Abstract
In this paper, a medical decision approach for cardiac MRI by the registration algorithm and the reconstruction of 5D stacks for cine MRI sequences was developed. The estimation of the ejection fraction of the left ventricle (LVFE) was developed as an example. This study included 18 patients who underwent 1.5 T cardiac MRI for different etiologies: a total of 395 series and 18,483 scans was tested. The estimated values with the classical method of segmentation by contour with extreme (LVFE) in the sample was compared. A registration algorithm was implemented, with mean elapsed time registration = 0.5 s. The range of (LVFE) varied between [20 and 87%] shows that the results are satisfactory for the experts by comparing with the clinical assessment for the study of the anomalies of myocardial contractility and kinetic abnormalities and the error rate was significantly reduced. At baseline, the correlation between the clinical assessment of the LVFE with the 5D approach was consistent with 0.92, and with a 1.39% error rate mean for the sample studied, a mean = 60.34, the probability = 0.46 and the standard deviation was estimated to be 20.33 and about 21.78 for the LVFE with the 5D approach. Therefore, the 5D stacks approach applied to cine-cardiac MRI sequences for medical decision-making was implemented in the left ventricle with cardiac MRI. This approach shows its effectiveness in improving the accuracy of computation of LVFE. This approach seems to be very interesting, but needs to be validated in a larger sample study.
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Acknowledgements
A bundle of thanks is sent to Carthage International Medical Center that supported this work and to the medical staff for providing the blind data used in this study.
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Sakly, H., Said, M. & Tagina, M. Reconstruction of 5D cardiac MRI through the blood flow registration: simulation of the fifth dimension and assessment of the left ventricular ejection fraction. Netw Model Anal Health Inform Bioinforma 9, 61 (2020). https://doi.org/10.1007/s13721-020-00266-3
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DOI: https://doi.org/10.1007/s13721-020-00266-3