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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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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

SPINNED: Simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast MRI

Muhammad Asaduddin¹, Eung Yeop Kim², Sung-Hong Park¹

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

Magnetic Resonance in Medicine

|April 16, 2024

Summary

A new simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) offers more accurate and robust results than existing methods. SPINNED also provides faster processing speeds for improved clinical diagnoses.

Keywords:

DSC MRI deconvolution perfusion maps physics informed neural network synthetic data

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

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Published on: November 8, 2012

Area of Science:

Medical Imaging
Artificial Intelligence
Neurosurgery

Background:

Dynamic susceptibility contrast (DSC) MRI is crucial for assessing brain perfusion.
Accurate deconvolution is essential for quantifying DSC-MRI parameters.
Existing deconvolution methods face challenges with accuracy and robustness.

Purpose of the Study:

To introduce the simulation-based physics-informed neural network for deconvolution of DSC-MRI (SPINNED).
To establish SPINNED as a robust and accurate alternative to current deconvolution techniques.
To enhance the reliability of DSC-MRI data analysis.

Main Methods:

Developed SPINNED using synthetic DSC-MRI data generated from simulated tissue residue and arterial input functions.
Trained the SPINNED model to learn the deconvolution relationship within DSC-MRI data.
Validated SPINNED against circulant and Volterra singular value decomposition using simulated and real patient data.

Main Results:

SPINNED demonstrated superior accuracy across all signal-to-noise ratio (SNR) levels compared to conventional methods.
The method exhibited enhanced robustness against noise in both simulated and real-world patient data.
SPINNED achieved significantly faster processing speeds than existing deconvolution techniques.

Conclusions:

SPINNED presents a viable alternative for DSC-MRI deconvolution, outperforming current approaches.
The method eliminates the need for separate ground-truth measurements, simplifying training.
SPINNED offers faster processing and broader clinical applicability, promising more reliable and rapid diagnoses.