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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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Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

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MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study.

Joshua Shur¹, Matthew Blackledge², James D'Arcy²

¹Department of Radiology, The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, London, Surrey, SM2 5PT, UK.

European Radiology Experimental

|January 19, 2021

Summary

Most magnetic resonance imaging (MRI) texture features show high repeatability in phantom studies. Feature robustness varies with acquisition parameters, guiding selection for clinical validation.

Keywords:

Magnetic resonance imaging Phantoms (imaging)Radiomics Reproducibility of results Texture analysis

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Area of Science:

Medical Imaging
Radiology
Quantitative Imaging

Background:

Texture analysis in MRI is crucial for quantitative imaging.
Evaluating the reliability of MRI texture features is essential for clinical applications.
Phantom studies provide a controlled environment to assess feature repeatability and robustness.

Purpose of the Study:

To assess the repeatability and robustness of magnetic resonance imaging (MRI) texture features.
To evaluate feature performance using both phantom test-retest and in silico experiments.
To classify MRI texture features based on their reliability under varying acquisition conditions.

Main Methods:

A test-retest study using water and tissue phantoms on a 1.5-T scanner.
Extraction of 46 texture features, including first and second-order statistics.
In silico manipulation of image noise and resolution to assess feature robustness.
Calculation of concordance correlation coefficient for repeatability and coefficient of variation for robustness.

Main Results:

Eighty percent of MRI texture features demonstrated high repeatability (concordance correlation coefficient > 0.9).
Approximately 90% of features showed strong or intermediate correlation with image acquisition parameters.
41% of features were highly robust to noise (CV < 5%) and 28% to resolution (CV < 5%).

Conclusions:

The majority of MRI texture features exhibit excellent repeatability in phantom studies.
Phantom data can establish a baseline for MRI feature repeatability.
Feature robustness is dependent on acquisition parameters, informing the selection of features for clinical studies.