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Induction and Micro-CT Imaging of Cerebral Cavernous Malformations in Mouse Model
Published on: September 4, 2017
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Statistical properties of cerebral CT perfusion imaging systems. Part II. Deconvolution-based systems
Ke Li1,2, Guang-Hong Chen1,2
1Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI, 53705, USA.
Medical Physics
|September 10, 2019
Summary
This study establishes quantitative links between input parameters and output map statistics for deconvolution-based cerebral perfusion imaging. The findings improve understanding of cerebral blood flow and volume measurements in CTP imaging.
Area of Science:
- Medical Imaging
- Biomedical Engineering
- Radiology
Background:
- Deconvolution-based computed tomography perfusion (CTP) imaging estimates physiological parameters from dynamic contrast-enhanced scans.
- Understanding the relationship between system input parameters and output perfusion map statistics is crucial for accurate quantification.
Purpose of the Study:
- To develop a theoretical framework quantifying the relationship between input parameters of deconvolution-based CTP systems and the statistical properties of output perfusion maps.
- To establish a theoretical basis for signal and noise characteristics in cerebral blood flow (CBF) and time-to-maximum ( ) estimations.
Main Methods:
- Derived analytical formulas for the expected value and autocovariance of the residue function using singular value decomposition-based deconvolution.
- Analyzed statistical properties of "max" and "arg max" operators to link perfusion parameters to the residue function and system parameters.
- Validated the theoretical model using simulated CTP images of a digital head phantom and an in vivo canine experiment for cerebral blood volume (CBV) noise.
Main Results:
- Numerical simulations showed high accuracy (≤0.21% error for autocovariance, ≤0.13% for expected value) between measured and theoretical residue function statistics.
- Bland-Altman analysis confirmed no significant difference between measured and theoretical mean or noise values for perfusion parameters.
- The theoretical CBV noise model aligned with experimental data, highlighting the impact of baseline image noise on deconvolution-based CBV.
Conclusions:
- Established quantitative relationships between statistical properties of deconvolution-based CTP maps and various system parameters (acquisition, reconstruction, contrast injection, deconvolution).
- Demonstrated the utility of the theoretical framework in understanding and optimizing CTP imaging protocols, particularly regarding baseline image quality.
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