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Morphologically normalized left ventricular motion indicators from MRI feature tracking characterize myocardial

Paolo Piras1,2, Luciano Teresi3, Paolo Emilio Puddu4

  • 1Dipartimento di Scienze Cardiovascolari, Respiratorie, Nefrologiche, Anestesiologiche e Geriatriche, Sapienza Università di Roma, Via del Policlinico 155, 00186, Roma, Italy.

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Myocardial infarction significantly alters left ventricular (LV) motion. Spatio-temporal analysis of LV motion attributes accurately identifies infarction, outperforming previous methods and applicable to various cardiac diseases.

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Area of Science:

  • Cardiovascular Research
  • Biomedical Engineering
  • Medical Imaging Analysis

Background:

  • Myocardial infarction (MI) causes significant changes in left ventricular (LV) function.
  • Accurate characterization of LV motion is crucial for diagnosing and understanding cardiac diseases.

Purpose of the Study:

  • To characterize spatio-temporal motion attributes of the left ventricle in patients with myocardial infarction.
  • To assess the efficacy of these attributes in differentiating between infarcted and control subjects.

Main Methods:

  • Acquired time-varying 3D finite element shape models from 300 controls and 300 MI patients.
  • Utilized parallel transport to normalize for inter-individual shape variations.
  • Employed principal component (PC) scores to define trajectory attributes and tested with ANOVA/MANOVA.
  • Applied support vector machine (SVM) for classification using endocardial PC scores in shape space.

Main Results:

  • Infarcted patients exhibited significantly different LV trajectory magnitudes, orientations, and shapes compared to controls.
  • Distinct angular differences were observed between PC scores in both endocardial and epicardial layers.
  • Endocardial motion magnitude showed the most substantial differences.
  • Endocardial PC scores in shape space achieved superior classification accuracy via SVM.

Conclusions:

  • LV spatio-temporal motion attributes effectively characterize the presence of myocardial infarction.
  • The shape space analysis demonstrated higher classification power than size-and-shape space.
  • This methodology is generalizable for studying diverse cardiac pathologies and their pathophysiological impacts.