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Cardiac Output and Stroke Volume

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Cardiac output (CO) is an integral aspect of human physiology, reflecting the heart's efficiency and responsiveness to the body's needs. It represents the volume of blood that the left or right ventricle ejects into the aorta or pulmonary trunk each minute. The CO is calculated by multiplying the heart rate (HR)—the number of heartbeats per minute—by the stroke volume (SV)—the amount of blood pumped out with each heartbeat.
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Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
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Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

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Cardiac output (CO), the amount of blood the heart pumps per minute, is a parameter in cardiovascular physiology determined by stroke volume and heart rate. Stroke volume, the amount of blood pushed from one of the ventricles per heartbeat, is influenced by preload, afterload, and contractility.
Preload
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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
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Regulation of Stroke Volume01:27

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The regulation of stroke volume, which is the amount of blood the heart pumps out during each heartbeat, is critical for maintaining a healthy circulatory system. Stroke volume is influenced by three main factors: preload, contractility, and afterload.
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AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep

Christian Weihsbach1, Nora Vogt2, Ziad Al-Haj Hemidi1

  • 1Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany.

Sensors (Basel, Switzerland)
|April 13, 2024
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Summary

This study introduces a deep learning model for cardiac MRI that reconstructs heart shape from limited data, optimizing views for faster, high-quality imaging. It achieves accurate 3D heart shape reconstruction, improving cardiac cine MRI efficiency.

Keywords:
cardiac magnetic resonance imagingdeep learningshape reconstructionview optimization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Cardiovascular Imaging

Background:

  • Cardiac cine MRI is crucial for assessing heart function but is limited by motion artifacts and long acquisition times.
  • Achieving high-resolution, volumetric, isotropic data with high temporal resolution in cardiac MRI is constrained by physics.
  • Current clinical protocols may not be optimal for comprehensive whole-heart shape assessment within minimal acquisition time.

Purpose of the Study:

  • To develop a deep learning model for reconstructing the volumetric shape of cardiac chambers from limited MRI slices.
  • To simultaneously optimize slice acquisition orientation for improved shape reconstruction.
  • To evaluate the model's performance against standard clinical views in simulated and real cardiac MRI data.

Main Methods:

  • A deep learning model was designed to reconstruct 3D cardiac chamber shapes from a reduced set of input MRI slices.
  • Slice acquisition orientations were jointly optimized with the shape reconstruction task.
  • The model's reconstruction accuracy was compared between standard clinical views and optimized views using metrics like HD95 and Dice scores.

Main Results:

  • The proposed deep learning model achieved accurate high-resolution multi-chamber shape reconstruction.
  • Reconstruction errors were below 13 mm HD95, and Dice scores exceeded 80%.
  • The optimized views demonstrated superior shape reconstruction quality compared to standard clinical views.

Conclusions:

  • The deep learning approach effectively reconstructs volumetric cardiac shapes from limited data, addressing motion artifacts in cardiac cine MRI.
  • Simultaneous optimization of slice acquisition orientation enhances reconstruction accuracy and efficiency.
  • This method shows significant potential for improving diagnostic capabilities in cardiac MRI, especially in cases with diverse pathological shape variations.