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3D Diffusion Posterior Sampling for CT Reconstruction.

Peiqing Teng¹, Xiao Jiang², Liang Cai³

¹Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.

Proceedings of Spie--The International Society for Optical Engineering

|August 11, 2025

Summary

This summary is machine-generated.

This study introduces 3D Diffusion Posterior Sampling (DPS) for computed tomography (CT) reconstruction, overcoming computational limits of previous 2D models. The research presents strategies for efficient 3D DPS CT reconstruction using neural networks.

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Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Diffusion models excel at high-quality image generation.
Diffusion Posterior Sampling (DPS) offers unsupervised learning and flexibility for CT restoration and reconstruction.
Current DPS methods are primarily limited to 2D, while clinical CT is inherently 3D.

Purpose of the Study:

To develop and evaluate strategies for 3D Diffusion Posterior Sampling (DPS) CT reconstruction.
To address the computational challenges of applying 3D DPS to realistic CT volumes.
To enable efficient and effective 3D CT reconstruction using advanced generative models.

Main Methods:

Implementation of a 3D neural network for learning the prior distribution in DPS.
Modification of the standard DPS algorithm to reduce memory usage and increase sampling speed.
Evaluation of alternative strategies for enabling 3D DPS on realistic CT volume sizes.

Main Results:

Development of computationally feasible strategies for 3D DPS CT reconstruction.
Significant reduction in memory requirements and acceleration of sampling speed for 3D DPS.
Comparative analysis of different strategies for 3D DPS implementation in CT.

Conclusions:

The proposed strategies enable practical 3D DPS for CT reconstruction, overcoming previous computational barriers.
The research facilitates the application of advanced generative models to complex 3D medical imaging tasks.
This work paves the way for more efficient and accurate 3D CT reconstruction using diffusion models.