Feasibility study of using masked auto-encoder for a streak artifact reduction in sparse view CT

Gihun Kim; Jongduk Baek

doi:10.1117/12.3006134

2 April 2024 Feasibility study of using masked auto-encoder for a streak artifact reduction in sparse view CT

Gihun Kim, Jongduk Baek

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292624 (2024) https://doi.org/10.1117/12.3006134
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In this study, we investigated the feasibility of using masked auto-encoder (MAE) for sinogram inpainting to reduce a streak artifact in sparse view CT, taking their successful application in image inpainting tasks. To handle the sparse view sinogram, the flattener operator, which flattened the image patch into a token, was modified to handle the 1D image patches. We compared the artifact reduction performance for trained MAE using random sampling (i.e., MAE (Random)) and periodic sparse view sampling (i.e., MAE (Sparse)). To generate the training and validation samples, a software phantom consisting of multiple simple figures was used for the CT simulation with Siddon algorithm and filtered back-projection algorithm. To evaluate the performance of MAE for streak artifact reduction in sparse view CT, we implemented RED-CNN and compared the streak artifact reduction performance of RED-CNN and MAE. SSIM and PSNR were used to quantitatively measure the performance of MAE and RED-CNN. MAE provided better performance for streak artifact reduction in sparse view CT than conventional deep-learning-based artifact reduction technique (i.e., RED-CNN). MAE with random view sampling showed better performance than with sparse view CT images. We expect that MAE for medical imaging could be applied in other fields of medical imaging study.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gihun Kim and Jongduk Baek "Feasibility study of using masked auto-encoder for a streak artifact reduction in sparse view CT", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292624 (2 April 2024); https://doi.org/10.1117/12.3006134

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