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Proceedings Paper

Is there more valuable information in PWI datasets for a voxel-wise acute ischemic stroke tissue outcome prediction than what is represented by typical perfusion maps?
Author(s): Nils Daniel Forkert; Susanne Siemonsen; Michael Dalski; Tobias Verleger; Andre Kemmling; Jens Fiehler
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Paper Abstract

The acute ischemic stroke is a leading cause for death and disability in the industry nations. In case of a present acute ischemic stroke, the prediction of the future tissue outcome is of high interest for the clinicians as it can be used to support therapy decision making. Within this context, it has already been shown that the voxel-wise multi-parametric tissue outcome prediction leads to more promising results compared to single channel perfusion map thresholding. Most previously published multi-parametric predictions employ information from perfusion maps derived from perfusion-weighted MRI together with other image sequences such as diffusion-weighted MRI. However, it remains unclear if the typically calculated perfusion maps used for this purpose really include all valuable information from the PWI dataset for an optimal tissue outcome prediction. To investigate this problem in more detail, two different methods to predict tissue outcome using a k-nearest-neighbor approach were developed in this work and evaluated based on 18 datasets of acute stroke patients with known tissue outcome. The first method integrates apparent diffusion coefficient and perfusion parameter (Tmax, MTT, CBV, CBF) information for the voxel-wise prediction, while the second method employs also apparent diffusion coefficient information but the complete perfusion information in terms of the voxel-wise residue functions instead of the perfusion parameter maps for the voxel-wise prediction. Overall, the comparison of the results of the two prediction methods for the 18 patients using a leave-one-out cross validation revealed no considerable differences. Quantitatively, the parameter-based prediction of tissue outcome led to a mean Dice coefficient of 0.474, while the prediction using the residue functions led to a mean Dice coefficient of 0.461. Thus, it may be concluded from the results of this study that the perfusion parameter maps typically derived from PWI datasets include all valuable perfusion information required for a voxel-based tissue outcome prediction, while the complete analysis of the residue functions does not add further benefits for the voxel-wise tissue outcome prediction and is also computationally more expensive.

Paper Details

Date Published: 13 March 2014
PDF: 7 pages
Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 90381O (13 March 2014); doi: 10.1117/12.2043490
Show Author Affiliations
Nils Daniel Forkert, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)
Susanne Siemonsen, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)
Michael Dalski, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)
Tobias Verleger, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)
Andre Kemmling, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)
Jens Fiehler, Univ. Medical Ctr. Hamburg-Eppendorf (Germany)

Published in SPIE Proceedings Vol. 9038:
Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging
Robert C. Molthen; John B. Weaver, Editor(s)

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