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Updated: May 5, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Self-consistent recursive diffusion bridge for medical image translation.

Fuat Arslan¹, Bilal Kabas¹, Onat Dalmaz¹

¹Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey.

Medical Image Analysis

|August 6, 2025

Summary

This summary is machine-generated.

A new method, SelfRDB, improves medical image translation by directly using source images during the diffusion process, overcoming limitations of standard denoising diffusion models (DDM) for better quality.

Keywords:

Bridge CT Diffusion Generative MRI Medical image translation Synthesis

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Last Updated: May 5, 2026

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Area of Science:

Medical Imaging
Artificial Intelligence
Image Translation

Background:

Denoising diffusion models (DDM) show promise in medical image translation due to stability and fidelity.
However, DDMs can struggle with suboptimal source-modality guidance, impacting performance in medical image translation tasks.
This is because their denoising process diverges from the direct source-to-target transformation required.

Purpose of the Study:

To introduce a novel self-consistent recursive diffusion bridge (SelfRDB) for enhanced medical image translation.
To address the performance limitations of standard DDMs in medical image translation by incorporating direct source-modality guidance.
To improve image quality and robustness in cross-modality medical image translation.

Main Methods:

SelfRDB utilizes a novel forward diffusion process starting from the target image and ending based on the source image.
A unique noise scheduling with monotonically increasing variance towards the end-point facilitates inter-modality information transfer.
A recursive sampling procedure refines target image estimates until a self-consistent solution is achieved.

Main Results:

SelfRDB demonstrates state-of-the-art performance in medical image translation tasks.
Experiments in multi-contrast MRI and MRI-CT translation confirm superior image quality.
The proposed method shows improved robustness against measurement noise.

Conclusions:

SelfRDB offers a significant advancement over standard DDMs for medical image translation.
Direct source-modality guidance and novel diffusion strategies enhance translation accuracy and image fidelity.
The method holds potential for improving various medical imaging applications requiring cross-modality translation.