Back to Search Start Over

A self-optimized software tool for quantifying the degree of left ventricle hyper-trabeculation.

Authors :
Bernabé, Gregorio
Casanova, José D.
Cuenca, Javier
González-Carrillo, Josefa
Source :
Journal of Supercomputing. Mar2019, Vol. 75 Issue 3, p1625-1640. 16p.
Publication Year :
2019

Abstract

Left ventricular non-compaction is characterized by the presence of multiple trabecules in the left ventricle myocardium, associated with multiple inter-trabecular recesses communicated with the ventricular cavity. The medical community needs an objective quantification of non-compacted cardiomyopathy, characterized by a trabeculated mass in the left ventricle myocardium. A software tool for the automatic quantification of the exact hyper-trabeculation degree in the left ventricle myocardium for a population of hypertrophic cardiomyopathy (QLVTHC) patients is developed and tested. End-diastolic cardiac magnetic resonance images of the patients are the input of the software, while the volumes of the compacted zones and the trabeculated zones are necessary to produce the percentage quantification of the trabecular zone with respect to the compacted zone. Significant improvements are obtained with respect to the manual process, by saving valuable diagnosis time. The development of a self-optimized software tool (SOST) based on the outputs of 50 patients with hypertrophic cardiomyopathy automatically produces the volumes of the compacted zones and the trabeculated zones, as a percentage quantification. Now, the SOST is tested with a different population of patients, with different characteristics. Besides, a parallelization for the detection of the external layer of the compacted zone allows the real-time analysis per slice in a patient, obtaining important speedups with regard to the QLVTHC proposed and the manual process used traditionally by cardiologists. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09208542
Volume :
75
Issue :
3
Database :
Academic Search Index
Journal :
Journal of Supercomputing
Publication Type :
Academic Journal
Accession number :
135780630
Full Text :
https://doi.org/10.1007/s11227-018-2722-x