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

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

You might also read

Related Articles

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

Sort by

Same author

Noise2Noise Diffusion for Thin-Slice Brain CT Denoising without Clean Training Data.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Same author

Assessing imaging performance of ultrasound systems using a random hypoechoic sphere phantom with freehand scanning.

Medical physics·2026

Same author

Diffusion Model-Based Motion Correction in Portable Computed Tomography for Brain: A Human Observer Study.

Academic radiology·2026

Same author

Overview of Diagnostic Ultrasound Safety: Review for Research and Institutional Review Boards.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2025

Same author

Clinical applications of portable CT scanners.

The British journal of radiology·2025

Same author

Task based evaluation of sparse view CT reconstruction techniques for intracranial hemorrhage diagnosis using an AI observer model.

Scientific reports·2025

Same journal

Hybrid Spectral CT: Combining rapid kVp-switching and dual-layer detectors for high sensitivity iodine imaging.

Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography·2025

Same journal

Optimizing Conditional DDPM for Head CT Motion Artifact Reduction: Brain vs. Skull and 3D vs. 2D.

Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography·2025

Same journal

Long-term quantitative stability of a first-generation dual-source photon-counting CT.

Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography·2025

Same journal

Quantitative metal artifact reduction algorithm for spectral CT thermometry.

Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography·2025

Same journal

Spectral quantification in different lumen diameters for cardiovascular applications.

Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography·2025

Same journal

Double bowtie design for high sensitivity pediatric spectral CT.

Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography·2025

See all related articles

Search research articles

Related Experiment Video

Updated: May 16, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Noise Controlled CT Super-Resolution with Conditional Diffusion Model.

Yuang Wang^1,2, Siyeop Yoon¹, Rui Hu¹

¹Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston MA 02114, USA.

Conference Proceedings. International Conference on Image Formation in X-Ray Computed Tomography

|April 4, 2025

Summary

This summary is machine-generated.

This study presents a novel conditional diffusion model for CT image super-resolution, effectively controlling noise amplification. The method enhances spatial resolution in CT scans using hybrid training data, proving effective in real-world applications.

Keywords:

Conditional Diffusion Model Noise Controlling Super-Resolution

More Related Videos

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Related Experiment Videos

Last Updated: May 16, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

Medical Imaging
Artificial Intelligence
Image Processing

Background:

Improving spatial resolution in CT images is crucial but challenging.
Noise amplification often accompanies super-resolution techniques in CT imaging.

Purpose of the Study:

To introduce an innovative framework for noise-controlled CT super-resolution.
To leverage conditional diffusion models for enhanced CT image quality.

Main Methods:

Developed a conditional diffusion model for CT super-resolution.
Trained the model on hybrid datasets: noise-matched simulations and real segmented details.
Validated the framework using real CT images.

Main Results:

The proposed framework effectively improves spatial resolution in CT images.
Noise amplification is successfully controlled during the super-resolution process.
Experimental results demonstrate the framework's effectiveness on real CT data.

Conclusions:

The conditional diffusion model offers a promising approach for noise-controlled CT super-resolution.
The framework shows significant potential for practical applications in CT imaging.
This method addresses key challenges in enhancing CT image resolution and quality.