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Related Concept Videos

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
Positron Emission Tomography01:29

Positron Emission Tomography

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.
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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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.
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Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
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Stereotype Content Model

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Related Experiment Video

Updated: Jun 24, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

A plug-and-play method for guided multi-contrast MRI reconstruction based on content/style modeling.

Chinmay Rao1, Matthias van Osch1, Nicola Pezzotti2

  • 1Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.

Medical Image Analysis
|June 22, 2026
PubMed
Summary

This study introduces PnP-CoSMo, a novel method for reconstructing undersampled MRI contrasts using image-domain data, eliminating the need for k-space training. This approach enhances MRI reconstruction quality and generalizability.

Keywords:
Content/style decompositionMRI reconstructionMulti-modalPlug-and-play

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Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI) Reconstruction
  • Computational Imaging

Background:

  • Undersampled MRI contrasts contain redundant information, enabling guided reconstruction using other contrasts.
  • Existing end-to-end deep learning methods require large paired k-space and image-domain datasets for training.
  • A significant challenge is the need for extensive, aligned multi-contrast MRI data.

Purpose of the Study:

  • To develop a novel, modular, plug-and-play MRI reconstruction method that does not require k-space training data.
  • To leverage partially paired image-domain datasets for guided reconstruction.
  • To enable cross-contrast generalizability and provide an interpretable framework for MRI reconstruction.

Main Methods:

  • A content/style model is learned from image-domain data, disentangling contrast-independent and contrast-specific factors.
  • The learned model is used as a plug-and-play operator within an iterative reconstruction framework.
  • The method combines content consistency, data consistency, and a corrective procedure, termed PnP-CoSMo.

Main Results:

  • PnP-CoSMo demonstrates equivalent or superior image quality and greater generalizability compared to end-to-end methods on the NYU fastMRI dataset.
  • Achieved up to 32.6% greater acceleration on in-house multi-coil datasets compared to non-guided reconstruction.
  • Simulations confirm the interpretability and convergence properties of the PnP-CoSMo approach.

Conclusions:

  • PnP-CoSMo offers a practical and effective solution for guided MRI reconstruction using only image-domain data.
  • The method overcomes the data requirements of traditional deep learning approaches.
  • PnP-CoSMo provides a generalizable and interpretable framework for accelerating MRI acquisition.