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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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Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study.

Jiahao Huang^1,2,3, Pedro F Ferreira^4,5, Lichao Wang^4,6

¹National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK. j.huang21@imperial.ac.uk.

Scientific Reports

|March 7, 2024

Summary

This summary is machine-generated.

Deep learning models enhance in vivo cardiac diffusion tensor imaging (cDTI) reconstruction for clinical use. SwinMR is recommended for acceleration factors up to ×4, but higher factors require further development.

Keywords:

CNN Cardiac diffusion tensor Deep learning MRI reconstruction Transformer

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Area of Science:

Cardiovascular Imaging
Medical Image Reconstruction
Artificial Intelligence in Medicine

Background:

In vivo cardiac diffusion tensor imaging (cDTI) is a valuable MRI technique for assessing myocardial microstructure and cardiac function.
Clinical adoption of cDTI is hindered by technical challenges like low signal-to-noise ratio and long scan times.
Deep learning-based reconstruction offers a potential solution to accelerate cDTI acquisition.

Purpose of the Study:

To investigate and implement three deep learning-based MRI reconstruction models for cDTI.
To evaluate the performance of these models regarding reconstruction quality, diffusion tensor parameter accuracy, and computational cost.
To determine the feasibility of using these models for clinical cDTI at various acceleration factors (AF).

Main Methods:

Implementation of three distinct deep learning MRI reconstruction models.
Assessment of reconstruction quality using objective metrics.
Evaluation of diffusion tensor parameter accuracy and map quality at AF ×2, ×4, and ×8.
Analysis of computational efficiency for each model.

Main Results:

Deep learning models are suitable for clinical cDTI at AF ×2 and ×4.
The D5C5 model demonstrated superior reconstruction fidelity, while SwinMR offered higher perceptual scores.
At AF ×2 and ×4, diffusion tensor parameters showed no significant statistical difference from the reference, with acceptable map quality.
Model performance degraded significantly at AF ×8, with limited parameter recovery and potential for misleading results.

Conclusions:

Deep learning reconstruction models, particularly SwinMR, show promise for accelerating clinical cDTI at AF ×2 and ×4.
Current models are not yet ready for clinical application at higher acceleration factors (AF ×8) due to performance limitations.
Further research and development are needed to optimize deep learning models for higher acceleration factors in cDTI.