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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: May 24, 2025

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data.

Guoyao Shen^1,2, Yancheng Zhu¹, Mengyu Li^1,2

¹Department of Mechanical Engineering, Boston University, Boston, MA 02215, USA.

|March 4, 2025

Summary

This summary is machine-generated.

Regularization by Neural Style Transfer (RNST) reconstructs high-quality MRI images from low-field data without paired training. This novel method enhances clarity and contrast, offering a data-efficient solution for limited-data settings.

Keywords:

MRI deep learning image reconstruction neural style transfer regularization by denoising

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Area of Science:

Medical Imaging
Artificial Intelligence
Deep Learning

Background:

Deep learning significantly advanced MRI reconstruction but requires large, specific datasets.
Reconstruction in data-limited scenarios remains a critical challenge.
Existing methods like regularization by denoising (RED) use denoisers as priors.

Purpose of the Study:

To introduce Regularization by Neural Style Transfer (RNST) for MRI field-transfer reconstruction.
To address the challenge of data-limited settings in MRI reconstruction.
To generate high-field-quality MRI images from low-field inputs without paired training data.

Main Methods:

Integrated a neural style transfer (NST) engine with a denoiser.
Developed a novel framework named RNST.
Leveraged style priors from NST to overcome data limitations.

Main Results:

RNST successfully reconstructed high-quality MRI images across axial, coronal, and sagittal planes.
Achieved superior image clarity, contrast, and structural fidelity compared to low-field references.
Demonstrated robustness even with imperfect alignment between style and content images.

Conclusions:

RNST offers a scalable and data-efficient solution for MRI field-transfer reconstruction.
The framework shows significant potential for resource-limited clinical settings.
RNST enables high-field-quality image generation without requiring large, task-specific datasets.