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Related Concept Videos

The Cardiac Cycle01:13

The Cardiac Cycle

95.8K
The heart beats rhythmically in a sequence called the cardiac cycle—a rapid coordination of contraction (systole) and relaxation (diastole).
The Process
Electrical signals—sent from the sinoatrial (SA) node in the right atrial wall to the atrioventricular (AV) node between the right atrium and right ventricle—cause both atria to simultaneously contract. When the signal reaches the AV node, it pauses for approximately a tenth of a second, allowing the atria to contract and...
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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Cardiac Cycle01:29

Cardiac Cycle

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The cardiac cycle refers to the sequence of events that occur in the heart from the beginning of one heartbeat to the next. It's characterized by alternating periods of contraction (systole) and relaxation (diastole) of the heart muscles.
During the cardiac cycle, blood flow through the heart is regulated entirely by changing pressure gradients. This sequence of events begins with the heart in a state of total relaxation, known as mid-to-late diastole, during which blood passively flows from...
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Physiology of the Heart: The Cardiac Cycle01:18

Physiology of the Heart: The Cardiac Cycle

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The cardiac cycle describes the events from one heartbeat to the next. It includes three main phases: diastole, atrial systole, and ventricular systole, all driven by changes in chamber pressures and the function of heart valves.
Diastole: The Relaxation Phase
During diastole, all four heart chambers relax. The atrioventricular (AV) valves open, and the semilunar valves close. This phase sees the lowest chamber pressures, promoting ventricular filling. Venous blood enters the heart through the...
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Related Experiment Video

Updated: Dec 6, 2025

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
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Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

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End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences.

Raphael Royer-Rivard, Fantin Girard, Nagib Dahdah

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    This study introduces a neural network to synchronize X-ray angiography views for cardiac cycle analysis. This synchronization improves the accuracy of dynamic coronary artery reconstructions, aiding clinical perfusion assessments.

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    High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

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    Area of Science:

    • Medical Imaging
    • Cardiovascular Imaging
    • Artificial Intelligence in Medicine

    Background:

    • Dynamic 3D+T reconstructions of coronary arteries offer vital perfusion data.
    • Accurate temporal matching of non-simultaneously acquired views is crucial for stereo-matching coronary segments.

    Purpose of the Study:

    • To develop a neural network capable of synchronizing different X-ray angiography views.
    • To enable accurate temporal alignment of coronary artery segments using only raw X-ray videos.

    Main Methods:

    • Training a neural network on angiographic sequences to extract cardiac cycle progression features.
    • Computing distance maps between feature vectors of different views to identify temporal associations.
    • Employing pathfinding algorithms to determine the most coherent frame associations between videos.

    Main Results:

    • Generated distance maps with distinct stripe patterns indicating temporal relationships.
    • Achieved a 96.04% accuracy in aligning synchronized frames with ECG signals in an evaluation set.

    Conclusions:

    • A neural network approach effectively synchronizes X-ray angiography views for cardiac analysis.
    • This method facilitates accurate temporal matching, crucial for 3D+T coronary artery reconstructions and perfusion assessment.