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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Related Experiment Video

Updated: Jun 18, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
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Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Bayesian PET image reconstruction incorporating anato-functional joint entropy.

Jing Tang1, Arman Rahmim

  • 1Department of Radiology, The Johns Hopkins University, Baltimore, MD 21287, USA. jtang18@jhmi.edu

Physics in Medicine and Biology
|November 12, 2009
PubMed
Summary

We developed a novel maximum a posteriori (MAP) reconstruction method for positron emission tomography (PET) that integrates magnetic resonance (MR) imaging. This approach significantly enhances image quality by improving the noise-versus-bias tradeoff for better PET image reconstruction.

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Neuroscience

Background:

  • Positron Emission Tomography (PET) is crucial for functional brain imaging.
  • PET image reconstruction often faces challenges with noise and bias.
  • Magnetic Resonance (MR) imaging provides detailed anatomical information.

Purpose of the Study:

  • To develop an advanced PET image reconstruction method.
  • To incorporate anatomical information from MR images into PET reconstruction.
  • To improve the quantitative performance and image quality of PET scans.

Main Methods:

  • Developed a maximum a posteriori (MAP) reconstruction algorithm.
  • Utilized joint entropy between PET and MR image features as a regularization constraint.
  • Employed a non-parametric method for joint probability density estimation.
  • Validated the method using simulated human brain phantoms (PET and MR).

Main Results:

  • The proposed MAP reconstruction method significantly improved the noise-versus-bias tradeoff.
  • Anatomical information from MR images enhanced PET image quality across all regions of interest.
  • Improved contrast-versus-noise tradeoff was observed even for lesions without direct MR anatomical correspondence.
  • Performance gains were demonstrated after parameter optimization.

Conclusions:

  • Integrating MR anatomical information into PET reconstruction via joint entropy regularization is effective.
  • The developed MAP method offers a substantial improvement over conventional PET reconstruction techniques.
  • This approach has the potential to enhance diagnostic accuracy in PET imaging.