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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
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Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
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Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
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Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

742
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
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Deconvolution01:20

Deconvolution

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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

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Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
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Related Experiment Video

Updated: Oct 9, 2025

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories
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Deep Learning Based Joint PET Image Reconstruction and Motion Estimation.

Tiantian Li, Mengxi Zhang, Wenyuan Qi

    IEEE Transactions on Medical Imaging
    |December 20, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a robust deep learning (DL) method for positron emission tomography (PET) imaging, improving motion artifact correction. The DL-ADMM algorithm enhances image quality by reducing bias and noise, outperforming traditional methods.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence in Healthcare
    • Nuclear Medicine

    Background:

    • Respiratory motion significantly degrades Positron Emission Tomography (PET) image quality, introducing motion artifacts.
    • Conventional motion estimation techniques are often sensitive to noise, limiting their effectiveness.
    • Simultaneous estimation of motion and emission images is crucial for accurate PET reconstruction.

    Purpose of the Study:

    • To develop a robust joint estimation method for PET imaging that integrates deep learning (DL) for motion estimation.
    • To improve the accuracy and quality of PET image reconstruction by mitigating respiratory motion artifacts.
    • To enhance lesion contrast and boundary definition in PET images through advanced motion compensation.

    Main Methods:

    • A novel joint estimation framework was proposed, incorporating a DL-based image registration network into regularized PET image reconstruction.
    • The framework formulated joint estimation as a constrained optimization problem, solved using the Alternating Direction Method of Multipliers (ADMM) algorithm.
    • The DL-ADMM algorithm was validated using both simulated and real PET data, with comparisons to iterative methods and motion-compensated reconstructions.

    Main Results:

    • The DL-ADMM joint estimation method demonstrated reduced bias compared to ungated images without increasing noise in simulations.
    • In real data studies, the proposed method achieved higher lesion contrast and sharper liver boundaries than ungated images.
    • The DL-ADMM method exhibited lower noise levels compared to the reference gated image, indicating superior performance.

    Conclusions:

    • The proposed DL-ADMM joint estimation method offers a robust and effective solution for motion artifact reduction in PET imaging.
    • This deep learning-integrated approach significantly improves image quality, lesion detectability, and anatomical detail in PET scans.
    • The findings suggest a promising direction for enhancing the clinical utility of PET imaging through advanced AI-driven reconstruction techniques.