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Visual analysis of longitudinal brain tumor perfusion

Sylvia Glasser, Steffen Oeltze, Uta Preim, Atle Bjørnerud and Helwig Hauser, Bernhard Preim

INPROCEEDINGS, Proc. SPIE, 2013

Abstract

In clinical research on diagnosis and evaluation of brain tumors, longitudinal perfusion MRI studies are acquired for tumor grading as well as to monitor and assess treatment response and patient prognosis. Within this work, we demonstrate how visual analysis techniques can be adapted to multidimensional datasets from such studies within a framework to support the computer-aided diagnosis of brain tumors. Our solution builds on two innovations: First, we introduce a pipeline yielding comparative, co-registered quantitative perfusion parameter maps over all time steps of the longitudinal study. Second, based on these time-dependent parameter maps, visual analysis methods were developed and adapted to reveal valuable insight into tumor progression, especially regarding the clinical research area of low grade glioma transformation into high grade gliomas. Our examination of four longitudinal brain studies demonstrates the suitability of the presented visual analysis methods and comprises new possibilities for the clinical researcher to characterize the development of low grade gliomas.

Published

Proc. SPIE

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BibTeX

@inproceedings{Glasser13VisualAnalysis,
author = {Sylvia Glasser and Steffen Oeltze and Uta Preim and Atle Bj{\o}rnerud and
 Helwig Hauser and Bernhard Preim},
title = {Visual analysis of longitudinal brain tumor perfusion},
booktitle = {Proc. SPIE},
volume = {8670},
pages = {86700Z-86700Z-11},
year = {2013},
doi = {10.1117/12.2007878},
URL = { http://dx.doi.org/10.1117/12.2007878},
abstract = {
 In clinical research on diagnosis and evaluation of brain tumors, longitudinal 
 perfusion MRI studies are acquired for tumor grading as well as to monitor and 
 assess treatment response and patient prognosis. Within this work, we demonstrate 
 how visual analysis techniques can be adapted to multidimensional datasets from 
 such studies within a framework to support the computer-aided diagnosis of brain 
 tumors. Our solution builds on two innovations: First, we introduce a pipeline 
 yielding comparative, co-registered quantitative perfusion parameter maps over 
 all time steps of the longitudinal study. Second, based on these time-dependent 
 parameter maps, visual analysis methods were developed and adapted to reveal 
 valuable insight into tumor progression, especially regarding the clinical research 
 area of low grade glioma transformation into high grade gliomas. Our examination 
 of four longitudinal brain studies demonstrates the suitability of the presented 
 visual analysis methods and comprises new possibilities for the clinical researcher 
 to characterize the development of low grade gliomas.},

}






 Last Modified: Jean-Paul Balabanian, 2014-11-06