, stayed constant or grew despite treatment. Distance, angle, cross-sectional area and volume measurements are performed to support such tasks. Therefore, we discuss algorithms for efficiently and precisely determining minimum distances between anatomical structures as well as an algorithm to determine the longest diameter of a segmented object. We also discuss different variants of integrating these measures in 3D visualizations. Measurements always require a discussion of their accuracy, since unreliable measurements may mislead users.

Surgeons and interventional radiologists benefit from intraoperative guidance to deliver the treatment precisely, as it was planned. Printouts from a planning system are often of limited value, since they are hard to translate to the intraoperative situation. This chapter focuses on techniques that are used during an intervention. Hence, they are called intraoperative techniques. In intraoperative visualization, a lot of research is motivated by the challenges of minimally-invasive surgery and interventions, such as needle insertion, laparoscopic and endoscopic interventions. In particular, we introduce augmented reality (AR) techniques where relevant information from preoperative images is superimposed with live images from surgery. Metaphorically, many people refer to this technology as X-ray vision, i.e., the surgeon is able to see through the skin or an organ and observes the operative site before he or she actually arrives there. Such technologies were introduced in the 1990s in industrial applications, such as the assembly and maintenance of aircrafts and cars. Workers were supported by placing blueprints onto surfaces. In a similar way, planned resection lines, surgical targets or risk structures may be displayed along with organ surfaces, the skull or other anatomical structures. Thus, the target regions, e.g., a deep-seated tumor, may be reached faster with reduced risks of harming critical structures. Thus, implants in total knee replacement, hip or shoulder replacement may be fitted more accurately and, hopefully, the rate of revision surgery is reduced. In very difficult cases, e.g., a large deep-seated tumor close to vital structures, navigation and intraoperative visualization may even shift the border of operability. Major application areas are ENT and neurosurgery as well as orthopedics. Intraoperative visualization requires an appropriate dataset of the patient, preprocessing, e.g., segmentations, a sufficiently accurate registration procedure that maps the patient’s dataset to the patient himself. Once this registration is obtained and carefully validated, visual output from the dataset and surgical instruments can be mapped to the patient. These instruments need to be tracked by a navigation system. Thus, surgeons can locate instruments on the patient (with their eyes) and in the dataset (through the registration mapping). This setup with a direct correlation between the patient’s dataset and the patient is referred to as image-guided surgery (IGS). We also want to make the reader aware of potential and real drawbacks which are due to a complex processing chain that may involve significant errors. Moreover, the use of the underlying technology is challenging and the necessary setup times are often significant.

In this chapter, we describe techniques to interactively explore volume data. Exploration in this context refers to the specification of which portions of the data should be visible and how they should be displayed. The techniques we describe are used to modify the brightness, contrast and opacity of voxels, and thus the contrast between different materials. Transfer functions, clipping planes, and more advanced virtual resection techniques are amongst these techniques. A crucial problem is to provide user interfaces and interaction techniques that enable physicians to specify their intent as directly as possible. The intent of physicians is to emphasize certain tissues, material boundaries, or spatial relations between adjacent structures, e.g., around a pathology. Simple user interfaces, however, just provide a transfer function editor, where the user has to spend much effort in order to come up with a visualization that is at least close to the above-mentioned visualization goals. Moreover, physicians think in terms of diagnostic and treatment decisions, e.g., with respect to infiltration and resectability. The raw parameters of a transfer function are too low level to enable convenient assistance for such questions. While traditional transfer functions are one-dimensional and global, we also discuss more advanced strategies since these allow to discriminate tissues better. Two-dimensional transfer functions, using gradient magnitude or distances to a target structure are specific examples that overcome the restriction of intensity-based 1D transfer functions. We put special emphasis on recently introduced techniques, such as size-based transfer functions, visibility-driven transfer functions, and local transfer functions, e.g., those that employ LH (low/high) histograms. Several of these advanced techniques employ clustering as an analysis technique to identify regions with similar features. Therefore we briefly introduce a number of clustering techniques and discuss their potential and limitations. Volume interaction also comprises techniques to geometrically specify which portions of the data are visible, e.g., with clipping planes or cutting geometries that are moved inside the data. In this way, virtual resections can be performed in order to define which portions of an organ remain after a pathology is removed from a certain area with a certain safety margin. A basic interaction technique is picking where a certain anatomical structure is selected as a prerequisite for further interaction. Picking suffers from ambiguities since several structures are partially visible at each screen space location. We describe contextual picking, as a knowledge-based technique, and perception-based techniques to disambiguate selections.

Endoscopy is a diagnostic procedure that is used by pulmonologists to diagnose bronchial diseases, such as lung cancer, by gastroenterologists to detect changes of the colon wall, and by a number of other medical disciplines. The cameras attached to the tip of an endoscope provide full color images of internal structures at high resolution. Endoscopy is an invasive diagnostic procedure where sedation of the patient is necessary and complications may occur. Thus, it is an essential goal to reduce the necessity of such procedures. Inspired by “real” fiber endoscopy, the walls of vascular and air-filled structures can be explored by means of internal rendering of CT or MRI data. Such virtual endoscopy should resemble fiber endoscopy, e.g., similar wide-angle optics should be simulated to enable efficient diagnosis and treatment planning. Thus, endoscopy is a metaphor, highly useful for developers and users of virtual endoscopy applications. Virtual endoscopy, a classic area in medical visualization, can partially replace fiber endoscopy as a diagnostic procedure. Moreover, it may be used in a complementary manner. While real vessel walls are opaque, in virtual endoscopy some areas may be rendered semitransparently to reveal important structures behind the cavity that is explored (the “look behind the wall”). While in patients, the passage of an area in the bronchial or a vascular tree may be blocked by a severe stenosis, the virtual camera in a 3D model may pass that region. In principle, virtual endoscopy may provide the same information as the underlying CT or MRI data. However, the internal rendering may significantly improve the detection and characterization of pathologies along walls. In particular, the assessment of anatomical structures with a complex morphology, e.g., the bronchial tree and the colon, benefits from internal views and guidance through that anatomy. However, internal renderings do not provide sufficient context and orientation. At least one additional view is required to indicate the position and orientation of the endoscope. In this chapter, we also discuss the peculiarities of rendering rather narrow internal structures, since these peculiarities enable an efficient rendering. The performance gain may be used to integrate effects that increase the visual realism—an aspect that is relevant for surgical planning and training. Virtual endoscopy involves a number of user interface issues, e.g., how users control the virtual camera, how they can deviate from the pre-planned path and how additional views may support the interpretation of the current internal 3D rendering. Finally, we give a survey on the most essential clinical applications, namely virtual colonoscopy, virtual bronchoscopy, and virtual angioscopy focusing on specific clinical questions and the evaluation. Despite the general acceptance of virtual endoscopy there are ongoing debates on specific diagnostic and treatment planning questions where exactly virtual endoscopy should be used and how it should be integrated in clinical workflows. Therefore, we give an overview on related clinical studies.

In this chapter we discuss trends for the future of visual computing in medicine. Our exposition is based both on discussions with physicians about the future of medicine, and on discussions with medical visualization experts. Development is driven by advances in data acquisition, advances in computing and the growing complexity and expectations associated with clinical tasks. In order to address these challenges, visual computing in medicine has to be integrated with developments in a number of related fields. Personalized medicine, predictive modeling and large scale epidemiological studies are major application domains medicine that will play an important future role in our field.

To efficiently support tasks in clinical practice, visual computing algorithms need to be integrated in a carefully designed user interface. The goal of regular clinical use requires to adopt a user-centered design approach. This comprises an in-depth analysis of the tasks to be solved and the target user group. Task analysis methods, such as observation and interviews, are discussed to enable readers to identify, which diagnostic or treatment processes are essential, which decisions have to be taken, which criteria are essential for such decisions, which information is needed to support such decisions, and how this information shall be displayed. The design and development of prototypes should be carried out only based on verified assumptions about the essential usage scenarios. This process usually starts with some kind of representation of the current solution, e.g., a workflow diagram or, more informally, a set of user stories. The process continues with a representation of user needs and their priorities as well as a representation of the envisioned solution. Thus, user interface design is much more than a nice visual wrap-up for some algorithms. It is instead a complex and highly iterative process, which requires early and continuous feedback from the target user group and other relevant stakeholders. Users of medical visualization systems are medical doctors from a specific discipline, such as radiology, surgery, nuclear medicine or radiation treatment, who use such tools for diagnosis support, treatment planning, and follow-up studies to evaluate the success of treatment. There are, of course, strong differences between research settings, where requirements may be vague, and more product-oriented developments, where precise functional goals and usability goals drive the whole process. We cover both situations but slightly focus on research projects. Among the many interaction techniques developed so far, 3D interaction is particularly relevant for medical visualization. Thus, we discuss 3D selection, 3D transformation and navigation. While in current practice, most medical visualization systems are operated with keyboard and mouse, more advanced input and output devices, e.g., 3D input devices, have great potential. Thus, we give an overview of such devices and the experiences gained in medical applications. A specially challenging situation for medical visualization is intraoperative use, where devices need to be sterile and surgeons need to focus on the patient. Interaction techniques and devices for these settings are also described. Finally, we discuss the evaluation of interaction techniques and user interfaces.

In this chapter, we will explain how general strategies for computer-assisted surgery are adapted and employed to meet the needs of ENT surgery (ear, nose, throat). The high density of crucial anatomical structures in the ear, nose, and throat makes surgery highly demanding. Accuracy is often essential, e.g., in implanting hearing aids, and esthetic considerations are often relevant. Therefore, this surgical discipline is characterized by a high level of technical support. Anatomical structures are relatively fixed and do not exhibit large displacements of tissue in surgery. This makes ENT surgery an ideal goal for computer-assistance. In this chapter, we focus on 3D visualizations used for surgical planning and interdisciplinary treatment discussions in ENT surgery. We start with virtual endoscopy solutions for planning and training functional endoscopic sinus surgery. This intervention is performed in the nasal region if patients suffer from sinusitis. This type of surgery becomes risky in case of extended sinusitis when surgery needs to be performed close to the base of the skull or close to the optical nerve. We also discuss surgery in the filigrane ear region where implants and hearing aids are inserted in order to improve hearing. Implant selection and placement are essential aspects of this kind of surgery. The largest part of the chapter is devoted to the removal of tumors in the neck region, e.g., neck dissections where surgeons are interested in a detailed inspection of the spatial relations around primary tumors and metastases.

Compared to static image data where the morphology of anatomical and pathological structures is represented with high spatial resolution, dynamic image data characterizes functional processes, such as metabolism and blood flow. These functional processes are often essential to detect diseases at an early stage or to discriminate pathologies with very similar morphology. Perfusion imaging, where the perfusion of tissue with blood is measured, is an important example of dynamic imaging. We describe perfusion data, which are acquired to support essential diagnostic tasks, e.g., stroke diagnosis, the assessment of different types of tumors and the diagnosis of the coronary heart disease.

In this chapter, we discuss image analysis techniques which extract clinically relevant information from medical image data. Image analysis enables the generation of high-quality visualizations focused on selected anatomical structures and also supports quantitative analysis of pathologies and spatial relations, e.g., volumes of pathological structures or distances between anatomical structures. Image analysis is crucial for many diagnosis and therapy planning tasks. As an example, the identification and delineation of a tumor is often a prerequisite to determine its extent and volume and finally to select an optimal therapy. This chapter provides an overview on important image analysis tasks. We introduce image analysis pipelines, starting with preprocessing and filtering to support subsequent algorithms. The chapter is focused on image segmentation—a process which assigns labels (unique identifiers) of anatomical or pathological structures to parts of the image data but also considers registration—a process where two datasets, e.g., a CT and MRI of the same patient are matched with each other to be available in a common coordinate system. Thus, registration enables the integrated multimodal visualization. The overview includes validation aspects, because of the particular relevance of accuracy and reproducibility for medical applications. We shall not only discuss algorithms which detect and analyze features in medical image data, but also interaction techniques which allow the user to guide an algorithm and to modify an existing result. This is essential, since for most typical image analysis problems fully automatic solutions are not available. Medical image data, anatomical relations, pathological processes, image modalities, and biological variability exhibit such a large variety that automatic solutions for the detection and delineation of certain structures cannot cope with all such cases.

Surgery refers to the act of cutting the tissue of a patient for the purpose of treating or diagnosis. The goal is to improve the patient’s condition, either by learning more about the disease, or by surgically remedying it. However, there is a significant risk of doing the opposite.

With the advent of modern GPUs, an increasing number of existing algorithms have been adapted, or new algorithms have been specifically developed to enable advanced GPU-based volume rendering. As a consequence, it became possible to reach interactive frame rates even for large datasets and when using high sampling rates. With the further development of modern GPUs, more computation time was available during rendering and thus the usage of more advanced algorithms became feasible. Since these approaches should be considered as extensions to the existing volume rendering paradigms rather than new algorithms, we describe them separately within this chapter. We have grouped the covered advanced direct volume rendering algorithms into two classes, based on the main challenges which have been addressed. Advanced volume rendering techniques are motivated to a large extent by perceptual issues. Thus, color, contrast, and gray value perception are essential. In addition, we discuss how advanced rendering supports the interpretation of spatial relations. While the standard volume rendering integral, based on emission and absorption, allows to convey the basic structures inherent to a volumetric dataset, it does not take the perceptual capabilities of the human visual system into account. Several studies indicate that incorporating these capabilities results in an improved perception, which could either allow a more accurate or a more rapid spatial comprehension. The dark-means-deep paradigm is a relatively old model for understanding and describing depth relationships. It is motivated by the color diminishing due to environmental fog. This is just one example indicating perceptual benefits when incorporating the human visual capabilities during image generation. Two strategies are employed to reach this goal. First, focusing on mimicking the illumination capabilities present in the real world, and second, using more abstract depth enhancement techniques, for which no counterpart can be found in real-world illumination. Within this chapter we will address both of these areas.

In medical visualization, there are a number of mapping techniques that are employed to reduce 3D data, either by projection or reformation, to effective 2D visual representations. Straightforward projection, as employed in the standard 3D graphics pipeline, also serves to reduce 3D data to a 2D representation. However, in this chapter we discuss examples that go a step further in terms of complexity and effectiveness. We have classified these reductive techniques broadly into two groups: projections and reformations. In projections, 3D information is accumulated, or flattened, along one or more directions to obtain a 2D image. In reformations, 3D information is sampled along some geometry that can later be flattened, and the sampled information along with it. There are examples that have characteristics of more than one class. The Bull’s Eye Plot, that is frequently used in cardiology, is an example of a projection (data is aggregated through the heart wall), whereas multiplanar reformatting (MPR), is a straightforward example of reformation.

Surface rendering is one of the two major rendering modes for medical volume data. Instead of classifying volume data and mapping it directly to the viewport, surface rendering is based on an indirect surface mesh representation. This mesh is either generated by extracting an isosurface from the original volume data or by transforming a segmentation result. Surface rendering of volume data was primarily motivated by two issues: it was much faster than volume rendering, since a mesh has a significantly lower memory footprint than a volume dataset and it provides clearly recognizable images with depth cues such as those caused by illumination. These original advantages have somehow lost their relevance when GPU rendering became more and more powerful and could be used to support all stages of direct volume rendering. Thus, even advanced volume rendering of typical medical datasets can be performed in real-time. In the last years, three developments occurred that raised the importance of surface extraction and surface rendering: • With the increased use of biophysical simulation, there is a need for surface meshes as a basis for volume grids that are used for simulations. • Interactive 3D visualizations are used in web browsers and mobile settings. Although direct volume rendering is also feasible in such settings, surface renderings are prevailing due to the lower memory requirements. • Surface mesh construction becomes increasingly important for 3D printing. For challenging treatment planning questions, relevant portions of the human anatomy are modeled and printed in 3D to enable an in-depth collaborative discussion of treatment options. In this chapter, we discuss surface extraction with respect to efficiency, accuracy and smoothness. This includes a discussion of GPU-based mesh extraction. We introduce a variety of mesh smoothing algorithms that process a surface mesh and reduce artifacts and staircases. Advanced mesh smoothing algorithms may be locally adapted and control volume shrinkage or other excessive modifications that hamper the accuracy. Mesh simplification algorithms are introduced to support simulation applications as well as web-based use of medical surface representations.

This chapter describes the diagnostic use of medical volume data in radiology, nuclear medicine, and radiation treatment planning. First general criteria for the quality of diagnostic processes, such as sensitivity and specificity, are explained. A brief discussion of relevant aspects of visual perception, primarily the perception of contrasts in gray value images is provided. According to the focus on diagnostic processes, we carefully discuss radiology, where the large majority of medical image data are acquired. We explain formats for the storage of medical image data according to the DICOM standard. Soft-copy reading with its potential is introduced along with conventional film-based diagnosis to contrast these processes. Computer-aided detection is introduced as an advanced and highly specialized support for physicians. 3D visualization techniques, such as Maximum intensity projection, are briefly introduced along with examples of their clinical use. Report generation and dedicate support for this process is discussed. We consider radiation treatment as a special medical discipline, where image analysis and advanced 3D visualization is already a mandatory part of the clinical routine. Finally, we discuss how medical image data and derived analysis results are used in medical team meetings, such as tumor board discussions. These meetings often involve physicians from different disciplines, discussing, e.g., combined treatment options for severe diseases.

This chapter is dedicated to educational applications of medical visualization techniques. Interactive 3D visualizations have great potential for anatomy education, as well as for surgery education, with potential users ranging from high school students and physiotherapists to medical doctors who want to rehearse therapeutical interventions. Labeling, virtual resection, and interaction techniques, such as selection and 3D rotation, are essential in these systems. Computer-assisted training systems enhance surgical manuals, surgical courses and cadaver studies by providing up-to-date multimedia content. This content is structured according to specific learning objectives and may provide tasks, such as puzzles and quizzes to challenge learners. Recent advances include web-based systems, that enable personalized access with an individual profile and possibilities to upload content and discuss with colleagues. While advanced visualization techniques currently play a minor role only, they have potential to enhance the display of complex interventions. The most complex systems aiming at medical education are surgical simulators that integrate haptic feedback, collision detection, soft-tissue deformation as well as the simulation of various complications. We discuss these different classes of medical education systems and focus on the interactive visualization techniques used in these settings. Despite their great potential, computer-assisted training systems are still not widespread. Thus, we discuss future work, e.g., improved curricular integration.

Due to continuing advances in medical imaging technology, and in medicine itself, techniques for visualizing medical image data have become increasingly important. In this chapter, we present a brief overview of the past 30 years of developments in medical visualization, after which we discuss the research challenges that we foresee for the coming decade.

For diagnosis as well as for many therapy planning tasks it is crucial to understand the branching pattern and morphology of tree-like anatomical structures, such as nerves, vascular, and bronchial trees. A slight difference in vascular topology may lead to a strong difference in the treatment strategy. For therapy planning, it is of paramount importance to recognize shape features and morphology of vascular structures as well as spatial relations between vascular and other relevant structures. For a convenient interpretation, the curvature, the depth relations, and the diminution of the diameter toward the periphery should be depicted correctly. For diagnosis it is essential to reliably recognize and assess vessel wall abnormalities, such as narrowings (stenosis) or dilated parts (aneurysms) as well as plaque and thrombus formation. Primarily CT and MR angiography are employed. Digital subtraction angiography (DSA), a variant of X-ray, is also available as a 3D imaging technique. We focus on processing and visualizing information derived from CT and MR angiography data. In particular for diagnosis, 2D views of the original data, derived information, such as oblique reformations and changes of the vessel diameter, need to be integrated carefully with 3D views. We briefly explain image analysis (segmentation and centerline extraction) techniques, which are a prerequisite for high-quality vessel visualization. We continue with surface visualization techniques using the vessel segmentation result. Model-based and illustrative surface visualizations of vascular trees serve primarily for treatment planning. They are based on model assumptions, such as a circular cross section. In contrast, model-free approaches do not enforce any assumptions but display vascular structures, as they have been segmented. To achieve a high visual quality and ease interpretation, again, implicit surface visualization techniques are considered. Adapted volume rendering also has a great potential for visualizing vascular structures in particular for diagnostic purposes. We present strategies for transfer function specification and local volume rendering techniques, using the segmentation result to emphasize certain regions.

In direct volume visualization, the volumetric dataset is directly represented visually without generating an intermediate representation. This is performed by somehow projecting the volumetric information onto the viewing plane. In this chapter we explain the major strategies and variations that can be employed for this projection. We will focus on the theoretical aspects of direct volume visualization, in particular the employed physical model and the mathematical foundation of the resulting volume rendering equation. Volume rendering relies on numerical approximations of this integral. Volume rendering approaches differ in the processing order of the pipeline that evaluates the volume rendering equation and its components such as sampling and compositing. We explain the volume raycasting algorithm, after which we compactly treat the implementation of direct volume raycasting on the GPU. Despite the classical volume rendering that provides essential depth cues, we also introduce compositing variations, such as maximum intensity projection and X-ray projection. Although these methods do provide only limited depth cues when handling data interactively, there are specific use cases, in particular for the maximum intensity projection that is frequently used for the assessment of vascular structures. Efficient rendering, e.g., by using hierarchical data structures or early ray termination (in volume raycasting) are briefly explained. Slab rendering, where instead of the full volume only a limited set of slices is rendered at the same time, is often an appropriate trade-off between slice-based visualization and 3D rendering. PreIntegrated volume rendering is explained since it leads to a particularly high image quality.

In small animal imaging studies, when the locations of the micro-structures of interest are unknown a priori, there is a simultaneous need for full-body coverage and high resolution. In MRI, additional requirements to image contrast and acquisition time will often make it impossible to acquire such images directly. Recently, a resolution enhancing post-processing technique called super-resolution reconstruction (SRR) has been demonstrated to improve visualization and localization of micro-structures in small animal MRI by combining multiple low-resolution acquisitions. However, when the field-of-view is large relative to the desired voxel size, solving the SRR problem becomes very expensive, in terms of both memory requirements and computation time. In this paper we introduce a novel local approach to SRR that aims to overcome the computational problems and allow researchers to efficiently explore both global and local characteristics in whole-body small animal MRI. The method integrates state-of-the-art image processing techniques from the areas of articulated atlas-based segmentation, planar reformation, and SRR. A proof-of-concept is provided with two case studies involving CT, BLI, and MRI data of bone and kidney tumors in a mouse model. We show that local SRR-MRI is a computationally efficient complementary imaging modality for the precise characterization of tumor metastases, and that the method provides a feasible high-resolution alternative to conventional MRI.

The surgical anatomy of the pelvis is highly complex. Anorectal and urogenital dysfunctions occur frequently after pelvic oncological surgery and are mainly caused by surgical damage of the autonomic nerves. A highly-detailed 3D pelvic model could increase the anatomical knowledge and form a solid basis for a surgical simulation system. Currently, pelvic surgeons still rely on the preoperative interpretation of 2D diagnostic images. With a 3D simulation system, pelvic surgeons could simulate and train different scenes to enhance their preoperative knowledge and improve surgical outcome. To substantially enrich pelvic surgery and anatomical education, such a system must provide insight into the relation between the autonomic network, the lymphatic system, and endopelvic fasciae. Besides CT and MR images, Visible Human Datasets (VHDs) are widely used for 3D modeling, due to the high degree of anatomical detail represented in the cryosectional images. However, key surgical structures cannot be fully identified using VHDs and radiologic imaging techniques alone. Several unsolved anatomical problems must be elucidated as well. Therefore, adequate analysis on a microscopic level is inevitable. The development of a comprehensive anatomical atlas of the pelvis is no straightforward task. Such an endeavor involves several anatomical and technical challenges. This article surveys all existing 3D pelvic models, focusing on the level of anatomical detail. The use of VHDs in the 3D reconstruction of a highly-detailed pelvic model and the accompanying anatomical challenges will be discussed Clin. Anat., 2012. © 2012 Wiley Periodicals, Inc.

The visualisation of vector fields is essential for many applications in science, engineering and biomedicine. A large number of vector icons has been developed, but little research has been done on their effectiveness, especially when visualising multiple vector fields simultaneously. We apply research in visualisation and cognitive science to identify four classes of post-processing techniques for visualising two 2D vector fields simultaneously: blending, overlay, bump mapping, and masking. We apply these four post-processing methods to Line Integral Convolution (LIC) textures and thus develop several novel multi-field visualisation techniques. We evaluate their effectiveness with a user study requiring participants to locate and classify singularities, and to rate each method on its effectiveness and aesthetic value. The results of the study suggest that blending is the most effective technique to combine multiple vector field visualisation textures, while masking performs worst. There is some evidence that visualisations with smooth colour changes are perceived as visually more attractive, and that aesthetics increases the perceived effectiveness of a visualisation technique.

Objective The integrated visualization of cardiac MRI during a ventricular tachycardia (VT) mapping and ablation procedure would provide improved catheter guidance and tissue assessment. We developed a system for and explored the added value of simultan o s visualization of intracardiac voltage measurements and MRI-derived myocardial scar information during VT ablation procedures. Method We propose the use of a synchronized 3D and 2D view. In 3D, the catheter will be guided optimally by assessing 3D scar characteristics and its relation to the ventricular anatomy. In 2D, a detailed assessment of the tissue can be made. We developed several 3D visualization techniques, including volume rendering of the scar and myocardial surfaces colored according to the voltage measurements. We also visualized context structures in the heart. For the 2D view, we proposed showing three adjacent slices simultaneously. To link the 3D with the 2D view, we added a linking plane and linking contours; the slice level shown in the 2D view is indicated in the 3D view. Results We evaluated our method via a case study during which we simulated the visual environment of an ablation procedure. The MRI-based volume rendering of scar tissue and the linking between the 3D and 2D views were both positively received. However, the visualization of the voltage measurements was found to be hard to interpret, partly due to the perceptually suitable but non-standard colormap. Conclusions Based on this study, we can conclude that our approach of displaying MRI data and integrating it with voltage measurements has potential to improve VT ablation procedures.

The field of volume visualization has undergone rapid development during the past years, both due to advances in suitable computing hardware and due to the increasing availability of large volume datasets. Recent work has focused on increasing the visual realism in Direct Volume Rendering (DVR) by integrating a number of visually plausible but often effect-specific rendering techniques, for instance modeling of light occlusion and depth of field. Besides yielding more attractive renderings, especially the more realistic lighting has a positive effect on perceptual tasks. Although these new rendering techniques yield impressive results, they exhibit limitations in terms of their exibility and their performance. Monte Carlo ray tracing (MCRT), coupled with physically based light transport, is the de-facto standard for synthesizing highly realistic images in the graphics domain, although usually not from volumetric data. Due to the stochastic sampling of MCRT algorithms, numerous effects can be achieved in a relatively straight-forward fashion. For this reason, we have developed a practical framework that applies MCRT techniques also to direct volume rendering (DVR). With this work, we demonstrate that a host of realistic effects, including physically based lighting, can be simulated in a generic and flexible fashion, leading to interactive DVR with improved realism. In the hope that this improved approach to DVR will see more use in practice, we have made available our framework under a permissive open source license.

Super-resolution reconstruction (SRR) is a post-acquisition method for producing a high-resolution (HR) image from a set of low-resolution (LR) images. However, for large volumes of data, this technique is computationally very demanding and time consuming. In this study we focus on the specific case of whole-body mouse data and present a novel, integrated, end-to-end approach to overcome this problem. We combine articulated atlas-based segmentation and planar reformation techniques with state-of-the-art in SRR to produce high resolution, interactively selected, localized isotropic volumes-of-interest in whole-body mouse MRI. With this method we overcome time and memory related limitations when applying the SRR algorithm to the entire dataset, enabling interactive visualization and exploration of anatomical structures of interest in whole-body MRI mouse data on a normal desktop PC.

Computer tomography (CT) technology has greatly contributed to the feasibility and convenience of detecting and visualizing the internal material constitution and geometrical fabrication of museum artifacts. This paper presents a case study of 3D virtual reconstruction for the CT-acquisition-based study of a cultural heritage artifact. It documents the complete procedure, including the preprocessing, segmentation and visualization of the data by providing coarse interactive exploration and integrated high-quality renderings. A parallel aim achieved was to use open source tools and free software for segmentation and visualization, thus providing full transparency of the adopted methodology and 3D visualization methods, and a cost effective solution for ordinary CPU-based PC users. Furthermore, the challenges of the large data volumes involved have been addressed using preprocessing, a segmentation scheme and linked front-to-back management to keep interaction and high-quality rendering available, thus achieving corresponding demands. © 2011 Elsevier Masson SAS.

Multidimensional projection has emerged as an important visualization tool in applications involving the visual analysis of high-dimensional data. However, high precision projection methods are either computationally expensive or not flexible enough to enable feedback from user interaction into the projection process. A built-in mechanism that dynamically adapts the projection based on direct user intervention would make the technique more useful for a larger range of applications and data sets. In this paper we propose the Piecewise Laplacian-based Projection (PLP), a novel multidimensional projection technique, that, due to the local nature of its formulation, enables a versatile mechanism to interact with projected data and to allow interactive changes to alter the projection map dynamically, a capability unique of this technique. We exploit the flexibility provided by PLP in two interactive projection-based applications, one designed to organize pictures visually and another to build music playlists. These applications illustrate the usefulness of PLP in handling high-dimensional data in a flexible and highly visual way. We also compare PLP with the currently most promising projections in terms of precision and speed, showing that it performs very well also according to these quality criteria.

OBJECTIVE: Computed tomography is the modality of choice for measuring osteolysis but suffers from metal-induced artefacts obscuring periprosthetic tissues. Previous papers on metal artefact reduction (MAR) show qualitative improvements, but their algorithms have not found acceptance for clinical applications. We investigated to what extent metal artefacts interfere with the segmentation of lesions adjacent to a metal femoral implant and whether metal artefact reduction improves the manual segmentation of such lesions. MATERIALS AND METHODS: We manually created 27 periprosthetic lesions in 10 human cadaver femora. We filled the lesions with a fibrotic interface tissue substitute. Each femur was fitted with a polished tapered cobalt-chrome prosthesis and imaged twice-once with the metal, and once with a substitute resin prosthesis inserted. Metal-affected CTs were processed using standard back-projection as well as projection interpolation (PI) MAR. Two experienced users segmented all lesions and compared segmentation accuracy. RESULTS: We achieved accurate delineation of periprosthetic lesions in the metal-free images. The presence of a metal implant led us to underestimate lesion volume and introduced geometrical errors in segmentation boundaries. Although PI MAR reduced streak artefacts, it led to greater underestimation of lesion volume and greater geometrical errors than without its application. CONCLUSION: CT metal artefacts impair image segmentation. PI MAR can improve subjective image appearance but causes loss of detail and lower image contrast adjacent to prostheses. Our experiments showed that PI MAR is counterproductive for manual segmentation of periprosthetic lesions and should be used with care.

Nested or intersecting surfaces are proven techniques for visualizing shape differences between static 3D objects (Weigle and Taylor II, IEEE Visualization, Proceedings, pp. 503–510, 2005). In this paper we present an image-based formulation for these techniques that extends their use to dynamic scenarios, in which surfaces can be manipulated or even deformed interactively. The formulation is based on our new layered rendering pipeline, a generic image-based approach for rendering nested surfaces based on depth peeling and deferred shading. We use layered rendering to enhance the intersecting surfaces visualization. In addition to enabling interactive performance, our enhancements address several limitations of the original technique. Contours remove ambiguity regarding the shape of intersections. Local distances between the surfaces can be visualized at any point using either depth fogging or distance fields: Depth fogging is used as a cue for the distance between two surfaces in the viewing direction, whereas closest-point distance measures are visualized interactively by evaluating one surface’s distance field on the other surface. Furthermore, we use these measures to define a three-way surface segmentation, which visualizes regions of growth, shrinkage, and no change of a test surface compared with a reference surface. Finally, we demonstrate an application of our technique in the visualization of statistical shape models. We evaluate our technique based on feedback provided by medical image analysis researchers, who are experts in working with such models.

textabstractAims: Images acquired of coronary vessels during a pullback of time-domain optical coherence tomography (OCT) are influenced by the dynamics of the heart. This study explores the feasibility of applying an in-house developed retrospective image-based gating method for OCT and the influence of catheter dislocation and luminal changes during the cardiac cycle on the outcome of quantitative OCT (QOCT). Methods and results: The gating method was developed using Matlab (The Mathworks, Natick, MA, USA) and operates in a fully-automatic manner. OCT image data of 20 randomly selected patients, acquired with a commercially available system (Lightlab Imaging, Westford, MA, USA), were pulled from our OCT database for development and validation. Twelve of the 20 datasets could be gated; the other eight pullbacks could not be gated due to a lack of motion induced artefacts. Computations required approximately 30 minutes/dataset. Quantitative comparisons between the gated and the non-gated QOCT results showed significant differences for mean areas and volumes (p

Kinematics is the analysis of motions without regarding forces or inertial effects, with the purpose of understanding joint behaviour. Kinematic data of linked joints, for example the upper extremity, i.e. the shoulder and arm joints, contains many related degrees of freedom that complicate numerical analysis. Visualisation techniques enhance the analysis process, thus improving the effectiveness of kinematic experiments. This paper describes a new visualisation system specifically designed for the analysis of multi-joint kinematic data of the upper extremity. The challenge inherent in the data is that the upper extremity is comprised of five cooperating joints with a total of fifteen degrees of freedom. The range of motion may be affected by subtle deficiencies of individual joints that are difficult to pinpoint. To highlight these subtleties our approach combines interactive filtering and multiple visualisation techniques. Our system is further differentiated by the fact that it integrates simultaneous acquisition and visual analysis of biokinematic data. Also, to facilitate complex queries, we have designed a visual query interface with visualisation and interaction elements that are based on the domain-specific anatomical representation of the data. The combination of these techniques form an effective approach specifically tailored for the investigation and comparison of large collections of kinematic data. This claim is supported by an evaluation experiment where the technique was used to inspect the kinematics of the left and right arm of a patient with a healed proximal humerus fracture, i.e. a healed shoulder fracture.

The analysis of multi-timepoint whole-body small animal CT data is greatly complicated by the varying posture of the subject at different timepoints. Due to these variations, correctly relating and comparing corresponding regions of interest is challenging. In addition, occlusion may prevent effective visualization of these regions of interest. To address these problems, we have developed a method that fully automatically maps the data to a standardized layout of sub-volumes, based on an articulated atlas registration. We have dubbed this process articulated planar reformation, or APR. A sub-volume can be interactively selected for closer inspection and can be compared with the corresponding sub-volume at the other timepoints, employing a number of different comparative visualization approaches. We provide an additional tool that highlights possibly interesting areas based on the change of bone density between timepoints. Furthermore we allow visualization of the local registration error, to give an indication of the accuracy of the registration. We have evaluated our approach on a case that exhibits cancer-induced bone resorption.