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Magnetic Resonance Imaging

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...

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Updated: Jul 5, 2025

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

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling.

Chih-Wei Chang¹, Junbo Peng¹, Mojtaba Safari¹

¹Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.

Physics in Medicine and Biology

|January 19, 2024

Summary

This summary is machine-generated.

This study introduces a deep learning framework using denoising diffusion probabilistic models (DDPM) to generate high-resolution MRI scans from low-resolution images, enhancing image quality without increasing scan time.

Keywords:

MRI deep learning diffusion model high-resolution imaging image synthesis

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Last Updated: Jul 5, 2025

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

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Published on: November 8, 2012

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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

Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Deep Learning for Image Reconstruction

Background:

High-resolution magnetic resonance imaging (MRI) is crucial for accurate lesion diagnosis and delineation.
Current limitations in gradient power and hardware restrict MRI resolution (sub-1 mm slices).
Long MRI scan times are clinically unacceptable, hindering the acquisition of high-resolution images.

Purpose of the Study:

To develop a framework for generating high-resolution MRI from low-resolution images using diffusion probabilistic deep learning.
To improve the uncertainty and quality of denoising diffusion probabilistic models (DDPM) for MRI super-resolution.
To overcome the limitations of conventional methods in capturing complex, high-dimensional image data.

Main Methods:

A diffusion probabilistic deep learning framework utilizing denoising diffusion probabilistic models (DDPM) was developed.
The forward process involved systematically adding Gaussian noise to low-resolution MRI images.
The reverse process trained a U-Net model to denoise images and generate high-resolution outputs conditioned on low-resolution counterparts, tested on prostate and brain MRI datasets (BraTS2020).

Main Results:

The proposed DDPM framework improved noise quality by 12.8% for prostate MRI, outperforming Bicubic (4.4%) and CGAN (5.7%).
Signal-to-noise ratios were enhanced by 11.7% with DDPM, exceeding Bicubic (9.8%) and CGAN (8.1%).
For BraTS2020 data, DDPM achieved a peak signal-to-noise ratio enhancement of 9.1%, with high multi-scale structural similarity (0.970 ± 0.019).

Conclusions:

The developed deep learning-based diffusion probabilistic framework effectively enhances MRI resolution.
This approach enables the acquisition of high-resolution MRI images without extending scan times, potentially improving clinical workflows.
Future research will focus on prospectively validating the framework's efficacy across various clinical indications.