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The Journal of Supercomputing

, Volume 75, Issue 3, pp 1625–1640 | Cite as

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

  • Gregorio BernabéEmail author
  • José D. Casanova
  • Javier Cuenca
  • Josefa González-Carrillo
Article
  • 37 Downloads

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.

Keywords

Hypertrophic cardiomyopathy Trabecules Left ventricle Self-optimized software tool Automatic quantification Parallelization Real-time 

Notes

Acknowledgements

This work was supported by the Spanish MINECO, as well as by European Commission FEDER funds, under Grant TIN2015-66972-C5-3-R.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer EngineeringUniversity of MurciaMurciaSpain
  2. 2.Hospital Virgen of ArrixacaMurciaSpain

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