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

Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Deconvolution01:20

Deconvolution

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...
Upsampling01:22

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

You might also read

Related Articles

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

Sort by
Same author

Intraindividual cognitive variability predicts amyloid beta, tau PET, and dementia conversion in Down syndrome: a potential marker of cognitive resilience.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

Inflammation Associated With Obesity, Aging, and Amyloid Burden in Adults With Down Syndrome.

Obesity (Silver Spring, Md.)·2026
Same author

Circadian Rest-Activity Rhythms, Cognition, and Alzheimer Disease Dementia in Adults With Down Syndrome.

Neurology·2026
Same author

Longitudinal Evaluation of <sup>18</sup>F-MK-6240 Along the Alzheimer Disease Continuum.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same author

Neuropathological measures of increased tau phosphorylation across the Down syndrome lifespan.

Acta neuropathologica·2026
Same author

Longitudinal amyloid burden with combined [<sup>11</sup>C]PiB and [<sup>18</sup>F]NAV4694 PET scans.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Overall Survival with [<sup>177</sup>Lu]Lu-PSMA-617 Versus [<sup>177</sup>Lu]Lu-PSMA I&T: A Propensity Score-Matched Real-World Analysis.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same journal

Toward a Biopsy-Free Diagnosis of Prostate Cancer: Potential of Combined <sup>18</sup>F-Flotufolastat PSMA PET and mpMRI.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same journal

PSMA PET/CT-Targeted Biopsy in Men with Negative or Equivocal Multiparametric MRI and Exploratory Dynamic Total-Body PET: The FUPERMAN Study.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same journal

Erratum.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same journal

Live from 2026 SNMMI Annual Meeting in Los Angeles!

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same journal

CAR T-Cell Therapy for Cancer: Updates and Challenges for Response Assessment.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 12, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Dynamic PET denoising with HYPR processing.

Bradley T Christian1, Nicholas T Vandehey, John M Floberg

  • 1Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53705, USA. bchristian@wisc.edu

Journal of Nuclear Medicine : Official Publication, Society of Nuclear Medicine
|June 18, 2010
PubMed
Summary
This summary is machine-generated.

The modified HYPR-LR algorithm significantly enhances signal-to-noise ratio in dynamic PET imaging. This advanced technique improves PET time series analysis for better radiotracer visualization without compromising spatial resolution.

Related Experiment Videos

Last Updated: Jun 12, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Area of Science:

  • Medical Imaging
  • Nuclear Medicine
  • Image Processing

Background:

  • HighY constrained backPRojection (HYPR) is an effective image-processing technique for time-resolved MRI.
  • HYPR is suitable for positron emission tomography (PET) applications requiring time-series data.
  • The standard HYPR method creates a composite image from time-series data to improve signal-to-noise ratio (SNR).

Purpose of the Study:

  • To introduce and evaluate a modified HYPR algorithm, termed HYPR-LR (HYPR with local regions of interest), for dynamic PET studies.
  • To assess the performance of HYPR-LR in improving SNR in PET time-series data.
  • To demonstrate the potential of HYPR-LR for voxel-based analysis and visualization of radiotracer dynamics.

Main Methods:

  • Implementation of the HYPR-LR algorithm for processing dynamic PET data.
  • Performance evaluation using phantom, small-animal, and human studies.
  • Assessment through qualitative, semiquantitative, and quantitative comparisons.

Main Results:

  • The HYPR-LR algorithm significantly improved the SNR in PET time-series data.
  • Spatial resolution was maintained while enhancing SNR, particularly for voxel-based analysis.
  • Demonstrated successful application in phantom, small-animal, and human PET studies.

Conclusions:

  • HYPR-LR processing offers substantial SNR improvements for dynamic PET scans.
  • This method is highly beneficial for nuclear medicine imaging, especially for low-SNR applications.
  • HYPR-LR facilitates the generation of parametric images and visualization of rapid radiotracer uptake and distribution.