Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Relaxation of Skeletal Muscles01:29

Relaxation of Skeletal Muscles

10.9K
The period of muscle contraction primarily influences the duration of stimulation at the neuromuscular junction (NMJ), the presence of free calcium ions in the sarcoplasm, and the availability of energy or ATP to support contractions.
When an action potential reaches the axon terminal, it depolarizes the membrane and opens voltage-gated sodium channels. Sodium ions enter the cell, further depolarizing the presynaptic membrane. This depolarization causes voltage-gated calcium channels to open....
10.9K
Atomic Nuclei: Types of Nuclear Relaxation01:28

Atomic Nuclei: Types of Nuclear Relaxation

1.2K
Nuclear relaxation restores the equilibrium population imbalance and can occur via spin–lattice or spin–spin mechanisms, which are first-order exponential decay processes.
In spin–lattice or longitudinal relaxation, the excited spins exchange energy with the surrounding lattice as they return to the lower energy level. Among several mechanisms that contribute to spin–lattice relaxation, magnetic dipolar interactions are significant. Here, the excited nucleus transfers...
1.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

415
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
415
Atomic Nuclei: Nuclear Relaxation Processes01:23

Atomic Nuclei: Nuclear Relaxation Processes

1.4K
In the absence of an external magnetic field, nuclear spin states are degenerate and randomly oriented. When a magnetic field is applied, the spins begin to precess and orient themselves along (lower energy) or against (higher energy) the direction of the field. At equilibrium, a slight excess population of spins exists in the lower energy state. Because the direction of the magnetic field is fixed as the z-axis,  the precessing magnetic moments are randomly oriented around the z-axis.
1.4K
Linearization and Approximation01:26

Linearization and Approximation

215
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
215
NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

1.2K
When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A clinically feasible framework to estimate tau pathology and clinical-biological discordance in the Alzheimer's disease spectrum.

Alzheimer's research & therapy·2026
Same author

ADAS-Cog11 performance as a surrogate of tau PET burden in an Asian Alzheimer's disease cohort.

Journal of Alzheimer's disease : JAD·2026
Same author

Unsupervised 1D CNN -bidirectional long short-term memory model with multi-head attention for generating intravoxel incoherent motion maps.

Medical physics·2026
Same author

Longitudinal voxel-based FDG PET assessment of chemotherapy effects on brain metabolism in lung cancer.

Brain research bulletin·2026
Same author

Amyloid PET quantification with deep learning segmentation models without MRI.

EJNMMI physics·2026
Same author

An External Validation Study on Two Pre-Trained Large Language Models for Multimodal Prognostication in Laryngeal and Hypopharyngeal Cancer: Integrating Clinical, Treatment, and Radiomic Data to Predict Survival Outcomes with Interpretable Reasoning.

Bioengineering (Basel, Switzerland)·2025

Related Experiment Video

Updated: Apr 20, 2026

Mechanical Control of Relaxation Using Intact Cardiac Trabeculae
07:51

Mechanical Control of Relaxation Using Intact Cardiac Trabeculae

Published on: February 17, 2023

1.7K

Acceleration of MAP-EM algorithm via over-relaxation.

Yu-Jung Tsai1, Hsuan-Ming Huang2, Yu-Hua Dean Fang3

  • 1Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|December 4, 2014
PubMed
Summary

This study introduces a modified Maximum a Posteriori Expectation-Maximization (MAP-EM) algorithm, called MAP-AEM, using an over-relaxation factor. MAP-AEM significantly accelerates tomographic image reconstruction convergence compared to standard MAP-EM.

Keywords:
Faster tomographic reconstructionMAP-EM algorithmPET reconstructionSPECT reconstruction

More Related Videos

The Mechanics of Poro-Elastic Contractile Actomyosin Networks As a Model System of the Cell Cytoskeleton
08:50

The Mechanics of Poro-Elastic Contractile Actomyosin Networks As a Model System of the Cell Cytoskeleton

Published on: March 10, 2023

1.3K
Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry
11:19

Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry

Published on: September 6, 2016

13.2K

Related Experiment Videos

Last Updated: Apr 20, 2026

Mechanical Control of Relaxation Using Intact Cardiac Trabeculae
07:51

Mechanical Control of Relaxation Using Intact Cardiac Trabeculae

Published on: February 17, 2023

1.7K
The Mechanics of Poro-Elastic Contractile Actomyosin Networks As a Model System of the Cell Cytoskeleton
08:50

The Mechanics of Poro-Elastic Contractile Actomyosin Networks As a Model System of the Cell Cytoskeleton

Published on: March 10, 2023

1.3K
Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry
11:19

Characterizing Multiscale Mechanical Properties of Brain Tissue Using Atomic Force Microscopy, Impact Indentation, and Rheometry

Published on: September 6, 2016

13.2K

Area of Science:

  • Medical Imaging
  • Computational Science
  • Image Processing

Background:

  • The Maximum a Posteriori Expectation-Maximization (MAP-EM) algorithm is crucial for tomographic reconstruction.
  • Improving the convergence rate of MAP-EM is essential for efficient image reconstruction.

Purpose of the Study:

  • To develop a modified MAP-EM algorithm (MAP-AEM) that accelerates image reconstruction.
  • To evaluate the convergence rate and noise properties of MAP-AEM compared to MAP-EM and ordered-subset methods.

Main Methods:

  • Introduction of an over-relaxation factor into the MAP-EM algorithm to create MAP-AEM.
  • Comparative analysis of MAP-AEM against MAP-EM and an ordered-subset algorithm.
  • Evaluation based on convergence rate and image noise properties.

Main Results:

  • The proposed MAP-AEM method demonstrates significantly faster numerical convergence than the standard MAP-EM algorithm.
  • The convergence speed of MAP-AEM is comparable to that of ordered-subset methods.
  • MAP-AEM effectively accelerates tomographic reconstruction.

Conclusions:

  • The MAP-AEM algorithm is an effective enhancement for accelerating tomographic image reconstruction.
  • The over-relaxation factor successfully improves the convergence rate of MAP-EM.
  • MAP-AEM offers a viable alternative for faster and efficient tomographic reconstructions.