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Magnetic Resonance Imaging

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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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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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GAN-MRI enhanced multi-organ MRI segmentation: a deep learning perspective.

Arvind Channarayapatna Srinivasa¹, Seema S Bhat², Dikendra Baduwal³

¹Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Republic of Singapore. arvindcs@bii.a-star.edu.sg.

Radiological Physics and Technology

|August 8, 2025

Summary

This summary is machine-generated.

This study introduces an AI framework to enhance MRI image quality and segmentation accuracy, significantly improving diagnostic precision and reducing scan times for better patient comfort and treatment planning.

Keywords:

Attention-residual U-net Deep learning Generative adversarial networks (GAN)Magnetic resonance imaging (MRI)Quick scan

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

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

Published on: January 7, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Clinical magnetic resonance imaging (MRI) provides high-resolution anatomical detail but suffers from long scan times, leading to motion artifacts and patient discomfort.
Fast MRI techniques compromise image quality (noise, low contrast), impacting segmentation accuracy crucial for diagnosis and treatment.
Existing methods struggle with data variability from different MRI scanners and centers.

Purpose of the Study:

To develop an end-to-end framework for enhancing MRI image quality and improving segmentation accuracy across various anatomies and scanner types.
To reduce MRI scan times while maintaining or improving diagnostic information and patient comfort.

Main Methods:

An integrated framework combining a BIDS-based data organizer/anonymizer, a Generative Adversarial Network (GAN) for MR image enhancement (GAN-MRI), AssemblyNet for brain segmentation, and an attention-residual U-Net with Guided loss for abdominal/thigh segmentation.
Utilized 30 brain, 32 abdominal, and 55 thigh MRI scans from GE, Siemens, and Toshiba scanners for evaluation.

Main Results:

Significant improvements in Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) for brain and abdominal scans (e.g., brain SNR increased from 28.44 to 42.92, p < 0.001).
Substantial gains in segmentation accuracy for thigh (muscle +21%, IMAT +9%) and abdominal regions (VAT +12%).
Improved visualization of anatomical structures and bias field correction, with stable brain segmentation metrics.

Conclusions:

The proposed framework effectively enhances MRI quality and segmentation accuracy across diverse anatomies and scanner variations.
This approach promises to improve diagnostic precision and treatment planning by reducing scan times and enhancing patient comfort.
The framework's adaptability to multi-center, multi-scanner data makes it a robust solution for clinical applications.