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

Positron Emission Tomography Imaging of the Human Brain Using a Radiotracer02:18

Positron Emission Tomography Imaging of the Human Brain Using a Radiotracer

425
Source: Jamadar, S. et. al. Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET. J. Vis. Exp. (2019)This video demonstrates the quantification of brain glucose metabolism using positron emission tomography (PET). The participant is infused with the tracer F-18 fluorodeoxyglucose (FDG) during scanning. FDG accumulates in active neurons and emits positrons upon F-18 decay. These positrons interact with electrons,...
425
Positron Emission Tomography01:29

Positron Emission Tomography

7.0K
Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...
7.0K
Imaging Studies II: Positron Emission Tomography and Scintigraphy01:25

Imaging Studies II: Positron Emission Tomography and Scintigraphy

510
Positron Emission Tomography (PET) is a medical imaging technique that provides crucial insights into the body's physiological functions at a molecular level. It is an indispensable resource for diagnosing, staging, and monitoring various illnesses, notably cancer, neurological disorders, and cardiovascular conditions.
Fundamental Principles of PET
510
18F-Labeling of Radiotracers Functionalized with a Silicon Fluoride Acceptor (SiFA) for Positron Emission Tomography09:57

18F-Labeling of Radiotracers Functionalized with a Silicon Fluoride Acceptor (SiFA) for Positron Emission Tomography

8.0K
The synthesis of fluorine-18 (18F) labeled radiopharmaceuticals for positron emission tomography typically requires months of experience. When incorporated into a radiotracer, the silicon-fluoride acceptor (SiFA) motif enables a simple 18F-labeling protocol that is independent of costly equipment and preparatory training, while reducing precursor quantity needed and utilizing milder reaction...
8.0K
Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function10:21

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function

8.8K
Here a protocol for continuous blood sampling during PET/CT imaging of rats to measure the arterial input function (AIF) is described. The catheterization, the calibration and setup of the system and the data analysis of the blood radioactivity are demonstrated. The generated data provide input parameters for subsequent bio-kinetic...
8.8K
Non-invasive Imaging and Analysis of Cerebral Ischemia in Living Rats Using Positron Emission Tomography with 18F-FDG10:31

Non-invasive Imaging and Analysis of Cerebral Ischemia in Living Rats Using Positron Emission Tomography with 18F-FDG

14.3K
Brain damage resulting from cerebral ischemia may be non-invasively imaged and studied in rats using pre-clinical positron emission tomography coupled with the injectable radioactive probe, 18F-fluorodeoxyglucose. Further, the use of modern software tools that include volume of interest (VOI) brain templates dramatically increase the quantitative information gleaned from these...
14.3K

You might also read

Related Articles

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

Sort by
Same author

Bio-SCOPE: fast biexponential T<sub>1ρ</sub> mapping of the brain using signal-compensated low-rank plus sparse matrix decomposition.

Magnetic resonance in medicine·2019
Same author

Enantioselective Radical Ring-Opening Cyanation of Oxime Esters by Dual Photoredox and Copper Catalysis.

Organic letters·2019
Same author

ACCELERATING MAGNETIC RESONANCE IMAGING VIA DEEP LEARNING.

Proceedings. IEEE International Symposium on Biomedical Imaging·2019
Same author

Technical note: Development and application of KASP assays for rapid screening of 8 genetic defects in Holstein cattle.

Journal of dairy science·2019
Same author

Sesquiterpenes and diterpenes from Euphorbia thymifolia.

Fitoterapia·2019
Same author

Glechomanamides A-C, Germacrane Sesquiterpenoids with an Unusual Δ<sup>8</sup>-7,12-Lactam Moiety from <i>Salvia scapiformis</i> and Their Antiangiogenic Activity.

Journal of natural products·2019

Related Experiment Video

Updated: Jan 19, 2026

Positron Emission Tomography Imaging of the Human Brain Using a Radiotracer
02:18

Positron Emission Tomography Imaging of the Human Brain Using a Radiotracer

Published on: June 17, 2025

425

Image reconstruction for positron emission tomography based on patch-based regularization and dictionary learning.

Wanhong Zhang1,2, Juan Gao1, Yongfeng Yang1

  • 1Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.

Medical Physics
|September 9, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel patch-based dictionary learning (DL) algorithm for positron emission tomography (PET) image reconstruction. The patch-DL algorithm effectively suppresses noise while preserving crucial image details, enhancing diagnostic accuracy in nuclear medicine.

Keywords:
dictionary learningimage reconstructionpatch regularizationpositron emission tomography

More Related Videos

18F-Labeling of Radiotracers Functionalized with a Silicon Fluoride Acceptor SiFA for Positron Emission Tomography
09:57

18F-Labeling of Radiotracers Functionalized with a Silicon Fluoride Acceptor SiFA for Positron Emission Tomography

Published on: January 11, 2020

8.0K
Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function
10:21

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function

Published on: August 8, 2019

8.8K

Related Experiment Videos

Last Updated: Jan 19, 2026

Positron Emission Tomography Imaging of the Human Brain Using a Radiotracer
02:18

Positron Emission Tomography Imaging of the Human Brain Using a Radiotracer

Published on: June 17, 2025

425
18F-Labeling of Radiotracers Functionalized with a Silicon Fluoride Acceptor SiFA for Positron Emission Tomography
09:57

18F-Labeling of Radiotracers Functionalized with a Silicon Fluoride Acceptor SiFA for Positron Emission Tomography

Published on: January 11, 2020

8.0K
Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function
10:21

Continuous Blood Sampling in Small Animal Positron Emission Tomography/Computed Tomography Enables the Measurement of the Arterial Input Function

Published on: August 8, 2019

8.8K

Area of Science:

  • Nuclear Medicine
  • Medical Imaging
  • Computational Science

Background:

  • Positron emission tomography (PET) is vital for medical imaging but often suffers from noisy reconstructed images due to low count rates and detector noise.
  • Image quality in PET is frequently compromised, leading to ill-conditioned images and hindering accurate clinical diagnosis and research.
  • Developing advanced algorithms for high-quality PET image reconstruction is crucial for improving diagnostic capabilities.

Purpose of the Study:

  • To present a novel image reconstruction algorithm, patch-based dictionary learning (patch-DL), for improving PET image quality.
  • To address the challenge of noise in PET imaging while retaining essential image details.
  • To offer a superior alternative to existing reconstruction methods for enhanced clinical utility.

Main Methods:

  • The patch-DL algorithm integrates Expectation-Maximization (EM)-like updates, image smoothing, pixel fusion, and dictionary learning.
  • A 2D brain phantom was simulated with Poisson noise to generate sinogram data for algorithm evaluation.
  • The patch-DL algorithm was quantitatively compared against pixel-based, patch-based, and adaptive dictionary learning (AD) algorithms.

Main Results:

  • Computer simulations demonstrated that patch-DL excels in balancing noise suppression and detail retention compared to pixel-, patch-, and AD-based methods.
  • Quantitative analysis using MAE, CORR, and RMSE metrics showed statistically significant performance improvements in simulated regions of interest.
  • One-way ANOVA confirmed significant differences (P < 0.01 or P < 0.05) between patch-DL and traditional methods, highlighting its superior quantitative performance.

Conclusions:

  • The proposed patch-DL algorithm shows significant potential for enhancing the quality of PET image reconstruction.
  • Further validation with real 3D PET data is necessary to fully establish the algorithm's clinical applicability.
  • Future work will focus on optimizing computational efficiency through GPU parallelization for faster reconstruction times.