Limited-angle cherenkov-excited luminescence scanned tomography reconstruction based on spatial attention module

Mengfan Geng; Hu Zhang; Jingyue Zhang; Kebin Jia; Zhonghua Sun; Zhe Li; Jinchao Feng

doi:10.1117/12.2682920

18 July 2023 Limited-angle cherenkov-excited luminescence scanned tomography reconstruction based on spatial attention module

Mengfan Geng, Hu Zhang, Jingyue Zhang, Kebin Jia, Zhonghua Sun, Zhe Li, Jinchao Feng

Author Affiliations +

Proceedings Volume 12745, Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023); 1274510 (2023) https://doi.org/10.1117/12.2682920
Event: Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023), 2023, Haikou, China

Abstract

Cherenkov-Excited Luminescence Scanned Tomography (CELST) is a new emerging imaging modality, which uses the Cherenkov light to excite fluorophores for tomographic imaging. In order to improve the imaging depth and spatial resolution, a rotational CELST was developed to scan the imaging object to produce sinogram data, and a Filtered Back Projection (FBP) was used to recover the distribution of fluorophores. However, the images reconstructed by FBP are usually corrupted by artifacts due to measurements from limited angles. To reduce the artifacts, we propose a deep learning-based reconstruction algorithm (SAM-Unet), which is based on a fully convolutional deep neural network with U-Net structure, and a spatial attention module was added between the encoder and the decoder. The image features extracted by the spatial attention module are transferred to the decoder through a skip connection structure. The effectiveness of the proposed SAM-Unet is verified by numerical experiments, and the results show that the SAM-Unet can improve the mean square error (MSE) (97.5%), Peak Signal-To-Noise Ratio (PSNR) (81.9%) and Structure Similarity Index Measure (SSIM) (63.4%) compared with the FBP algorithm. Compared with the deep learning method U-Net, the MSE improved 39.8%, the PSNR improved 8.0% and SSIM improved 2.6%.

Citation Download Citation

Mengfan Geng, Hu Zhang, Jingyue Zhang, Kebin Jia, Zhonghua Sun, Zhe Li, and Jinchao Feng "Limited-angle cherenkov-excited luminescence scanned tomography reconstruction based on spatial attention module", Proc. SPIE 12745, Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023), 1274510 (18 July 2023); https://doi.org/10.1117/12.2682920

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: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Reconstruction algorithms

Image restoration

Detection and tracking algorithms

Fluorescence

Quantum efficiency

Luminescence

Tomography

Show All Keywords

Keywords/Phrases

Search In:

Publication Years