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AR2T: Advanced Realistic Rendering Technique for Biomedical Volumes

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14225))

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Abstract

Three-dimensional (3D) rendering of biomedical volumes can be used to illustrate the diagnosis to patients, train inexperienced clinicians, or facilitate surgery planning for experts. The most realistic visualization can be achieved by the Monte-Carlo path tracing (MCPT) rendering technique which is based on the physical transport of light. However, this technique applied to biomedical volumes has received relatively little attention, because, naively implemented, it does not allow to interact with the data. In this paper, we present our application of MCPT to the biomedical volume rendering–Advanced Realistic Rendering Technique (AR2T), in an attempt to achieve more realism and increase the level of detail in data representation. The main result of our research is a practical framework that includes different visualization techniques: iso-surface rendering, direct volume rendering (DVR) combined with local and global illumination, maximum intensity projection (MIP), and AR2T. The framework allows interaction with the data in high quality for the deterministic algorithms, and in low quality for the stochastic AR2T. A high-quality AR2T image can be generated on user request; the quality improves in real-time, and the process is stopped automatically on the algorithm convergence, or by user, when the desired quality is achieved. The framework enables direct comparison of different rendering algorithms, i.e., utilizing the same view/light position and transfer functions. It therefore can be used by medical experts for immediate one-to-one visual comparison between different data representations in order to collect feedback about the usefulness of the realistic 3D visualization in clinical environment.

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Author information

Authors and Affiliations

  1. Department of Information Engineering, University of Florence, 50139, Florence, Italy

    Elena Denisova & Leonardo Bocchi

  2. Department of Medical Biotechnologies, University of Siena, 53100, Siena, Italy

    Ernesto Iadanza

  3. Imaginalis S.r.l., 50019, Sesto Fiorentino, Italy

    Elena Denisova & Leonardo Manetti

  4. Eidolab, Florence, Italy

    Elena Denisova, Leonardo Manetti & Leonardo Bocchi

Authors
  1. Elena Denisova
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  2. Leonardo Manetti
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  3. Leonardo Bocchi
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  4. Ernesto Iadanza
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Corresponding author

Correspondence to Elena Denisova .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Denisova, E., Manetti, L., Bocchi, L., Iadanza, E. (2023). AR2T: Advanced Realistic Rendering Technique for Biomedical Volumes. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_34

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  • DOI: https://doi.org/10.1007/978-3-031-43987-2_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43986-5

  • Online ISBN: 978-3-031-43987-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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