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Published on: February 16, 2016
Automated Dual-energy Computed Tomography-based Extracellular Volume Estimation for Myocardial Characterization in
Andres F Abadia1, Gilberto J Aquino1, U Joseph Schoepf1
1Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC.
Insights
An automated dual-energy CT algorithm accurately assesses myocardial ECV in cardiomyopathy patients, significantly reducing analysis time. This method shows excellent diagnostic performance for distinguishing healthy individuals from those with heart conditions.
Area of Science:
- Cardiovascular Imaging
- Medical Physics
- Radiology
Background:
- Myocardial extracellular volume (ECV) assessment is crucial for diagnosing and managing cardiomyopathies.
- Current manual ECV calculation using dual-energy computed tomography (DECT) is time-consuming.
Purpose of the Study:
- To validate and test a prototype algorithm for automated DECT-based myocardial ECV assessment.
- To evaluate the algorithm's efficiency and diagnostic accuracy in patients with various cardiomyopathies.
Main Methods:
- Retrospective analysis of cardiac DECT data from healthy subjects (n=9) and patients with cardiomyopathy (n=109).
- Comparison of myocardial ECV calculated by a fully automated algorithm versus manual assessment.
- Receiver-operating characteristic (ROC) analysis to assess diagnostic performance.
Main Results:
- The automated method significantly reduced postprocessing time (2.2 min vs. 9.4 min, P <0.001).
- No significant difference in ECV values between automated and manual methods (P =0.088), with moderate correlation (r=0.68) and agreement (ICC=0.66).
- Automated ECV was significantly higher in cardiomyopathy patients (P <0.001) and achieved an AUC of 0.98 for discrimination.
Conclusions:
- Automated DECT-based ECV estimation is a valid method with moderate agreement to manual assessment.
- The algorithm demonstrates excellent diagnostic performance in differentiating healthy myocardium from diseased myocardium.
- This automated approach offers a time-efficient screening tool for cardiomyopathies.
Objectives:
We aimed to validate and test a prototype algorithm for automated dual-energy computed tomography (DECT)-based myocardial extracellular volume (ECV) assessment in patients with various cardiomyopathies.
Methods:
This retrospective study included healthy subjects (n=9; 61±10 y) and patients with cardiomyopathy (n=109, including a validation cohort n=60; 68±9 y; and a test cohort n=49; 69±11 y), who had previously undergone cardiac DECT. Myocardial ECV was calculated using a prototype-based fully automated algorithm and compared with manual assessment. Receiver-operating characteristic analysis was performed to test the algorithm's ability to distinguish healthy subjects and patients with cardiomyopathy.
Results:
The fully automated method led to a significant reduction of postprocessing time compared with manual assessment (2.2±0.4 min and 9.4±0.7 min, respectively, P <0.001). There was no significant difference in ECV between the automated and manual methods ( P =0.088). The automated method showed moderate correlation and agreement with the manual technique ( r =0.68, intraclass correlation coefficient=0.66). ECV was significantly higher in patients with cardiomyopathy compared with healthy subjects, regardless of the method used ( P <0.001). In the test cohort, the automated method yielded an area under the curve of 0.98 for identifying patients with cardiomyopathies.
Conclusion:
Automated ECV estimation based on DECT showed moderate agreement with the manual method and matched with previously reported ECV values for healthy volunteers and patients with cardiomyopathy. The automatically derived ECV demonstrated an excellent diagnostic performance to discriminate between healthy and diseased myocardium, suggesting that it could be an effective initial screening tool while significantly reducing the time of assessment.
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