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

Interaction of EM Radiation with Matter: Spectroscopy01:12

Interaction of EM Radiation with Matter: Spectroscopy

3.2K
Electromagnetic (EM) radiation can be considered an oscillating electric and magnetic field propagating through a medium that can interact with matter in its path. The electric field in the radiation can interact with electrical charges in the atoms or molecules in the matter. On the other hand, the magnetic field can interact with the magnetic field in the atomic nucleus. The study of the interaction between electromagnetic radiation and matter is termed spectroscopy. Spectroscopy is the study...
3.2K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

403
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
403
Dual Nature of Electromagnetic (EM) Radiation01:10

Dual Nature of Electromagnetic (EM) Radiation

3.8K
Electromagnetic (EM) radiation consists of electric and magnetic field components oscillating in planes perpendicular to each other and mutually perpendicular to radiation propagation through space. EM radiation can be classified as a wave, characterized by the properties of waves such as wavelength (denoted as λ) and frequency (represented by ν).
Wavelength is the distance between two consecutive peaks (the highest point) or troughs (the lowest point) in the wave. Frequency is the number of...
3.8K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

725
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
725
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.7K
3.7K
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.9K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.9K

You might also read

Related Articles

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

Sort by
Same author

Computed Quantitative Planar Imaging for Targeted Alpha Therapy: Model-Based Sparse Reconstruction Validated With a Novel <sup>225</sup>Ac Epoxy Phantom.

IEEE transactions on medical imaging·2026
Same author

An accelerated preconditioned proximal gradient algorithm with a generalized Nesterov momentum for PET image reconstruction.

Inverse problems·2025
Same author

MIRD Pamphlet No. 33: MIRDpvc-A Software Tool for Recovery Coefficient-Based Partial-Volume Correction.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2025
Same author

MIRD Pamphlet No. 32: A MIRD Recovery Coefficient Model for Resolution Characterization and Shape-Specific Partial-Volume Correction.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2025
Same author

Investigation and optimization of PET-guided SPECT reconstructions for improved radionuclide therapy dosimetry estimates.

Frontiers in nuclear medicine·2024
Same author

Two-dimensional quadratic Weyl points, nodal loops, and spin-orbit Dirac points in PtS, PtSe, and PtTe monolayers.

Physical chemistry chemical physics : PCCP·2024

Related Experiment Video

Updated: Jan 28, 2026

Visualization and Quantification of Brown and Beige Adipose Tissues in Mice using [18F]FDG Micro-PET/MR Imaging
08:31

Visualization and Quantification of Brown and Beige Adipose Tissues in Mice using [18F]FDG Micro-PET/MR Imaging

Published on: July 1, 2021

3.5K

A Krasnoselskii-Mann Algorithm With an Improved EM Preconditioner for PET Image Reconstruction.

Yizun Lin, C Ross Schmidtlein, Qia Li

    IEEE Transactions on Medical Imaging
    |February 23, 2019
    PubMed
    Summary

    A new algorithm, the improved EM preconditioned Krasnoselskii-Mann algorithm (IEM-PKMA), enhances positron emission tomography (PET) image reconstruction. This method accelerates convergence and prevents images from becoming stuck at zero, improving PET imaging quality.

    More Related Videos

    Functional Imaging of Brown Fat in Mice with 18F-FDG micro-PET/CT
    10:53

    Functional Imaging of Brown Fat in Mice with 18F-FDG micro-PET/CT

    Published on: November 23, 2012

    19.8K
    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
    07:05

    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

    Published on: February 15, 2022

    2.9K

    Related Experiment Videos

    Last Updated: Jan 28, 2026

    Visualization and Quantification of Brown and Beige Adipose Tissues in Mice using [18F]FDG Micro-PET/MR Imaging
    08:31

    Visualization and Quantification of Brown and Beige Adipose Tissues in Mice using [18F]FDG Micro-PET/MR Imaging

    Published on: July 1, 2021

    3.5K
    Functional Imaging of Brown Fat in Mice with 18F-FDG micro-PET/CT
    10:53

    Functional Imaging of Brown Fat in Mice with 18F-FDG micro-PET/CT

    Published on: November 23, 2012

    19.8K
    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
    07:05

    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

    Published on: February 15, 2022

    2.9K

    Area of Science:

    • Medical Imaging
    • Computational Science
    • Optimization

    Background:

    • Positron Emission Tomography (PET) image reconstruction is crucial for medical diagnostics.
    • Existing methods for higher-order total variation (HOTV) regularization face challenges with convergence and image artifacts.
    • The problem is formulated as a nonsmooth, three-term convex optimization model.

    Purpose of the Study:

    • To develop an efficient and accelerated algorithm for HOTV regularized PET image reconstruction.
    • To introduce an improved EM preconditioner (IEM-PKMA) to enhance the Krasnoselskii-Mann (KM) algorithm.
    • To address limitations of existing algorithms, specifically the issue of reconstructed images being 'stuck at zero'.

    Main Methods:

    • Formulation of the PET reconstruction as a three-term convex optimization problem (Kullback-Leibler fidelity, nonsmooth penalty, nonnegative constraints).
    • Development of a preconditioned Krasnoselskii-Mann (KM) algorithm incorporating an improved EM preconditioner (IEM-PKMA).
    • Utilizing a fixed-point characterization, momentum technique, and a novel EM preconditioner combining thresholding and solution estimation.

    Main Results:

    • The proposed IEM-PKMA algorithm demonstrates accelerated convergence compared to standard methods.
    • The IEM-PKMA effectively avoids the artifact of reconstructed images being 'stuck at zero'.
    • Numerical results show superior performance against state-of-the-art algorithms for both differentiable and nondifferentiable HOTV models.

    Conclusions:

    • The IEM-PKMA offers a significant advancement in PET image reconstruction, particularly for HOTV regularization.
    • The improved EM preconditioner is key to enhancing both speed and quality in PET imaging.
    • Initial experiments with clinical data show promising results, suggesting broad applicability.