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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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Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

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qMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks.

Huan Minh Luu¹, Dong-Hyun Kim¹, Jae-Woong Kim¹

¹Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea.

Magnetic Resonance in Medicine

|July 10, 2020

Summary

A new artificial neural network, quantitative magnetization transfer network (qMTNet), significantly accelerates quantitative magnetization transfer (qMT) imaging by speeding up both data acquisition and analysis. This advancement holds promise for clinical applications.

Keywords:

acceleration artificial neural network deep learning magnetization transfer quantitative imaging

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Area of Science:

Medical Imaging
Artificial Intelligence
Biophysics

Background:

Quantitative magnetization transfer (qMT) imaging is crucial for characterizing tissue microstructure.
Traditional qMT data acquisition and fitting are time-consuming, limiting clinical applicability.
Developing accelerated methods is essential for efficient qMT imaging.

Purpose of the Study:

To develop artificial neural networks, qMTNet, to accelerate quantitative magnetization transfer (qMT) imaging.
To create subnetworks for accelerating data acquisition (qMTNet-acq) and fitting (qMTNet-fit).
To integrate these networks into a single (qMTNet-1) or sequential (qMTNet-2) system for end-to-end acceleration.

Main Methods:

Acquired qMT data from multiple subjects for network development and validation.
Trained two subnetworks: qMTNet-acq for data acquisition and qMTNet-fit for parameter fitting.
Developed qMTNet-1 (integrated) and qMTNet-2 (sequential) for accelerated qMT parameter estimation from undersampled data.

Main Results:

The networks achieved high fidelity with peak signal-to-noise ratio >30 and structural similarity index >97 compared to ground truth.
qMTNet-fit outperformed traditional Gaussian kernel-based fitting.
qMTNet-1 and qMTNet-2 demonstrated significant acceleration: threefold for acquisition and 5800-fold/4218-fold for fitting, respectively.

Conclusions:

The developed qMTNet systems (qMTNet-1 and qMTNet-2) substantially accelerate the qMT imaging workflow.
qMTNet offers a promising solution for enhancing the efficiency of qMT imaging in clinical settings.
Further investigation is warranted to explore the full clinical potential of qMTNet.