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Found 5 papers, 1 papers with code
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography.
SmartTriage: A system for personalized patient data capture, documentation generation, and decision support
Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel.
MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
Other components, such as image priors and hyperparameters, are kept as the original design.
JSR-Net: A Deep Network for Joint Spatial-Radon Domain CT Reconstruction from incomplete data
CT image reconstruction from incomplete data, such as sparse views and limited angle reconstruction, is an important and challenging problem in medical imaging.
Dictionary-Learning-Based Reconstruction Method for Electron Tomography
In this paper, we evaluate the EST, ADSIR and an ordered-subset simultaneous algebraic reconstruction technique (OS-SART), and compare the ES and equally angled (EA) data acquisition modes.
