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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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Related Experiment Video

Updated: May 10, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Rosette Trajectory MRI Reconstruction with Vision Transformers.

Muhammed Fikret Yalcinbas¹, Cengizhan Ozturk^1,2, Onur Ozyurt³

¹Institute of Biomedical Engineering, Boğaziçi University, Istanbul 34684, Turkey.

Tomography (Ann Arbor, Mich.)

|April 25, 2025

Summary

This summary is machine-generated.

This study introduces an efficient pipeline for magnetic resonance imaging reconstruction using a vision transformer (ViT) network. The novel method enhances image quality and runtime performance for non-Cartesian data.

Keywords:

MRI machine learning medical imaging

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Reconstructing high-quality magnetic resonance imaging (MRI) from non-Cartesian data presents significant challenges.
Existing methods often require extensive preprocessing or struggle with complex spatial dependencies.

Purpose of the Study:

To develop an efficient and effective pipeline for rosette trajectory MRI reconstruction.
To leverage the capabilities of vision transformer (ViT) networks for improved image fidelity.

Main Methods:

A hybrid approach combining the inverse fast Fourier transform (iFFT) with a convolutional-enhanced vision transformer (ViT) network.
The iFFT provides an initial approximation, refined by the ViT for high-fidelity image generation.

Main Results:

The proposed method demonstrates superior performance compared to established deep learning techniques.
Achieved better normalized root mean squared error (NRMSE), peak signal-to-noise ratio (PSNR), and entropy-based image quality scores.
Exhibited improved runtime performance.

Conclusions:

The developed pipeline offers an efficient and high-quality solution for non-Cartesian MRI reconstruction.
The integration of iFFT and ViT network provides a robust framework for complex MRI data.
This approach sets a new benchmark for deep learning-based MRI reconstruction.