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Related Concept Videos

Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
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Structural Joints: Fibrous Joints01:03

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Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
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Structural Joints: Cartilaginous Joints01:17

Structural Joints: Cartilaginous Joints

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As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
There are two types of cartilaginous joints:
Synchondrosis
A synchondrosis ("joined by cartilage") is a cartilaginous joint where bones are connected by hyaline cartilage. Synchondrosis may be temporary...
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Joints01:26

Joints

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Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
The bones of a...
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Velocity of an Object01:18

Velocity of an Object

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Understanding how an object moves along a path requires distinguishing between motion over a time span and motion at a precise moment. A useful example is a vehicle traveling along a straight and level path, where its position at any given time is known. The initial step in analyzing this motion is to measure how far the vehicle travels over a fixed time period. This measurement, called average velocity, is computed by dividing the total change in position by the duration over which the change...
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Method of Joints01:30

Method of Joints

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The method of joints is a commonly used technique to analyze the forces in structural trusses. The method is based on the principle of equilibrium, which assumes that the truss members are connected by frictionless pins. The forces at each joint can be determined by considering the equilibrium of the forces acting on that joint.
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Related Experiment Video

Updated: Feb 12, 2026

The Crossmodal Congruency Task as a Means to Obtain an Objective Behavioral Measure in the Rubber Hand Illusion Paradigm
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Joint Optimization of Fluence Field Modulation and Regularization for Multi-Task Objectives.

Grace J Gang1, J Webster Stayman1

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, U.S.A.

Proceedings of Spie--The International Society for Optical Engineering
|April 7, 2018
PubMed
Summary
This summary is machine-generated.

This study optimizes imaging reconstruction for different organs by adjusting fluence field modulation (FFM) and regularization. Task-driven adjustments significantly improve image quality, enhancing diagnostic capabilities for complex scenarios.

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Image Reconstruction

Background:

  • Model-based iterative reconstruction (MBIR) requires optimization for diverse imaging tasks.
  • Different organs present unique imaging challenges (e.g., low-contrast vs. high-contrast detection).
  • Task-driven optimization aims to improve diagnostic performance for specific clinical needs.

Purpose of the Study:

  • To develop and evaluate a task-driven framework for optimizing fluence field modulation (FFM) and regularization in MBIR.
  • To investigate how different objective functions impact FFM and regularization strategies for multi-organ imaging.
  • To enhance image quality and detectability for specific diagnostic tasks within different anatomical regions.

Main Methods:

  • Formulated two objective functions: maxi-min (equal importance) and region-of-interest (ROI) based.
  • Applied FFM and spatially varying regularization to an abdomen phantom simulating liver and kidney imaging tasks.
  • Evaluated performance using the detectability index (d') at discrete locations.

Main Results:

  • Maxi-min FFM improved d' by ~35% by optimizing for the most challenging liver task.
  • ROI-based FFM boosted liver d' by ~59% while maintaining minimum performance elsewhere.
  • Spatially varying regularization was crucial, with optimal strengths differing by two orders of magnitude between tasks.

Conclusions:

  • Multi-task objectives are vital for shaping optimal FFM and MBIR regularization.
  • Task-driven acquisition and reconstruction design can be generalized for complex diagnostic scenarios.
  • Optimized FFM and regularization enhance image quality and diagnostic performance across different organs.