Dual-domain joint learning reconstruction method (JLRM) combined with physical process for spectral computed tomography
Ma, G.; Zhao, X.
Show abstract
Spectral computed tomography (SCT) is an powerful imaging modality with broad applications and advantages such as contrast enhancement, artifact reduction, and material differentiation. The positive process or data collected process of SCT is a nonlinear physical process existing scatter and noise, which make it is an extremely ill-posed inverse problem in mathematics. In this paper, we propose a dual-domain iterative network combining a joint learning reconstruction method (JLRM) with a physical process. Specifically, a physical module network is constructed according to the SCT physical process to accurately describe this forward process, which makes the nonlinear use of the traditional mathematical iterative algorithm effective and stable. Additionally, we build a residualto-residual strategy with an attention mechanism to overcome the slow speed of the traditional mathematical iterative algorithm. We have verified the feasibility of the method through our winning submission to the AAPM DL-spectral CT challenge, and demonstrated that high-accuracy also basis material decomposition results can be achieved with noisy data.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- High precision fluorescence tomography-guided system for pre-clinical radiation research: system design and validation 95%
- Development of a Coupled Simulation Toolkit for Computational Radiation Biology Based on Geant4 and CompuCell3D 95%
- Deciphering Oxygen Distribution and Hypoxia Profiles in the Tumor Microenvironment: A Data-Driven Mechanistic Modeling Approach 94%
Similar papers in this journal
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 94%
- Effective Deep Learning Approaches for Predicting COVID-19 Outcomes from Chest Computed Tomography Volumes 94%
- Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease 93%
Similar papers in this journal
- Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling 93%
- Fully Automated Explainable Abdominal CT Contrast Media Phase Classification Using Organ Segmentation and Machine Learning 93%
- Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.