3D directional gradient L0 norm minimization guided limited-view reconstruction in a dual-panel positron emission mammography

Yu Shi; Yirong Wang; Fanzhen Meng; Jianwei Zhou; Bo Wen; Xuexue Zhang; Yanyun Liu; Lei Li; Juntao Li; Xu Cao; Fei Kang; Shouping Zhu

doi:10.1016/j.compbiomed.2023.107010

3D directional gradient L₀ norm minimization guided limited-view reconstruction in a dual-panel positron emission mammography

Comput Biol Med. 2023 Jul:161:107010. doi: 10.1016/j.compbiomed.2023.107010. Epub 2023 May 18.

Authors

Yu Shi¹, Yirong Wang², Fanzhen Meng³, Jianwei Zhou¹, Bo Wen¹, Xuexue Zhang¹, Yanyun Liu¹, Lei Li¹, Juntao Li¹, Xu Cao⁴, Fei Kang⁵, Shouping Zhu⁶

Affiliations

¹ School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China.
² Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China.
³ School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; School of Medical Imaging, Hebei Medical University, Shijiazhuang City, Hebei, 050017, China.
⁴ School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China. Electronic address: caoxu327@gmail.com.
⁵ Department of Nuclear Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, 710032, China. Electronic address: fmmukf@qq.com.
⁶ School of Life Science and Technology, Xidian University & Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, Xi'an, Shaanxi, 710126, China; Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information & International Joint Research Center for Advanced Medical Imaging and Intelligent Diagnosis and Treatment, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, 710126, China; Innovation Center for Advanced Medical Imaging and Intelligent Medicine, Guangzhou Institute of Technology, Xidian University, Guangzhou, Guangdong, 51055, China. Electronic address: spzhu@xidian.edu.cn.

PMID: 37235943
DOI: 10.1016/j.compbiomed.2023.107010

Abstract

Background: Dual-panel PET is often used for local organ imaging, especially breast imaging, due to its simple structure, high sensitivity, good in-plane resolution, and straightforward fusion with other imaging modalities. Nevertheless, because of data loss caused by the dual-panel structure, using conventional image reconstruction methods results in limited-view artifacts and low image quality in dual-panel positron emission mammography (PEM), which may seriously affect the diagnosis. To mitigate the limited-view artifacts in the dual-panel PEM, we propose a 3D directional gradient L₀ norm minimization (3D-DL₀) guided reconstruction method.

Methods: The detailed derivation and reasonable simplification of the 3D-DL₀ algorithm are given first. Using this algorithm, we then obtain a prior image with edge recovery but contrast loss. To limit the solution space, the 3D-DL₀ prior is introduced into the Maximum a Posteriori reconstruction. Meanwhile, a space-invariant point spread function is also implemented to restore image contrast and boundaries. Finally, the reconstructed images with limited-view artifact suppression are obtained. The proposed method was evaluated using the data acquired from physical phantoms and patients with breast tumors on a commercial dual-panel PET system.

Results: The qualitative and quantitative studies for phantom data and the blind reader study for clinical data show that the proposed method is more effective in reaching a balance between artifact elimination and image contrast improvement compared with various limited-view reconstruction methods. In addition, the iteration process of the method is proved convergent numerically.

Conclusions: The image quality improvement confirms the potential value of the proposed reconstruction algorithm to address the limited-view problem, and thus improve diagnostic accuracy in dual-panel PEM imaging.

Keywords: 3D directional L(0)gradient; Dual-panel PET; Limited-view imaging; Mammography; Maximum a posteriori.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artifacts
Breast / diagnostic imaging
Electrons*
Humans
Image Processing, Computer-Assisted / methods
Mammography* / methods
Phantoms, Imaging