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Visualizing tumor environment with perfusion and diffusion MRI: Computational challenges

Authors:

Kostas Marias,

Katerina Nikiforaki,

Georgios C. Manikis,

Eleftherios Kontopodis,

Nikolaos PapanikolaouAuthors Info & Claims

CGI '16: Proceedings of the 33rd Computer Graphics International

Pages 113 - 116

https://doi.org/10.1145/2949035.2949064

Published: 28 June 2016 Publication History

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Abstract

Visualizing tumor environment is a critical task for assessing treatment response as well as tailoring therapy to the individual by better understanding the viable, necrotic and hypoxic areas. While a number of imaging modalities can provide complementary information about the tumor composition, there are several constraints regarding radiation, cost and patient tolerance that dictate the need of non-invasive and cost-effective methods to be used for tumor imaging in the context of personalized medicine. In this paper we present some of the major challenges in imaging tumor environment using perfusion and diffusion Magnetic Resonance Imaging (MRI) based on the actual computational workflows and discuss important computational issues that affect the robustness, reproducibility as well as the clinical significance of the extracted clinical biomarkers.

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Cited By

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Garrison LKolesar IViola IHauser HBruckner S(2022)Trends & Opportunities in Visualization for Physiology: A Multiscale OverviewComputer Graphics Forum10.1111/cgf.1457541:3(609-643)Online publication date: 29-Jul-2022
https://doi.org/10.1111/cgf.14575
Wu XLi JLang SZhang XShi CLi XJia KZou M(2019)Perceptual Generative Adversarial Network for Image Inpainting with Regular and Irregular Patch2019 2nd International Conference on Safety Produce Informatization (IICSPI)10.1109/IICSPI48186.2019.9095931(46-50)Online publication date: Nov-2019
https://doi.org/10.1109/IICSPI48186.2019.9095931

Visualizing tumor environment with perfusion and diffusion MRI: Computational challenges
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis

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Published In

CGI '16: Proceedings of the 33rd Computer Graphics International

June 2016

130 pages

ISBN:9781450341233

DOI:10.1145/2949035

Conference Chair:
George Papagiannakis
University of Crete & FORTH, Greece
,
Program Chairs:
Daniel Thalmann
NTU, Singapore & EPFL, Switzerland
,
Panos Trahanias
University of Crete & FORTH, Greece

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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FORTH: Foundation for Research and Technology - Hellas

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Association for Computing Machinery

New York, NY, United States

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Published: 28 June 2016

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CGI '16

CGI '16: Computer Graphics International

June 28 - July 1, 2016

Heraklion, Greece

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Garrison LKolesar IViola IHauser HBruckner S(2022)Trends & Opportunities in Visualization for Physiology: A Multiscale OverviewComputer Graphics Forum10.1111/cgf.1457541:3(609-643)Online publication date: 29-Jul-2022
https://doi.org/10.1111/cgf.14575
Wu XLi JLang SZhang XShi CLi XJia KZou M(2019)Perceptual Generative Adversarial Network for Image Inpainting with Regular and Irregular Patch2019 2nd International Conference on Safety Produce Informatization (IICSPI)10.1109/IICSPI48186.2019.9095931(46-50)Online publication date: Nov-2019
https://doi.org/10.1109/IICSPI48186.2019.9095931

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A model-free approach for imaging tumor hypoxia from DCE-MRI data

Brain MR Image Tumor Segmentation with Ventricular Deformation

A Recurrent Two-Stage Anatomy-Guided Network for Registration of Liver DCE-MRI

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