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Related Experiment Video

Updated: Jan 17, 2026

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

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

Published on: November 8, 2012

LearnDiff: MRI image super-resolution using a diffusion model with learnable noise.

Sagnik Goswami¹, Akriti Gupta¹, Angshuman Paul¹

¹Indian Institute of Technology Jodhpur, NH 62, Karwar, Jodhpur, 342037, Rajasthan, India.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|September 13, 2025

Summary

This summary is machine-generated.

LearnDiff, a novel diffusion model, enhances magnetic resonance imaging (MRI) super-resolution by using learnable noise. This approach significantly improves image quality and diagnostic accuracy compared to traditional methods.

Keywords:

Diffusion model Gaussian distribution Learnable noise MRI super-resolution

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Area of Science:

Medical Imaging
Artificial Intelligence
Image Processing

Background:

High spatial resolution in MRI is crucial for accurate and rapid diagnosis.
Standard diffusion models often use fixed noise distributions, which may be suboptimal for MRI super-resolution.

Purpose of the Study:

To introduce LearnDiff, a diffusion probabilistic model designed for MRI super-resolution.
To enhance MRI image quality and diagnostic capabilities through adaptive noise modeling.

Main Methods:

Developed LearnDiff, a diffusion model incorporating a learnable Gaussian distribution in its bottleneck.
Implemented dynamic adaptation of both forward and reverse diffusion processes.
Applied a residual approach for MRI super-resolution.

Main Results:

Achieved state-of-the-art (SOTA) performance on public MRI datasets.
Demonstrated a 3.8% improvement in Peak Signal-to-Noise Ratio (PSNR) over existing SOTA methods.
Significantly outperformed traditional diffusion models in quantitative metrics and image detail capture.

Conclusions:

LearnDiff effectively enhances MRI super-resolution by utilizing learnable noise distributions.
The model shows superior performance across multiple MRI datasets, offering improved image quality and diagnostic potential.
The dynamic adaptability of LearnDiff addresses limitations of fixed noise models in medical imaging.