Immersive Vis and 3D Interaction for Volume Data Analysis

Visualization of volume data is critical in a variety of application domains, such as medicine, geophysical exploration, and biomechanics. For effective analysis of a 3D volume, scientists and other users need to integrate various views and to peer inside the volume. However, despite many advances in volume rendering algorithms, neither traditional displays nor traditional interaction techniques are sufficient for efficient and accurate analysis of complex volume datasets.

We are studying the use of displays with higher levels of fidelity, as well as natural and innovative 3D interaction techniques, to allow scientists to view and analyze volume datasets easily without prior segmentation. Based on our findings, we plan to design, prototype, and evaluate a next-generation interactive volume data analysis system with best-practice display characteristics and interaction techniques.

Support for this project is provided by the National Science Foundation under a collaborative award between Virginia Tech and Brown University (NSF award numbers 1320046 and 1319606.


DISPLAY FIDELITY STUDIES

Our prior studies shown benefits of immersive displays with high fidelity in the visual analysis of volume data. Those studies are described in the project Immersive Volume Data Visualization and the associated publications at IEEE VR 2012 and IEEE VR 2013.
 
In this project our goal is to understand how the different components of display fidelity affect the analysis of volume datasets. In the experiments we use
datasets generated from synchrotron microscopic computed tomography (micro-CT) scans of beetles. The tasks were based on general tasks from the biomechanical domain, classified by our proposed taxonomy (see below). The study was divided into two phases.
 

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Isosurfaces of the datasets used in the study. Left: Pterostichus, Right: Platynus genus

 
Phase I
 
In the first phase we focused on the effect of Field of Regard (FOR), Stereoscopy (ST), and Head Tracking (HT) on task performance. The goal
was to gather evidence to support or reject the following hyphothesis.
 
Hypothesis 1 - Levels of FOR, Stereo and HT affect performance in different ways across task categories.
Hypothesis 2 - Combinations with higher fidelity levels would benefit task performance 
 
We completed a major experiment (72 participants) evaluating the effects of VR system fidelity on the analysis of isosurface-rendered volume datasets. We visualized the isosurfaces in a CAVE-like display. We ran a controlled experiment evaluating the effects of three components of system fidelity (field of regard, stereoscopy, and head tracking) on a variety of abstract task categories that are applicable to various scientific domains, and also compared our results with those from previous studies.  We report many significant findings.
 

Findings:

  • —Higher levels of system fidelity improved task performance
  • Stereo had the strongest effects on search and spatial judgment

    • FOR improved performance in 2 spatial judgment tasks
    • HT improved confidence in a search task
  • Compared significant findings with those from VR-STUDY-I

    • Effects of system fidelity may depend on volume rendering technique

For example, for search and spatial judgment tasks with isosurface visualization, a stereoscopic display provides better performance, but for tasks with 3D texture based rendering, displays with higher field of regard were more effective, independent of the levels of the other display components. We also found that systems with high field of regard and head tracking improve performance in spatial judgment tasks. Our results extend existing knowledge and produce new guidelines for designing VR systems to improve the effectiveness of volume data analysis.

 

Phase II

Our former experiment (phase one) evaluated the effect of different virtual reality display characteristics on tasks involving isosurface datasets. In the Phase-II we looked more closely into the effect of FOR and ST across different display platforms. For that we performed a multi-site VR fidelity component experiment with 60 participants in collaboration with Brown University. We used the phase one experimental design and the task taxonomy introduced in our 2015 Vis publication. Virginia Tech  conducted the experiment in a 4 wall CAVE and in a HMD, while Brown performed the same experiment in another similar CAVE.

Our results confirm the high-level results of our previous study. Higher fidelity settings for both, field of regard and head tracking significantly improve the performance of users in complex spatial data exploration tasks. However, we found differences in individual task performances between platforms. Additionally they informed us about the platform dependent differences that warrant further experiments. A publication summarizing our finding is currently in preparation.

 

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Display platforms used in the Phase II of the study.
Left: The CAVE at Brown University. Center: Nvis SX111 HMD. Right: Virginia Tech Visionarium Viscube.

 

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Pterostichus dataset running on the Yurt.

 

High Fidelity Immersive Visualization 
 

The YURT is a high fidelity VR display room at Brown University that uses 69 full HD projectors and 145 mirrors to create a 360 degree curved surface with pixels smaller than the human retina can perceive. As part of this project we performed several iterations of calibration procedures to allow the use of its unique display capabilities in upcoming studies. As first experiment in the YURT environment we have performed a case study evaluating the potential for interactively identifying placental blood vessels using MRI in large-scale virtual reality environment. This collaboration with the Rhode Island Hospital (RIH) aims to improve the planning stage of twin-to-twin transfusion syndrome intervention surgery, a potentially fatal vascular condition in twin pregnancies. Treatment of the condition involves endoscopically severing connected placental blood vessels and current clinical practice does not include a planning stage to locate these vessels.

Our results show that medical professionals are able to identify relevant vessels within our immersive visualization system and effectively utilize the larger range of motion allowed in virtual reality, in contrast to the limited views provided by traditional endoscopes. Our medical professional collaborator and participants believe that by providing surgeons with a way to find these vessel connections before beginning surgery, we might enable them to improve the outcomes. The outcome of our experiment can therefore be seen as proof of concept for a new application area of immersive VR environments.

 

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Blood vessels visualization on the Yurt.

Current recomendations

The display fidelity study has already provided some insights on how to improve task performance for volume data analysis on immersive VR systems. 

  1. For analysis of isosurface rendering, stereoscopic displays can be very effective (particularly for search and spatial judgment tasks), while for analysis of volume visualization based on 3D texture, systems with high FOR are more effective, independent of the fidelity of other components of the VR system.

  2. When analyzing isosurface rendering, higher levels of fidelity based on FOR, ST and HT can improve analysis speed in a variety of tasks.

  3. We recommend VR systems with both FOR and HT at higher levels for tasks that require spatial judgment in volumes. 

 


3D INTERACTION TECHNIQUE DESIGN

Our prior work on the Volume Cracker - a Bimanual Free-Hand 3D interaction technique to iteratively crack Raw Volumetric Data - inspires our current work. The original Volume Cracker was published at ACM SUI 2013

The original Volume Cracker design was limited due to cumbersome and expensive hardware and the inability to interact with real volume data. New input devices such as the Leap Motion Controller enable 6-DOF tracking of bare hands and fingers, inspiring us to design a new version of the Volume Cracker addressing its prior limitations. We have iteratively designed and developed a prototype of the Bare-Hand Volume Cracker that works directly on real volume datasets (see video below). A study comparing the bare-hand VC with bare-hand versions of the axis-aligned slicing and arbitary 3D slicing techniques is complete and results are being prepared for publication.

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The bimanual interface of the bare hand volume cracker

We are currently working on designing new techniques and applying the Volume Cracker on immersive displays. To be able to evaluate our designs we need to support arbitrary manipulation of the volume dataset at interactive rates. Since immersive rendering can also be quite demanding (low latency and stereo) we are developing a framework to support voxel manipulation and stereo rendering using GPU programming.  This will allow the us to combine the findings of these two areas to support the analysis of volume datasets with more immersive metaphors.

 

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The GPU volume renderer

 

Heading towards our goal of designing a next generation volume analysis system, we have proceeded with the design of 3D interaction techniques for biomedical volume manipulation. In collaboration with domain scientists we have designed Capsule, an inertial input device to improve the scientist’s workflow during the analysis of 3D biological data such as proteins, CT scans or neuron fibers. The hand-held controller provides intuitive interaction methods for 3D orientation, selection and interaction tasks. We have used the first prototype to gather feedback on the design in the domains of molecular biology and biomechanics. We have also investigated the use of the device in collaborative 3D manipulation tasks. This new technique uses Capsule along with a 3D camera to support synchronous and asynchronous collaboration. It was presented at 2016 IEEE 3DUI, in Greenville as an entry to the 3DUI contest. From these findings we intend to produce a second improved prototype and conduct an evaluation  within immersive environments.

 

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The capsule interface device
 
 

TASK TAXONOMY

We have surveyed a broad community of international researchers working with volume data. This survey has revealed a number of interesting findings about the tasks performed in volume data analysis across a wide variety of domains. Based on the results of this survey, we have drafted a proposed task taxonomy for volume data analysis. The taxonomy lists generalized tasks such as search, pattern recognition, spatial understanding, quantitative estimation, and shape description. Some of the tasks also have sub-tasks (e.g., quantitiative estimation tasks can be absolute or relative). The taxonomy also lists supporting tasks (interactions performed during volume data analysis in service of the primary tasks, such as segmentation, navigation, or path following), and dataset properties. The taxonomy makes it possible to generalize the findings of experiments and to transition successful interaction technniques and strategies from one domain to another.

The taxonomy currently classifies volume data analysis tasks as follows:

  1. Search: We define a search task as one in which the user is searching for a feature in the dataset.

    • Searching for presence/non-presence of a particular feature/object in a dataset. e.g., “Search for distinct bone segments in this sample.”
    • Counting objects/features, given they are present. e.g., “Count the total number of tracheal tubes and categorize them by diameter.”
  2. Pattern Recognition: We define a pattern as a characteristic that is repeated or a trend that we see as we look through the dataset.

    • Looking for a trend. e.g., “Blood vessels on one side of the dataset are skinny and they get progressively bigger as we go through the dataset. Identify on which side of the dataset we notice this trend.”
    • Does a pattern repeat itself in different parts? How many times? e.g., “Looking through the sample longitudinally, count the number of fibers curving through the sample, and the number of straight fibers.”
  3. Spatial Understanding: These are tasks where the user makes a judgment in a 3D spatial context about the position and/or orientation of a feature on an absolute or relative sense. e.g., “Which sets of tubes are located closest to the front of the dataset?”

    • Absolute - Locating a feature/object which is the highest, lowest, farthest, or closest to/from a viewpoint, or in the overall dataset. e.g., “Which sets of tubes are located closest to the front of the dataset?”
    • Relative - Judging whether a feature/object ‘A’ is higher/lower/farther/closer than another feature/object ‘B’. e.g., “Which of the twisting fibers is the closest to the center of the bundle?”
    • Judging whether two features/objects intersect or if they are distinct. e.g., “Does the left side of the tracheal system connect to the right side, throughout the body?”
  4. Quantitative Estimation: These include tasks in which the user has to estimate some property of the dataset, or part of it, quantitatively. Examples of things we can estimate in a volume dataset are density, size, volume, distance, length, surface-area, curvature, angle, velocity and other super-scalar quantities, etc.

    • Absolute estimation. e.g., “What is the density of the highlighted region of the bone marrow?”
    • Relative estimation (binary). e.g., “Study the thickness of the borders and the joints between the cells. Are different joints and borders of comparable or variable thickness?”
    • Relative estimation (quantitative). e.g., “How many times denser is the boundary of the cells at the center than the outer boundary of the cluster?”
  5. Shape Description: These include tasks where the user describes the shape of either the whole or some part of the dataset qualitatively. e.g., “What is the cross-sectional shape of each tracheal tube?"

 

Publications

Sponsors