Aims and objectives
Cerebral perfusion computed tomography (PCT) is an important imaging technique in the case of an acute stroke event [1].
After a bolus injection,
a CT scan results in reconstructed time concentration curves (TCCs),
from which perfusion maps containing diagnostic information such as cerebral blood volume (CBV) and cerebral blood flow (CBF) are derived [2].
Cerebral PCT is of particular importance in the case of an ischemic stroke event.
The mismatch between the CBF and the CBV perfusion maps can be used to identify a region...
Methods and materials
Data acquisition
Data acquisition is performed by rotating source and detector multiple times around the patient.
In each 180° rotation,
data is sampled equiangularly with a starting angle chosen such that no angle is sampled twice during the entire acquisition protocol.
Reconstruction method
Overview
In contrast to the conventional approach where reconstructions are calculated independently per 180° projection data set,
our algorithm exploits the temporal correlation of the reconstructions.
This is achieved in two ways: static regions (e.g.,
bone and air) are forced to be...
Results
Projection data was simulated with a realistic brain perfusion phantom [5],
which enables accurate validation and reproducibility.
A region of moderate and severely reduced perfusion was indicated on the phantom,
which manifests itself by a reduced blood flow and volume in the ground truth CBV and CBF maps as shown in Fig.
6 and Fig.
7,
respectively.
Poisson distributed noise was applied to the projection data.
For every experiment,
the CBF and CBV were calculated with the truncated singular value decomposition approach [2].
The results...
Conclusion
The combination of an adapted scanning protocol with an iterative algorithm exploiting temporal correlation of the TCCs results in significantly better image quality for the same dose.
This implies that patient dose can be reduced while maintaining image quality.
Simulation experiments showed that our approach achieved the same image quality (in terms of TCCs) while reducing radiation dose to 32% of the dose compared to a conventional approach such as SIRT.
Personal information
G.
Van Eyndhoven
iMinds-Vision Lab
University of Antwerp
Universiteitsplein 1
B-2610 Wilrijk
Belgium
K.J.
Batenburg
Centrum Wiskunde & Informatica
Science Park 123
NL-1090 GB Amsterdam
The Netherlands
J.
Sijbers
iMinds-Vision Lab
University of Antwerp
Universiteitsplein 1
B-2610 Wilrijk
Belgium
References
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M.,
Sincic,
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Sridhar,
D.,
& Chien,
J.
(2008).
Cerebral perfusion CT: technique and clinical applications.
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35(5),
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[2] Fieselmann,
A.,
Kowarschik,
M.,
Ganguly,
A.,
Hornegger,
J.,
& Fahrig,
R.
(2011).
Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details.
International journal of biomedical imaging,
2011,
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[3] Murphy B D,
Fox A J,
Lee D H,
Sahlas D J,
Black S E,
Hogan M J,
Coutts S B,
Demchuk A M,
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Aviv...