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Related Concept Videos

Frames01:30

Frames

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Frames are essential components of various mechanical and structural systems used daily. These structures are known for their stability and ability to bear heavy loads. A frame is constructed using two-force and multi-force members, interconnected using pin joints. In contrast, trusses are made entirely of two-force members.
Frames are versatile and widely used in various applications such as structural supports for beams and columns, automobile chassis construction, and in the construction...
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Inertial Frames of Reference01:03

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Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with...
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Non-inertial Frames of Reference01:27

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A reference frame accelerating or decelerating relative to an inertial frame is a non-inertial frame. To help understand this, consider what taking off in an airplane, turning a corner in a car, riding a merry-go-round, and the circular motion of a tropical cyclone all have in common. All these systems are accelerating, decelerating, or rotating relative to the Earth; hence, they all are non-inertial frames. All these systems exhibit inertial forces, which merely seem to arise from motion,...
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Frames: Problem Solving II01:26

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Consider a hydraulic hoist supporting a load of 1 kN. Assuming a simplified schematic representation of this frame structure, the force acting on BD and BF members can be determined.
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Related Experiment Video

Updated: Mar 13, 2026

Applications of Spatio-temporal Mapping and Particle Analysis Techniques to Quantify Intracellular Ca2+ Signaling In Situ
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A Novel Elastographic Frame Quality Indicator and its use in Automatic Representative-Frame Selection from a Cine

Bhaskara Rao Chintada1, Adhitya Vikraman Subramani1, Bagyam Raghavan2

  • 1Biomedical Ultrasound Laboratory, Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India.

Ultrasound in Medicine & Biology
|October 11, 2016
PubMed
Summary

This study introduces an automated method for selecting representative breast ultrasound elastogram frames, reducing radiologist variability and saving time. The algorithm achieved high correlation with expert selections, improving objective frame selection.

Keywords:
Angle of compressionAxial strain elastogramAxial-shear strain elastogramElastographyFrame quality indicatorNormalized axial-shear strain areaReal-time feedbackRepresentative frame

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Ultrasound Technology

Background:

  • Freehand compression elastography generates numerous breast ultrasound frames, requiring manual selection of representative ones for analysis.
  • Manual frame selection introduces inter-observer variability, impacting diagnostic accuracy and efficiency.
  • Existing quality indicators for axial strain elastograms do not fully capture the nuances of axial-shear strain interpretation.

Purpose of the Study:

  • To develop and validate an automated algorithm for selecting representative frames from breast ultrasound elastography cine loops.
  • To incorporate novel quality factors, including the angle of compression, into an automated frame selection process.
  • To reduce inter-observer variability and enhance the objectivity of elastogram analysis.

Main Methods:

  • An algorithm was developed to identify contiguous frames (N=10) with the highest cumulative quality scores, integrating correlation coefficient, frame-average axial strain, and angle of compression.
  • The algorithm was tested on 40 in vivo breast lesions (malignant and benign).
  • Automated selections were compared against radiologist-identified frames using cross-correlation for similarity assessment.

Main Results:

  • The automated algorithm demonstrated high average correlation with radiologist selections: 82.2% (axial strain elastogram), 83.4% (axial-shear strain elastogram), and 78.4% (sonogram).
  • The developed quality metric showed potential for real-time feedback during elastographic data acquisition.
  • The method offers an objective alternative to manual frame selection, potentially saving radiologist time.

Conclusions:

  • The automated frame selection method provides an objective and efficient approach to identifying representative frames in breast ultrasound elastography.
  • Incorporating the angle of compression enhances the quality assessment for axial-shear strain elastograms.
  • The algorithm and quality metric can aid in standardizing elastogram analysis and improving training.