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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

809
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...
809
Deconvolution01:20

Deconvolution

655
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
655
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

531
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
531
Computed Tomography01:10

Computed Tomography

9.2K
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...
9.2K
Reducing Line Loss01:18

Reducing Line Loss

429
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
429
Bone Remodeling01:40

Bone Remodeling

40.8K
Bone remodeling is a continuous and balanced process of bone resorption by osteoclasts and bone formation by osteoblasts. In adults, it helps maintain bone mass and calcium homeostasis. While mechanical stress can stimulate turnover as part of the normal maintenance and reparative process, several hormones also regulate bone remodeling.
40.8K

You might also read

Related Articles

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

Sort by
Same author

Lipid droplet size profiling in yeast.

Biology open·2026
Same author

Zero-Shot Self-Supervised Learning of Single Breath-Hold Magnetic Resonance Cholangiopancreatography (MRCP) Reconstruction.

Magnetic resonance in medicine·2026
Same author

L-TGVN: Leveraging Longitudinal Priors for Personalized Rapid MRI.

ArXiv·2026
Same author

Vitabel: A Python Framework for Visualizing and Labelling High-Resolution Physiological Data for Critical Care Machine Learning.

Journal of medical systems·2026
Same author

CBCT-based synthetic MRI generation for target localization during deep inspiration breath hold (DIBH) abdominal radiotherapy.

Physics in medicine and biology·2026
Same author

SELFIE: Self-Supervised Learning for Fast Dynamic Golden-Angle Radial MRI.

NMR in biomedicine·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Mar 9, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

233

Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer.

Florian Knoll1, Martin Holler2, Thomas Koesters1

  • 1Bernard and Irene Schwartz Center for Biomedical Imaging, and the Center for Advanced Imaging Innovation and Research (CAIR), in the Department of Radiology at NYU School of Medicine, New York, NY, United States.

IEEE Transactions on Medical Imaging
|January 6, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for joint MR-PET image reconstruction. The method improves Positron Emission Tomography (PET) image quality and accuracy by sharing anatomical information from Magnetic Resonance (MR) imaging during reconstruction.

More Related Videos

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.1K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.5K

Related Experiment Videos

Last Updated: Mar 9, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

233
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

1.1K
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.5K

Area of Science:

  • Medical Imaging
  • Image Reconstruction
  • Multimodal Imaging

Background:

  • Current MR-PET scanners acquire simultaneous data but often reconstruct modalities separately.
  • Separate reconstruction limits the full potential of integrated MR-PET systems.
  • Sharing information between modalities can enhance image quality and quantitative accuracy.

Purpose of the Study:

  • To develop a novel multimodal reconstruction framework for simultaneous MR-PET data.
  • To leverage anatomical information from MR to improve PET image reconstruction.
  • To investigate the benefits of joint reconstruction using second-order Total Generalized Variation (TGV).

Main Methods:

  • Proposed a new multi-modality reconstruction framework.
  • Utilized second-order Total Generalized Variation (TGV) as a multi-channel regularization functional.
  • Jointly reconstructed MR and PET data, enabling information sharing.

Main Results:

  • Demonstrated improved PET image quality, resolution, and quantitative accuracy.
  • Results validated through numerical simulations and in-vivo experiments.
  • The framework showed benefits across various accelerated MR acquisitions and contrasts.

Conclusions:

  • The proposed joint reconstruction framework effectively integrates MR and PET data.
  • Sharing anatomical information enhances PET image reconstruction.
  • This approach offers improved performance for simultaneous MR-PET imaging.