Learning-based multi-material CBCT image reconstruction with ultra-slow kV switching.
Authors
Affiliations (7)
Affiliations (7)
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning, China.
- Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, Guangdong, China.
- Research Center for Advanced Detection Materials and Medical Imaging Devices, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
- Paul C Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
- State Key Laboratory of Biomedical Imaging Science and System.
- National Innovation Center for Advanced Medical Devices, Shenzhen, Guangdong, China.
- Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
Abstract
ObjectiveThe purpose of this study is to perform multiple (<math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo><mn>3</mn></math>) material decomposition with deep learning method for spectral cone-beam CT (CBCT) imaging based on ultra-slow kV switching.ApproachIn this work, a novel deep neural network called SkV-Net is developed to reconstruct multiple material density images from the ultra-sparse spectral CBCT projections acquired using the ultra-slow kV switching technique. In particular, the SkV-Net has a backbone structure of U-Net, and a multi-head axial attention module is adopted to enlarge the perceptual field. It takes the CT images reconstructed from each kV as input, and output the basis material images automatically based on their energy-dependent attenuation characteristics. Numerical simulations and experimental studies are carried out to evaluate the performance of this new approach.Main ResultsIt is demonstrated that the SkV-Net is able to generate four different material density images, i.e., fat, muscle, bone and iodine, from five spans of kV switched spectral projections. Physical experiments show that the decomposition errors of iodine and CaCl<math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow></mrow><mn>2</mn></msub></math> are less than 6<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>%</mi></math>, indicating high precision of this novel approach in distinguishing materials.SignificanceSkV-Net provides a promising multi-material decomposition approach for spectral CBCT imaging systems implemented with the ultra-slow kV switching scheme.