Deep learning CT image restoration using system blur and noise models

Yijie Yuan; Grace J. Gang; J. Webster Stayman

doi:10.1117/1.JMI.12.1.014003

3 February 2025 Deep learning CT image restoration using system blur and noise models

Yijie Yuan, Grace J. Gang, J. Webster Stayman

Author Affiliations +

Funded by: National Institute of Biomedical Imaging and Bioengineering (NIBIB)

Journal of Medical Imaging, Vol. 12, Issue 1, 014003 (February 2025). https://doi.org/10.1117/1.JMI.12.1.014003

Abstract

Purpose

The restoration of images affected by blur and noise has been widely studied and has broad potential for applications including in medical imaging modalities such as computed tomography. Recently, deep learning approaches have demonstrated the potential to enhance image quality beyond classic limits; however, most deep learning models attempt a blind restoration problem and base their restoration on image inputs alone without direct knowledge of the image noise and blur properties. We present a method that leverages both degraded image inputs and a characterization of the system’s blur and noise to combine modeling and deep learning approaches.

Approach

Different methods to integrate these auxiliary inputs are presented, namely, an input-variant and a weight-variant approach wherein the auxiliary inputs are incorporated as a parameter vector before and after the convolutional block, respectively, allowing easy integration into any convolutional neural network architecture.

Results

The proposed model shows superior performance compared with baseline models lacking auxiliary inputs. Evaluations are based on the average peak signal-to-noise ratio and structural similarity index measure, selected examples of top and bottom 10% performance for varying approaches, and an input space analysis to assess the effect of different noise and blur on performance. In addition, the proposed model exhibits a degree of robustness when the blur and noise parameters deviate from their true values.

Conclusion

Results demonstrate the efficacy of providing a deep learning model with auxiliary inputs, representing system blur and noise characteristics, to enhance the performance of the model in image restoration tasks.

Funding Statement

Citation Download Citation

Yijie Yuan, Grace J. Gang, and J. Webster Stayman "Deep learning CT image restoration using system blur and noise models," Journal of Medical Imaging 12(1), 014003 (3 February 2025). https://doi.org/10.1117/1.JMI.12.1.014003

Received: 13 June 2024; Accepted: 7 January 2025; Published: 3 February 2025

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $24.00

Non-members: $28.00 ADD TO CART

JOURNAL ARTICLE
16 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Performance modeling

Deep learning

Image restoration

Computed tomography

Education and training

Data modeling

Imaging systems

Purpose

Approach

Results

Conclusion

Funding Statement

Show All Keywords

Keywords/Phrases

Search In:

Publication Years