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 Experiment Videos

Improved parametric image generation using spatial-temporal analysis of dynamic PET studies.

Yun Zhou1, Sung-Cheng Huang, Marvin Bergsneider

  • 1The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institutions, Baltimore, MD 21287, USA.

Neuroimage
|February 19, 2002
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Predictors of hospital length of stay, discharge disposition, and readmission after craniopharyngioma surgery: a multicenter study from the RAPID database.

Neurosurgical focus·2026
Same author

Frailty and postoperative outcomes following craniopharyngioma resection: a multicenter Registry for Adenomas of the Pituitary and Related Disorders analysis.

Neurosurgical focus·2026
Same author

Risk factors for postoperative cerebrospinal fluid leak following endoscopic transsphenoidal surgery for craniopharyngioma: a multicenter cohort study with a contemporary surgeon practice survey.

Journal of neuro-oncology·2026
Same author

Benchmarking Surgical Outcomes for Endoscopic Transnasal Craniopharyngioma Resection: Implications for Clinical Practice From the RAPID Consortium.

Operative neurosurgery (Hagerstown, Md.)·2026
Same author

Adamantinomatous craniopharyngioma: outcomes from a US multicenter registry cohort (RAPID consortium study).

Journal of neurosurgery·2026
Same author

Kinetic Modeling of a Novel Putative Sphingosine-1-Phosphate Receptor 1 (S1PR1) Radiotracer [<sup>18</sup>F]TZ82112 in Nonhuman Primates.

Journal of neurochemistry·2026
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
See all related articles

A new nonlinear ridge regression with spatial constraint (NLRRSC) algorithm significantly improves parametric imaging for dynamic positron emission tomography (PET) by reducing noise and enhancing image quality compared to conventional methods.

Area of Science:

  • Medical Imaging
  • Quantitative Physiology
  • Computational Biology

Background:

  • Parametric images are valuable for visualizing tracer kinetics but are often degraded by noise in dynamic PET data.
  • Conventional weighted nonlinear least squares (WNLS) fitting struggles with the high noise levels in pixel-wise kinetic data.

Purpose of the Study:

  • To develop and evaluate a novel parametric imaging algorithm, nonlinear ridge regression with spatial constraint (NLRRSC), for dynamic PET.
  • To improve the quality of parametric images by integrating spatial and temporal information.

Main Methods:

  • Proposed NLRRSC algorithm, incorporating a spatial variation penalty into the WNLS cost function.
  • Utilized component representation model (CRM) with cluster analysis for initial estimates and spatial constraints.

Related Experiment Videos

  • Employed hierarchical clustering and a modified Gauss-Newton algorithm for optimization.

    • Main Results:

    • NLRRSC reduced the percent mean square error of estimates by 60-80% compared to WNLS in simulations.
    • Parametric images generated by NLRRSC demonstrated significantly improved quality over WNLS.
    • NLRRSC provided reliable glucose metabolite uptake rate estimates comparable to Patlak analysis.

    Conclusions:

    • NLRRSC is a robust and reliable parametric imaging algorithm for dynamic PET studies.
    • The algorithm effectively integrates spatial and temporal information to overcome noise limitations.