Dataset related to the article "Prediction of myocardial blood flow under stress conditions by means of a computational model"
<p>This record contains raw data related to the article “Prediction of myocardial blood flow under stress conditions by means<br> of a computational model”</p> <p><strong>Purpose. </strong>Quantification of myocardial blood flow (MBF) and functional assessment of coronary artery disease (CAD) can be achieved through stress myocardial computed tomography perfusion (stress-CTP). This requires an additional scan after the resting coronary computed tomography angiography (cCTA) and administration of an intravenous stressor. This complex protocol has limited reproducibility and non-negligible side effects for the patient. We aim to mitigate these drawbacks by proposing a computational model able to reproduce MBF maps.</p> <p><strong>Methods. </strong>A computational perfusion model was used to reproduce MBF maps. The model parameters were estimated by using information from cCTA and MBF measured from stress-CTP (MBF<sub>CTP</sub>) maps. The relative error between the computational MBF under stress conditions (MBF<sub>COMP</sub>) and MBF<sub>CTP</sub> was evaluated to assess the accuracy of the proposed computational model.</p> <p><strong>Results.</strong> Applying our method to 9 patients (4 control subjects without ischemica vs 5 patients with myocardial ischemia), we found an excellent agreement between the values of MBF<sub>COMP</sub> and MBF<sub>CTP</sub>. In all patients, the relative error was below 8% over all the myocardium, with an average-in-space value below 4%.</p> <p><strong>Conclusion. </strong>The results of this pilot work demonstrate the accuracy and reliability of the proposed computational model in reproducing MBF under stress conditions. This consistency test is a preliminary step in the framework of a more ambitious project which is currently under investigation, i.e. the construction of a computational tool able to predict MBF avoiding the stress protocol and potential side effects while reducing radiation exposure.</p>
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12/100
Overall dataset sharing score
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These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 0
- Reuse readiness
- 0
- Engagement
- 4