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3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

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MFAN: Multi-scale Feature Aggregation Network for Brain MRI Image Super-Resolution.

Abdulhamid Muhammad, Supavadee Aramvith, Titipat Achakulvisut

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 3, 2025

Summary

A new Multi-scale Feature Aggregation Network (MFAN) enhances brain MRI super-resolution by effectively aggregating multi-scale features. This advancement improves diagnostic accuracy in neuroimaging.

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

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Published on: May 10, 2012

Area of Science:

Medical imaging
Artificial intelligence
Neuroscience

Background:

Magnetic resonance imaging (MRI) is crucial for diagnosing brain conditions.
Current MRI super-resolution methods struggle with aggregating multi-scale textural details and high-frequency information.
Accurate reconstruction is vital for reliable clinical diagnosis and application.

Purpose of the Study:

To propose a novel Multi-scale Feature Aggregation Network (MFAN) for brain MRI image super-resolution.
To address the challenge of effectively aggregating multi-scale features and high-frequency information for improved MRI reconstruction.
To enhance the reliability of clinical diagnoses through advanced image super-resolution.

Main Methods:

Developed a Multi-scale Feature Aggregation Network (MFAN) for brain MRI super-resolution.
Incorporated Channel and Spatial Attention (CSA) mechanisms for shallow feature extraction.
Introduced a Multi-scale Feature Aggregation Attention Block (MFAAB) for fusing diverse multi-pathway features.

Main Results:

MFAN demonstrated superior performance compared to state-of-the-art methods on BraTS 2018 and Brain Tumor datasets.
Achieved PSNR improvements of 1.054 dB and 0.609 dB at ×2 and ×4 magnifications on the BraTS 2018 dataset.
Reported SSIM gains of 0.0128 and 0.0059 at ×2 and ×4 magnifications, respectively.

Conclusions:

MFAN significantly advances brain MRI super-resolution, addressing key challenges in clinical neuroimaging.
The network improves diagnostic precision by aggregating multiscale textural information and enhancing structural details.
MFAN offers a potential solution for more reliable detection and diagnosis, reducing the need for repeated scans or high-field MRI systems.