Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Deconvolution

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...

You might also read

Related Articles

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

Sort by

Same author

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

IEEE transactions on medical imaging·2026

Same authorSame journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026

Same author

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution.

Advances in neural information processing systems·2026

Same author

Mitochondrial quality control in health and disease: Updates 2026.

Chinese medical journal·2026

Same author

PD-1 H (VISTA) drives immunosuppressive reprogramming of glioma-associated myeloid cells to promote glioma progression.

Journal of translational medicine·2026

Same author

Seeing is believing: visualizing spatiotemporal lipophagic flux in vivo with the tfLiveDrop reporter mice.

Autophagy reports·2026

Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026

Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026

Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026

Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026

Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 6, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Unsupervised PET logan parametric image estimation using conditional deep image prior.

Jianan Cui¹, Kuang Gong², Ning Guo²

¹The State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China; The Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital/Harvard Medical School, Boston MA 02114, USA.

Medical Image Analysis

|June 29, 2022

Summary

This summary is machine-generated.

This study introduces a new deep learning method combining conditional deep image prior (CDIP) with Logan analysis for enhanced Positron Emission Tomography (PET) image denoising and parametric imaging. The method improves image quality and detail without needing extensive training data.

Keywords:

Deep image prior Logan plot PET Parametric image estimation Unsupervised learning

More Related Videos

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

Related Experiment Videos

Last Updated: Sep 6, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

Published on: August 6, 2013

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Human Brown Adipose Tissue Depots Automatically Segmented by Positron Emission Tomography/Computed Tomography and Registered Magnetic Resonance Images

Published on: February 18, 2015

Area of Science:

Medical Imaging
Artificial Intelligence
Quantitative Analysis

Background:

Deep learning methods are increasingly used for Positron Emission Tomography (PET) image denoising.
Unsupervised methods like conditional deep image prior (CDIP) offer advantages by not requiring prior training or large datasets.
Accurate parametric imaging is crucial for quantitative analysis in PET studies.

Purpose of the Study:

To develop a novel method for generating high-quality parametric images from PET data.
To combine the strengths of CDIP with the Logan reference tissue model for unsupervised PET image analysis.
To improve the contrast-to-noise ratio (CNR) and structural detail in parametric PET images.

Main Methods:

Integrated CDIP with the Logan reference tissue model to estimate parametric images (Logan slope and intercept).
Utilized patient's CT or MR images as anatomical input for the neural network.
Employed the alternating direction method of multipliers (ADMM) algorithm to solve the optimization function.
Validated the method using both simulated and clinical PET/CT and brain PET datasets.

Main Results:

The proposed method successfully generated parametric images with enhanced structural details compared to traditional methods.
Significant improvements in contrast-to-noise ratio (CNR) were observed: 62.25% for PET/CT, 129.51% for brain striatum, and 128.24% for brain thalamus.
Outperformed Gaussian filtering and nonlocal mean (NLM) denoising in CNR improvement across all tested datasets.

Conclusions:

The combined CDIP and Logan analysis approach provides a powerful unsupervised method for high-quality PET parametric imaging.
This technique offers superior denoising and quantitative accuracy, particularly for brain imaging applications.
The method demonstrates potential for advancing quantitative PET analysis without the need for arterial sampling or extensive training data.