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Journal of Medical Imaging

Evaluation of static and dynamic perfusion cardiac computed tomography for quantitation and classification tasks
Author(s): Michael D. Bindschadler; Dimple Modgil; Kelley R. Branch; Patrick J. La Rivière; Adam M. Alessio
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Paper Abstract

Cardiac computed tomography (CT) acquisitions for perfusion assessment can be performed in a dynamic or static mode. Either method may be used for a variety of clinical tasks, including (1) stratifying patients into categories of ischemia and (2) using a quantitative myocardial blood flow (MBF) estimate to evaluate disease severity. In this simulation study, we compare method performance on these classification and quantification tasks for matched radiation dose levels and for different flow states, patient sizes, and injected contrast levels. Under conditions simulated, the dynamic method has low bias in MBF estimates (0 to 0.1  ml/min/g) compared to linearly interpreted static assessment (0.45 to 0.48  ml/min/g), making it more suitable for quantitative estimation. At matched radiation dose levels, receiver operating characteristic analysis demonstrated that the static method, with its high bias but generally lower variance, had superior performance (p<0.05) in stratifying patients, especially for larger patients and lower contrast doses [area under the curve (AUC)=0.95 to 96 versus 0.86]. We also demonstrate that static assessment with a correctly tuned exponential relationship between the apparent CT number and MBF has superior quantification performance to static assessment with a linear relationship and to dynamic assessment. However, tuning the exponential relationship to the patient and scan characteristics will likely prove challenging. This study demonstrates that the selection and optimization of static or dynamic acquisition modes should depend on the specific clinical task.

Paper Details

Date Published: 2 May 2016
PDF: 11 pages
J. Med. Imag. 3(2) 024001 doi: 10.1117/1.JMI.3.2.024001
Published in: Journal of Medical Imaging Volume 3, Issue 2
Show Author Affiliations
Michael D. Bindschadler, Univ. of Washington (United States)
Dimple Modgil, The Univ. of Chicago (United States)
Kelley R. Branch, Univ. of Washington Medical Ctr. (United States)
Patrick J. La Rivière, The Univ. of Chicago (United States)
Adam M. Alessio, Univ. of Washington (United States)

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