Reconstruction From Projection TRIZ Cases
- AI Sinogram Metal Detection for X-Ray Dental Tomography Artefact Reduction
Trained AI detects metal in x-ray projections or sinograms, generating masks that speed artefact correction and improve dental image quality.
- Cross-Regional Learning for Sparse-View CBCT Reconstruction
Sparse projections can lower radiation dose but degrade CBCT image quality; C2RV uses cross-regional and cross-view learning to preserve detail.
- PCCT Image Reconstruction Using Scalar Encoding to Limit Artifacts
Detector inhomogeneities can create ring and beam-hardening artifacts; calibrated weights encode energy bins into one value for faster reconstruction.
- Industrial CT Path Compensation for Focal Point Shifts
Thermal expansion shifts the X-ray focal point and object path; automatic compensation supports routine calibration and accurate CT reconstruction.
- Variable Radial Sinogram Dimensions for PET Data Reduction
Axial-angle filtering and variable radial extents retain useful PET coincidence data while reducing storage and computation demands.
- Augmented Reality Overlays for Unobstructed Surgical Guidance
Virtual instrument overlays keep the surgical field visible while providing real-time identification, assembly instructions, and procedural guidance.