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Full Body Virtual Autopsies Using A State-of-the-art Volume Rendering Pipeline
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9288-5322
Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9446-6981
Linköping University, Department of Science and Technology, Visual Information Technology and Applications (VITA). Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9368-0177
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2006 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 12, no 5, 869-876 p.Article in journal (Other academic) Published
Abstract [en]

This paper presents a procedure for virtual autopsies based on interactive 3D visualizations of large scale, high resolutiondata from CT-scans of human cadavers. The procedure is described using examples from forensic medicine and the added valueand future potential of virtual autopsies is shown from a medical and forensic perspective. Based on the technical demands ofthe procedure state-of-the-art volume rendering techniques are applied and refined to enable real-time, full body virtual autopsiesinvolving gigabyte sized data on standard GPUs. The techniques applied include transfer function based data reduction using levelof-detail selection and multi-resolution rendering techniques. The paper also describes a data management component for large,out-of-core data sets and an extension to the GPU-based raycaster for efficient dual TF rendering. Detailed benchmarks of thepipeline are presented using data sets from forensic cases.

Place, publisher, year, edition, pages
2006. Vol. 12, no 5, 869-876 p.
Keyword [en]
Forensics, autopsies, medical visualization, volume rendering, large scale data
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-13950DOI: 10.1109/TVCG.2006.146ISI: 000241383300028OAI: oai:DiVA.org:liu-13950DiVA: diva2:22280
Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2017-12-13
In thesis
1. Efficient Methods for Direct Volume Rendering of Large Data Sets
Open this publication in new window or tab >>Efficient Methods for Direct Volume Rendering of Large Data Sets
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Direct Volume Rendering (DVR) is a technique for creating images directly from a representation of a function defined over a three-dimensional domain. The technique has many application fields, such as scientific visualization and medical imaging. A striking property of the data sets produced within these fields is their ever increasing size and complexity. Despite the advancements of computing resources these data sets seem to grow at even faster rates causing severe bottlenecks in terms of data transfer bandwidths, memory capacity and processing requirements in the rendering pipeline.

This thesis focuses on efficient methods for DVR of large data sets. At the core of the work lies a level-of-detail scheme that reduces the amount of data to process and handle, while optimizing the level-of-detail selection so that high visual quality is maintained. A set of techniques for domain knowledge encoding which significantly improves assessment and prediction of visual significance for blocks in a volume are introduced. A complete pipeline for DVR is presented that uses the data reduction achieved by the level-of-detail selection to minimize the data requirements in all stages. This leads to reduction of disk I/O as well as host and graphics memory. The data reduction is also exploited to improve the rendering performance in graphics hardware, employing adaptive sampling both within the volume and within the rendered image.

The developed techniques have been applied in particular to medical visualization of large data sets on commodity desktop computers using consumer graphics processors. The specific application of virtual autopsies has received much interest, and several developed data classification schemes and rendering techniques have been motivated by this application. The results are, however, general and applicable in many fields and significant performance and quality improvements over previous techniques are shown.

Place, publisher, year, edition, pages
Institutionen för teknik och naturvetenskap, 2006
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1043
Keyword
Computer Graphics, Scientific Visualization, Medical Imaging, Volume Rendering, Raycasting, Transfer Functions, Level-of-detail, Fuzzy Classification, Virtual Autopsies
National Category
Computer Science
Identifiers
urn:nbn:se:liu:diva-7232 (URN)91-85523-05-4 (ISBN)
Public defence
2006-10-06, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 09:15 (English)
Opponent
Supervisors
Note
On the defence date the status of article IX was Accepted.Available from: 2006-09-14 Created: 2006-09-14 Last updated: 2015-09-22
2. Efficient Medical Volume Visualization: An Approach Based on Domain Knowledge
Open this publication in new window or tab >>Efficient Medical Volume Visualization: An Approach Based on Domain Knowledge
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Direct Volume Rendering (DVR) is a visualization technique that has proved to be a very powerful tool in many scientific visualization applications. Diagnostic medical imaging is one domain where DVR could provide clear benefits in terms of unprecedented possibilities for analysis of complex cases and highly efficient work flow for certain routine examinations. The full potential of DVR in the clinical environment has not been reached, however, primarily due to limitations in conventional DVR methods and tools.

This thesis presents methods addressing four major challenges for DVR in clinical use. The foundation of all methods is to incorporate the domain knowledge of the medical professional in the technical solutions. The first challenge is the very large data sets routinely produced in medical imaging today. To this end a multiresolution DVR pipeline is proposed, which dynamically prioritizes data according to the actual impact in the rendered image to be reviewed. Using this prioritization the system can reduce the data requirements throughout the pipeline and provide high performance and visual quality in any environment.

Another problem addressed is how to achieve simple yet powerful interactive tissue classification in DVR. The methods presented define additional attributes that effectively captures readily available medical knowledge. The task of tissue detection is also important to solve in order to improve efficiency and consistency of diagnostic image review. Histogram-based techniques that exploit spatial relations in the data to achieve accurate and robust tissue detection are presented in this thesis.

The final challenge is uncertainty visualization, which is very pertinent in clinical work for patient safety reasons. An animation method has been developed that automatically conveys feasible alternative renderings. The basis of this method is a probabilistic interpretation of the visualization parameters.

Several clinically relevant evaluations of the developed techniques have been performed demonstrating their usefulness. Although there is a clear focus on DVR and medical imaging, most of the methods provide similar benefits also for other visualization techniques and application domains.

Place, publisher, year, edition, pages
Institutionen för teknik och naturvetenskap, 2007. 55 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1125
Keyword
Scientific Visualization, Medical Imaging, Computer Graphics, Volume Rendering, Transfer Function, Level-of-detail, Fuzzy Classification, Uncertainty Visualization, Virtual Autopsies
National Category
Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:liu:diva-9561 (URN)978-91-85831-10-4 (ISBN)
Public defence
2007-09-14, Berzeliussalen, Hälsouniversitetet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2007-08-24 Created: 2007-08-24 Last updated: 2015-09-22

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Ljung, PatricPersson, AndersLundström, ClaesYnnerman, Anders

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