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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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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks.

Kimberly Helm¹, Tejas Sudharshan Mathai¹, Boah Kim¹

¹Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.

|March 26, 2024

Summary

This study introduces an automated method to classify 3D Magnetic Resonance Imaging (MRI) sequences in the chest, abdomen, and pelvis. Our AI model accurately identifies MRI sequences, reducing the need for manual review in clinical studies and AI development.

Keywords:

Body Classification Deep Learning MRI Multi-parametric

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Area of Science:

Radiology and Medical Imaging
Artificial Intelligence in Healthcare
Medical Informatics

Background:

Multi-parametric MRI is crucial for disease diagnosis, but lacks standardized naming conventions for protocols and sequences.
Variations in imaging practices and MRI scanner manufacturers lead to differing intensity distributions and conflicting DICOM header information.
Current reliance on clinician oversight for accurate sequence identification hinders large-scale clinical studies and AI algorithm development.

Approach:

Developed an automated 3D DenseNet-121 model for classifying MRI sequences in the chest, abdomen, and pelvis.
The model was trained and validated on data from three Siemens MRI scanners.
Evaluated the model's performance in differentiating five common MRI sequences.

Key Points:

Achieved a 99.5% F1 score in differentiating common MRI sequences.
Demonstrated the model's effectiveness across different Siemens scanner models.
Established a novel automated method for 3D MRI sequence classification.

Conclusions:

The proposed automated method significantly reduces the need for manual clinician oversight in MRI sequence identification.
This approach ensures the validity of DICOM headers, crucial for clinical research and AI applications.
Outperformed previous state-of-the-art methods for MRI series classification.