http://journal.frontiersin.org/article/10.3389/fneur.2015.00239/full?
Jana Katharina Wrosch1*,
Bastian Volbers1,2,
Philipp Gölitz3,
Daniel Frederic Gilbert4,
Stefan Schwab2,
Arnd Dörfler3,
Johannes Kornhuber1 and 
Teja Wolfgang Groemer1,5
- 1Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany
- 2Department of Neurology, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany
- 3Department of Neuroradiology, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany
- 4Institute of Medical Biotechnology, Friedrich-Alexander University of Erlangen-Nuremberg, Erlangen, Germany
- 5Psychiatric and Neurological Ambulatory Care Office, Bamberg, Germany
Introduction
Worldwide, ischemic stroke is the third-most common cause of years of live lost (1). In developed countries, stroke even ranks second (1).
Routine patients presenting with stroke symptoms are typically
evaluated by assessing diffusion MRI data, which is the most accurate
method of illustrating the affected brain area in the acute phase of a
stroke (2).
In diffusion MRI, the degree to which water molecules can diffuse
freely in the tissue is measured. In brain regions affected by stroke,
the diffusion of water is impaired and can be detected as a reduced
darkening of the MRI image over a recording time of (typically) 20 ms
per layer (3).
To evaluate the results, the two-dimensional images are stacked to form
a three-dimensional dataset. Through this method of representing the
imaging data, an observer can only assess the full information through
visual inspection and steady rotation or direct manipulation of the
images (e.g., scrolling through the layers). This approach is not always
possible and limits the applicability of the method in printed reports.
Computer-aided diagnosis of stroke has gained in importance over the last decade. There is a multitude of approaches (4–7). Most groups use CT images to make early developments of stroke visible and detectable (8–10). Tyan et al. (11)
considered diffusion MRI data as well as CT images for unsupervised
computer-aided diagnosis of stroke-affected brain volume. The focus of
all these approaches lies in enhancing the imaging data so that search
algorithms can detect stroke symptoms. With this work, we aim to add to
the visualization possibilities, which are not addressed by most
computer-aided diagnosis approaches. The dimensionality reduction to a
two-dimensional depiction of the entire brain provides advantages in
print, in compatibility with brain surface measurements such as EEG and
in tracking changes of the stroke lesion over time. We here present a
new approach of converting the three-dimensional imaging data set to a
two-dimensional map by geoprojection. To ensure the correct
representation of strokes in the two-dimensional map, we developed a
computer-aided artery territory recognition algorithm, using the
projected maps as input.
The projection of three-dimensional data into a
two-dimensional map via Mollweide geoprojection is a common method in
other fields (12–14).
We applied this mathematical method to diffusion MRI data to create a
static, two-dimensional representation of the entire brain providing
accurate information on stroke position and size.
The purpose of this retrospective study was to develop
an intuitive two-dimensional representation of three-dimensional brain
imaging data and to probe it for its diagnostic validity. To validate
the projection outcome, we assessed the diagnostic accuracy of
computer-aided stroke territory recognition based on geoprojected
two-dimensional maps of diffusion MRI data.
More confusion at the link.
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