medical applications of multi-eld volume rendering and vr...

Joint EUROGRAPHICS - IEEE TCVG Symposium on Visualization (2004)O. Deussen, C. Hansen, D.A. Keim, D. Saupe (Editors)

Medical Applications of Multi-field Volume Rendering andVR Techniques

Joe Kniss1 Jürgen P. Schulze2 Uwe Wössner2 Peter Winkler3 Ulrich Lang4 Charles Hansen1

1Scientific Computing and Imaging Institute, University of Utah2High Performance Computing Center, Stuttgart

3Radiological Institute, Olgahospital Stuttgart4Center for Applied Informatics, University of Cologne

Abstract

This paper reports on a new approach for visualizing multi-field MRI or CT datasets in an immersive environmentwith medical applications. Multi-field datasets combine multiple scanning modalities into a single 3D, multi-valued, dataset. In our approach, they are classified and rendered using real-time hardware accelerated volumerendering, and displayed in a hybrid work environment, consisting of a dual power wall and a desktop PC. Forpractical reasons in this environment, the design and use of the transfer functions is subdivided into two steps,classification and exploration. The classification step is done at the desktop, taking advantage of the 2D mouse asa high accuracy input device. The exploration process takes place on the powerwall. We present our new approach,describe the underlying implementation issues, report on our experiences with different immersive environments,and suggest ways it can be used for collaborative medical diagnosis and treatment planning.

Categories and Subject Descriptors (according to ACM CCS): H.5.1 [Multimedia Information Systems]: Artificial,Augmented, and Virtual Realities; I.4.10 [Image Representation]: Multidimensional; I.4.10 [Image Representa-tion]: Volumetric; J.3 [Life and Medical Sciences]: Medical Information Systems.

1. IntroductionDirect volume rendering is an important and flexible tech-nique for visualizing 3D volumetric data. This technique hasbeen used with great success in medical imaging applica-tions, especially diagnosis [NT01, TKHS03, HWC∗03] andtreatment planning [LFP∗90]. The success of this techniqueand its resulting popularity can be attributed to several fac-tors. First, volume rendering does not require an intermedi-ate representation of the data for image generation, permit-ting interactive feature extraction with immediate feedback.Second, because the optical model used for volume render-ing allows features to be rendered with any degree of trans-parency, it can naturally provide context without obscur-ing features of interest by simultaneously rendering semi-transparent physical or anatomical landmarks. Finally, theuse of a transfer function, converting data into renderableoptical properties, allows one to classify and visualize fea-tures that may not be captured using other traditional tech-niques such as iso-surface extraction. In particular, multi-

dimensional transfer functions allow features to be classifiedbased on a unique combination of data values, which helpsto disambiguate distinct features that may share datavalueswith other, unimportant, features.

Immersive visualization using stereoscopic displays withhead and hand tracking can enhance visualization and dataanalysis by providing the user with a truly three dimen-sional view of the dataset. This is especially applicable todirect volume rendering since multiple overlapping semi-transparent features may lead to perceptual ambiguities inshape and depth. An immersive environment addresses theseproblems in several ways. Stereopsis and motion parallaxhelp the user resolve spatial feature placement using nat-ural and precognitive abilities of the human visual sys-tem. The coupling of head and hand tracking with di-rect manipulation of the visualization allows the user togain knowledge of the relative placement and scale offeatures within the dataset. These techniques are impor-tant for medical imaging, diagnosis, and treatment plan-

c© The Eurographics Association 2004.

Kniss, Schulze, Wössner, Winkler, Lang, Hansen / Multi-field Volume Rendering and VR Techniques

ning [Sht92, SR97, RC94, GELP∗96] as high resolution 3Dscans become increasingly more prevalent.

The goal of this work is to design a system for volume ren-dering in virtual reality that allows the user to freely exploredata without being burdened by the traditional ergonomicproblems of VR or the difficulties of manipulating a highdimensional transfer function. Such an environment is par-ticularly applicable for collaborative work where domain ex-perts interact with each other and with visualization experts.One such application is diagnosis of tumors through the useof multiple imaging datasets. The immersive environmentwith its high spatial acuity allows for collaborative surgicalplanning among multiple domain experts.

However, the manipulation of a desktop interface formulti-dimensional transfer functions is cumbersome in animmersive environment. To address this, we divide the spec-ification of multi-dimensional transfer functions into two in-dependent tasks, which we term classification and explo-ration, and describe their unique interface characteristics.

In the next section, Section 2, we describe our physicalVR environment and identify several key design choices.In Section 3 we describe a novel interface for manipulat-ing high dimensional transfer functions in VR. In Section 4we describe implementational details of our interface and thehardware that it involves. Section 5 compares our suggestedimmersive environment to the CAVE, and shows how it canbe used in a clinical environment. Finally, we conclude withresults and future work.

2. A Collaborative Immersive EnvironmentOur immersive volume visualization system was developedbased on three primary design criteria. First, successful vi-sualizations are typically accomplished via collaboration be-tween one or more domain and visualization experts. Vi-sualization systems can be complicated and require exten-sive knowledge of both the hardware and software that com-prise them. This is especially true for immersive environ-ments and modern hardware assisted volume rendering tech-niques. Software and hardware resources must be carefullymanaged in order to maintain a high level of interactivity.Second, user interfaces must be carefully designed to meetthe needs of the user. This issue is particularly relevant whenwe consider the difficult task of transfer function design. Fi-nally, ergonomic factors are an important issue for immer-sive environments. Since the primary mode of user inter-action is through head and hand tracked input, the user istypically encouraged to stand and move about the environ-ment using hand gestures to manipulate the visualization.This is in dramatic contrast to desktop configurations wherethe user views the visualization in a comfortable sitting po-sition and interacts with it via minimal hand motions usinga 2D mouse. Fatigue from standing and pointing (gesturing)for long periods can discourage the user from spending thetime to thoroughly investigate, and thus gain maximal bene-fit from, the visualization session.

Our system is designed to accommodate multiple userseither sitting or standing using two large stereoscopic dis-plays and a traditional desktop display. Figure 1 illustratesthe configuration. Two side by side vertical displays (A andB) are each configured using two projectors with comple-mentary polarization filters for passive stereo. A traditionaldesktop display (C) is located in front of one of the large ver-tical displays. The left display (A) is setup to provide headtracked 3D stereoscopic imagery. The right display (B) canbe configured as an extension of the left display making the3D environment larger, or as a separate 3D display providingdifferent views of the data and interaction tools. It can alsobe configured as a 2D display replicating the desktop’s out-put for an audience. The entire workspace has 3D positionand orientation tracking.

StandingSittin

g

Dual Projectors: Left & Right eyes

C) Desktop: 2D display

A) 3D displayB) 3D/2D display

Figure 1: Immersive volume rendering workspace. The topillustration shows the workspace configuration. The bottomimage shows standing interaction using the immersive 3Ddisplay.

This workspace allows users to interact with the visual-ization using the most appropriate modality. In a typical ses-sion, a visualization expert drives the visualization and pro-vides assistance using the 2D display. The domain expertis allowed to freely explore and interact with the visualiza-tion on any of the three displays. We found that interactingwith the virtual environment while sitting not only reducedfatigue but also improved the accuracy of interactions withimmersive tools and visualization. The preferred posture us-ing the 3D mouse had one’s elbow resting firmly on the tableproviding additional stability for fine movements.

3. Immersive Volume RenderingThe use of multi-dimensional transfer functions for vol-ume rendering applications has been shown to dramati-



cally improve one’s ability to classify features of inter-est in volume data [KKHar]. The visualization of nearlyall datasets can benefit from multi-dimensional transferfunctions, even scalar datasets. Unfortunately, manipulat-ing high-dimensional transfer functions is difficult and has asteep learning curve. In our experiments, we found that thisdifficulty is further compounded in an immersive environ-ment due to low accuracy in one’s ability to select relativelysmall control points and make fine movements with 3D in-put devices. Although recent studies demonstrate that im-proved accuracy for some interactions can be accomplishedby mapping small movements to wrist rotations[SWWL01],we have found the design of good transfer functions in animmersive environment to be a tedious and time consumingtask.

Our solution to this problem stems from the observationthat the role of transfer function design for volume renderingis essentially two independent tasks, classification and op-tical property specification. The classification step involvesidentifying the regions of the data domain, or feature space,that correspond to unique materials or material boundaries.Once these regions have been determined, all that remainsfor the user to do is assign color and opacity, making theclassified materials corresponding to features of interest visi-ble and unimportant materials transparent. With this in mind,our system is designed with two distinct interfaces for trans-fer function design.

3.1. ClassificationThe classification interface, seen in Figure I A (see color sec-tion for Roman numbered figures), is designed primarily forthe visualization expert. It is most similar to that proposedin [KKHar], with the addition of an interface for assigninga name to each classified feature. Initially, classification iscarried out as a preprocessing step prior to the visualizationsession using a 2D desktop configuration. The visualizationexpert attempts to classify any and all relevant features usinga variety of tools such as dual-domain interaction and jointhistogram analysis. Once features have been classified andnamed, the visualization expert can specify the initial op-tical properties and save the classification specification forlater use.

A classification interface, seen in Figure I B, has also beendeveloped for use in the immersive 3D display. The intent ofthis interface is to allow the user immediate access to clas-sification parameters for refinement during the visualizationsession. As noted earlier, direct manipulation of classifica-tion elements may not be appropriate when using 3D inputdevices. To address this, we also provide the user with a setof rotary knobs for manipulating each degree of freedom lin-early and independently. An example of the immersive clas-sification tool can be seen in Figure I B.

3.2. Optical PropertiesDuring the visualization session, the saved classification isloaded with the dataset and the domain expert is presented

with the initial optical properties specified in the classifica-tion step. Rather than being presented with the complicatedclassification interface, the user is provided with a simpli-fied material mixer, seen in Figure I C, which allows one toadjust the optical properties associated with each classifiedfeature. Each classified feature is identified by name. Thefeature’s opacity is specified by rotating a knob widget, itscolor is set using a standard color picker tool.

The main advantage of this interface is that the user is notburdened, or worse distracted, by the complicated and ab-stract nature of the high dimensional feature space in whichmaterials are classified. Rather, users are provided with therelevant degrees of freedom, namely; what feature is beingmanipulated, how opaque it is, and its color.

3.3. CollaborationWhile it is expected that the visualization expert carry outthe classification step, it must often be done with the guid-ance of the domain expert. It is frequently the case that fea-ture classification must be refined during the visualizationsession. The visualization expert can choose to manipulatethe transfer function using either the 2D desktop interface orassist the domain expert in the immersive environment.

4. Implementation4.1. HardwareOur hybrid work environment consists of two rear projectedpassive stereo displays, and a table in front of them. A videoswitching unit is used to select which PC’s output is usedfor each projector and desktop display. Our software alsoworks in CAVE-like environments [CNSD93] like the fourscreen CUBE at HLRS. Both environments are driven byCOTS PCs with NV25 based NVidia graphics cards.

4.2. SoftwareOur immersive volume rendering application is built on topof several existing visualization and scientific computingframeworks, namely OpenGL, OpenGL Performer, COVER,COVISE, Simian, and using Linux as the operating system.Figure 2 illustrates conceptually how these software compo-nents interact.

CPU & NetworkG r a p h i c s 3 D Tr a c k i n g

OpenGL

Performer

C o v e rS i m i a n

C o v i s e

Immers ive Volume Render ing

Figure 2: Software framework for immersive volume render-ing.

Simian [KKH01] is a volume rendering tool designed to



support multi-field volume rendering. It has been developedat the University of Utah. The entire tool is used for clas-sification at the desktop, and its rendering code is extractedfor use in the virtual environments, which run with COVISE.The original Simian software lacked the capability of pass-ing the transfer function widgets on to other programs. Weadded the option to write the widget parameters to a file.

COVISE is a visualization and scientific com-puting framework developed at the University ofStuttgart [RLR96]. Its virtual reality renderer COVER[RFL∗98] is a standalone, OpenGL Performer basedprogram that supports arbitrary virtual environments andinput devices. The user interface software used in the virtualenvironments is based on the volume rendering applicationpresented in [SWWL01] and [WSWL02]. Performer sup-ports parallel application, culling, and draw processes onmultiprocessor machines. For the integration of Simian, theuser interface and rendering routines had to be separatedand integrated into the application and draw processes,respectively.

4.2.1. Material MixerThe material mixer (see Figure I C) is made up entirely ofstandard COVER menu items, in this case labels for materialnames and rotary knobs for the opacity. The value range ofthe knobs is from “fully transparent” to “fully opaque”.

4.2.2. VR Classification InterfaceThe classification interface (see Figure I B) consists ofseveral groups of elements. The main rectangular regionshows the histogram. Here the transfer function widgetsare located, similar to the Simian transfer function editor[KKH01]. They can be moved by pointing and clicking atthem with the 3D wand. Widgets are created and deletedwith the green icons at the left of the window. The “Hist”button toggles the display of the histogram. The color diskand the “Bright” knob define the color of the active widget.The rotary knobs at the bottom of the window change a wid-get’s geometry and opacity. All the widgets are listed at theright edge of the window.

5. Results5.1. Immersive ComparisonAs previously stated, we have implemented this system forboth a CAVE immersive environment and a power wall im-mersive environment. A comparison of the two immersiveenvironments for collaborative exploration tasks involvingmedical diagnosis and surgical planning (described in thenext sub-sections) was useful. While the CAVE environmentis an attractive option for many immersive visualization andgraphics applications, we found it difficult to meet our sys-tem design goals in this workspace. The CAVE is designedto function primarily as a single user environment. Becauseof limited space and the use of all four displays as a singleintegrated view of the visualization, close collaboration be-tween the domain and visualization experts is difficult. We

cannot place a desk or use chairs inside this environmentsince it would interfere with the immersion gestalt, furtherlimit the available space, and potentially damage the deli-cate projection surfaces. We considered having the visualiza-tion expert assist in this environment using a laptop PC withwireless networking. Although this allowed the visualizationexpert to participate in the session from within the environ-ment, its use was quite limited. Since the laptop’s user hasto carry the unit, it is difficult to make fine adjustments inthe classification interface, and the user quickly becomes fa-tigued. Thus, the power wall environment was better suitedto our applications.

5.2. A Multi-spectral MRI Case Study: PreliminaryResults

We are currently investigating the use of our immersivevolume rendering system for medical diagnosis and sur-gical planning in a collaboration with the Olgahospital inStuttgart. The Olgahospital is a childrens’ hospital where anumber of patients are treated for seizures caused by cor-tical neurons. In patients with intractable epilepsy, the de-tection of lesions in areas of interest can help to decidewhether surgery to remove the lesions has a chance to stopthe seizures without creating significant damage. The detec-tion of these lesions is typically done using MRI. Unfortu-nately the lesions are often difficult to identify in these scansbecause they are characterized by subtle differences in con-trast, thickness, and sharpness of the border between whiteand gray matter. Today, these lesions are diagnosed usingseveral high resolution and high contrast MRI sequences thatare visually inspected using software designed to deal withthe acquired data on a slice by slice basis.

The focus of this study is two fold. First, we intend todemonstrate the effectiveness of multi-modal MRI data clas-sification using multi-dimensional transfer functions. Ourhope is that the tissue characteristics captured by differentMRI scanning sequences can be combined to better identifytissue types and lesions. Second, we intend to identify theways in which our immersive visualization system can assistin diagnosis and treatment planning. By providing a collabo-rative environment that allows multiple physicians and visu-alization experts to gain spatial awareness of the anatomicalfeatures, surgery can be planned as a collaborative process.

5.2.1. RegistrationFigure II shows an example of three MRI scan modalitiesused in traditional diagnosis and this study. The bottom rightimage is a multi-modal visualization, created by assigningeach modality to a color channel. In many cases, these scansare acquired at different resolutions and times. Thus, an ini-tial co-registration pre-processing step is required. There areseveral approaches for intermodal data registration rangingfrom completely manual to fully automatic. There are twowidely available and free registration tools Automatic ImageRegistration (AIR) [AIR] and Statistical Parametric Map-ping (SPM) [SPM]. A comparison of these packages can be



found in [KAPF97]. Although these tools are automatic, wediscovered that substantial manual registration was required,and that AIR performed best when the datasets were regis-tered to within 2 voxels and 3 degrees of rotation along anyaxis.

5.2.2. ClassificationOnce the registration parameters have been determined, thescans are resampled and combined into a single multi-valued dataset. In addition to the scan intensities, weadd multi-gradient magnitude measure, which is discussedin [Sap97, KKHar]. Classification is performed manually,guided primarily by joint histogram analysis. Figure 3 showsan example of joint histograms of co-registered proton den-sity (PD) and T2 MRI scans. Figure 3 A, left, shows howconsidering the unique combinations of datavalues in thesescans, using a joint histogram, can help identify featuresmore clearly than the 1D histograms of each dataset, seen atthe left and top. Figure 3 B, right, shows how the exclusionof high multi-gradient magnitudes can further disambiguatehomogeneous materials, i.e., B shows a joint histogram ofvalues representing relatively homogeneous materials. Thelabeled materials are: a cerebro-spinal fluid, b gray matter,c white matter, d fat, e background, f bone marrow. Con-versely, a joint histogram of values with high multi-gradientmagnitude allows us to identify boundaries between materi-als or material mixtures.

PD

T2 A B a

b

c

d

ef

Figure 3: Example joint-histogram of T2 and Proton Den-sity (PD) MRI scans. A shows the log-scale joint histogramwith the corresponding scalar histograms for each scan seenat the left and top. B shows a joint histogram created by ex-cluding values with high multi-gradient magnitudes. The la-beled materials are: a cerebro-spinal fluid, b gray matter, cwhite matter, d fat, e background, f bone marrow.

The desktop classification interface, described in Sec-tion 3.1, allows the visualization expert to use the mouseand place classification widgets at all locations in the 2Dhistogram where materials have been identified.

For the dataset used in Figure 3 B the classification pro-cess is trivial for materials a, d, e, and f, but it requires carefulrefinement and some experience to correctly distinguish ma-terials b and c (gray and white matter). The transfer function

widgets can optionally be assigned names for the materialsthey represent.

Note that in the classification step the domain expert doesnot need to be present. This is important because the domainexpert, typically a radiologist or physician, should not haveto spend time dealing with technical details that do not re-quire his expertise.

5.2.3. ExplorationA complete classification is often composed of a dozenor more individual classified features, which can make theclassification interface complicated and difficult to manip-ulate. This emphasizes the need for the simplified materialmixer interface. Our initial results suggest that our classi-fication/exploration approach is appropriate for this type ofmedical data and the immersive visualization can assist inunderstanding the spatial relationships among important 3Dstructures.

During exploration, the domain expert uses the materialmixer and the color picker to change the parameters of thepreviously defined materials. Depending on how well the vi-sualization expert was able to set the transfer function, thiswill be all the domain expert needs to work with. In ambigu-ous cases, e.g., the differentiation of gray and white matterin Figure 3 B, the complex transfer function editor in the vir-tual environment allows the domain expert to further refinethe transfer function parameters, which he might be able todo better than the visualization expert, given his greater do-main knowledge.

In contrast to the classification step, the focus of the ex-ploration is not on the definition of the transfer functions, butrather on finding spatial features in the dataset that may leadto the diagnosis of a patient’s illness. For this purpose, theuser can rotate the dataset, zoom in on arbitrary regions, useclipping planes, or look at the dataset from different anglesjust by moving the head. All of this having real-time visualfeedback, including any changes of the transfer functions,provides doctors a novel method to work with their MRI andCT datasets.

6. ConclusionThis paper presents a new immersive visualizationworkspace layout that emphasizes tight collaboration be-tween domain and visualization experts. We achieve this byproviding a workspace that combines traditional desktop andimmersive modes of interaction, emphasizing comfort andergonomics.

We describe a novel interface for volume rendering multi-field volume datasets in immersive environments. For appli-cation areas like medical data, in which the datasets consistof a combination of several distinct materials, we advocate atwo step approach to transfer function design, classificationand optical property specification, which can significantlyincrease the usability of the system for doctors.

The combination of the proposed workspace and user in-terface designs has demonstrated usefulness in applications



like the analysis of both scalar and multi-field volume datafrom MRI or CT scanners. Because transfer function designis divided into a classification, which benefits from the fea-tures of the desktop PC, and an exploration phase, whichtakes advantage of the virtual environment, the ability torapidly switch between the tasks and platforms allows usersto more efficiently achieve their visualization goals.

7. Future WorkWe intend to continue our multi-spectral MRI collabora-tion. The preliminary results suggest several ways our sys-tem could be improved. The co-registration step is tediousand time consuming in our current visualization pipeline.This is due to the fact that datasets must be relatively wellcoregistered before automatic methods succeed. As such, weare developing interactive and immersive tools to assist withthe initial registration step. While manual histogram analy-sis aids in material classification, automating this step us-ing statistical methods and segmentation would improve thequality of classification. Suggested exploration and interac-tion tool improvements include interactive local histogramanalysis that allows the user to investigate values in a sub-set of the data, and an interactive manual segmentation toolthat allows the user to mark or mask off localized features ofinterest so they can be visualized in isolation.

One frustrating aspect of our classification interface, forboth the desktop and immersive versions, is that we can onlyvisualize the feature space as 2D projections. We are inves-tigating an immersive interface that permits classification in3D, that is, the system allows users to refine the classifica-tion using three axes of the transfer function at once, ratherthan just two.

8. AcknowledgmentsThis work was funded in part by the Department of EnergyVIEWS program, the DOE Computation Science Fellowshipprogram, and the collaborative research centers (SFB) 374and 382 of the German Research Council (DFG). We alsoacknowledge the Teem Toolkit (teem.sourceforge.net).

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Figure I: Dual transfer function specification interfaces.The left images (A and B) show the classification in-terfaces. A is is the interface used for classification onthe desktop, B is the extended interface for classificationin an immersive environment. C shows the the materialmixer.

Figure II: Scans with different modalities. Top left: FluidAttenuation Inversion Recovery (FLAIR), top right: T2,bottom left: PD. At the bottom right is a multi-modal vi-sualization, created by assigning each modality to a colorchannel.

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