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Related Concept Videos

Neural Control of Respiration01:18

Neural Control of Respiration

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The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
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Respiratory Depth
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Respiratory volumes are crucial metrics, meticulously measured to quantify the air exchanged in and out of the lungs during various phases of the breathing cycle. These precise measurements are vital for assessing lung function, diagnosing respiratory conditions, and monitoring overall respiratory health. Each parameter provides specific insights into the mechanics of breathing and the functional capacity of the lungs.
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The respiratory system is responsible for the intake of oxygen and the expulsion of carbon dioxide from the body. Respiratory volumes describe the volume of air in the lungs at different phases of the respiratory cycle. Tidal volume is the air breathed in and out during normal, quiet breathing. Inspiratory reserve volume is the air that can be forcefully inspired beyond the tidal volume. In contrast, expiratory reserve volume refers to the air that can be expelled from the lungs after a normal...
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Assessment of Respiration01:23

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The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
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Related Experiment Video

Updated: Dec 14, 2025

3D Cine Magnetic Resonance Imaging of Respiratory Motion in Mechanically Ventilated Mice and Rats
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3D Cine Magnetic Resonance Imaging of Respiratory Motion in Mechanically Ventilated Mice and Rats

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Respiratory Motion Prediction Using Deep Convolutional Long Short-Term Memory Network.

Shahabedin Nabavi1, Monireh Abdoos1, Mohsen Ebrahimi Moghaddam1

  • 1Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran.

Journal of Medical Signals and Sensors
|July 18, 2020
PubMed
Summary
This summary is machine-generated.

Deep artificial neural networks can predict pulmonary movements for improved radiation therapy planning. This method generates computed tomography (CT) images during the breathing cycle, enhancing treatment accuracy when four-dimensional CT (4D CT) is unavailable.

Keywords:
Convolutional long short-term memorydeep neural networklung motionradiotherapyrespiratory motion prediction

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Area of Science:

  • Medical Imaging
  • Radiation Oncology
  • Artificial Intelligence

Background:

  • Pulmonary movements during radiation therapy can harm healthy tissues.
  • Accurate treatment planning requires adapting to tumor motion.
  • Four-dimensional computed tomography (4D CT) is a key tool for monitoring lung motion.

Purpose of the Study:

  • To apply deep artificial neural networks for predicting pulmonary motion.
  • To generate computed tomography (CT) images throughout the breathing cycle.
  • To improve radiotherapy treatment planning using AI-driven motion prediction.

Main Methods:

  • Convolutional long short-term memory networks were utilized.
  • 3295 CT images from six patients across three views served as reference data.
  • A leave-one-patient-out cross-validation approach was employed for evaluation.

Main Results:

  • The method achieved a weighted average root-mean-squared error of 9 × 10^-3.
  • A weighted average structural similarity index measure of 0.943 was obtained.
  • The convolutional long short-term memory network demonstrated effective image generation.

Conclusions:

  • The proposed generative method creates CT images during the breathing cycle.
  • This approach enhances radiotherapy treatment planning.
  • It is particularly beneficial when 4D CT imaging is not accessible.