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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...

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SCAR-AWARE LATE MECHANICAL ACTIVATION DETECTION NETWORK FOR OPTIMAL CARDIAC RESYNCHRONIZATION THERAPY PLANNING.

Jiarui Xing1, Shuo Wang2, Amit R Patel2

  • 1Department of Electrical and Computer Engineering, University of Virginia, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|September 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel scar-aware network for detecting late mechanical activation (LMA) regions, crucial for cardiac resynchronization therapy (CRT). The method accurately identifies LMA while avoiding false positives in myocardial scar tissue, improving CRT planning.

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Area of Science:

  • Cardiovascular Imaging
  • Medical Image Analysis
  • Computational Cardiology

Background:

  • Accurate identification of late mechanical activation (LMA) regions is vital for effective cardiac resynchronization therapy (CRT) lead implantation.
  • Current cardiac magnetic resonance (CMR) imaging methods for LMA detection may misidentify myocardial scar as delayed activation, potentially compromising CRT outcomes.
  • Integrating scar information into LMA detection is essential for improving the precision of CRT planning.

Purpose of the Study:

  • To develop and validate a scar-aware LMA detection network that simultaneously identifies myocardial scar and LMA regions.
  • To prevent the misclassification of scar tissue as LMA, thereby enhancing the accuracy of LMA mapping.
  • To improve the overall effectiveness of CRT planning through more precise LMA identification.

Main Methods:

  • Integration of a pre-trained scar segmentation network (using late gadolinium enhancement - LGE CMRs) with a LMA detection network utilizing displacement encoding with stimulated echoes (DENSE) CMRs.
  • Introduction of a novel scar-aware loss function to penalize false-positive LMA detections in scarred regions.
  • Model training capability with or without paired LGE data, and inference without requiring LGE images, relying on strain data patterns.

Main Results:

  • Demonstrated significantly improved LMA detection accuracy in subjects with and without myocardial scar.
  • Successfully mitigated false-positive LMA detection in potential scar regions during inference.
  • Validated the model's efficacy in scenarios with and without scar, showcasing robust performance.

Conclusions:

  • The proposed scar-aware LMA detection network offers a significant advancement in identifying LMA regions for CRT.
  • This approach enhances the accuracy of LMA mapping by effectively distinguishing scar tissue from delayed activation.
  • The findings pave the way for improved CRT planning and potentially better patient outcomes.