Reconstruction From Projection TRIZ Cases

Segmenting 3D volumes into sub-volumes reduces computational complexity while maintaining image quality.

A patient-adapted motion model derived from structural scans enables accurate reconstruction of functional projection data in medical imaging systems.

Virtual rectification support transforms cone-beam projections to reduce computational intensity during tomographic image reconstruction.

Differentiating cone beam projection data along the tangent direction of the scan trajectory to suppress non-local artifacts while preserving useful features.

A dynamic coincidence processing method adjusts detector ring acceptance criteria based on axial separation to manage positron annihilation event throughput.

Dynamic adjustment of scan speed and pulse duration based on real-time attenuation measurements reduces motion blur while accelerating acquisition.

Segmenting initial projection images into subsets reduces data volume while maintaining diagnostic accuracy through combined weighted re-projections.

A rotating slit collimator system captures one-dimensional images at various angles to generate optimized gamma-ray reconstructions.

Switchable fiducial markers enable accurate image registration without obscuring the clinician's view during medical device navigation.

Pre-computed regularization images from an atlas library eliminate in-scanning processing, reducing acquisition time while maintaining image quality.

A discriminator assembly counts photon hits at distinct energy thresholds to classify x-ray spectral data into discrete bins.

Transforming cumulative distribution functions into image space to represent voxel standard deviations for direct quality assessment.

Optical coherence tomography extracts subsurface tissue patterns into unique digital codes, resisting cosmetic alterations that compromise fingerprint accuracy.

Z-axis processing removes structured artifacts from cone-beam CT volumetric data using selective frequency filtering.

A deconvolution filter accelerates tomographic image reconstruction by approximating Hessian inversion during iterative updates.

Segmenting scan ranges with smaller view numbers advances X-ray OFF timing, reducing radiation dose and shortening total scanning time.

Segmenting MRI acquisition into sub-periods enables continuous motion tracking and data subset selection.

A CT image processing apparatus extracts accurate periodic motion waveforms using frequency threshold specification and band-pass filtering.

A dual-energy CT reconstruction method uses iterative optimization to generate material component density images from multi-spectral x-ray data.

Interleaving projection data via a quarter detector offset increases in-plane sampling rates, overcoming fixed focal spot resolution limits.

CT projection data analysis extracts respiratory timing information by tracking tissue center of mass changes during gantry rotation.

A narrow X-ray sensor paired with a collimator captures segmented image data during rotation to reconstruct customizable fields of view.

A probabilistic reconstruction method maps scanner noise to candidate images for enhanced data quality.

Photon counting detectors segment projection measurement data into high and low resolution groups to maintain spectral imaging fidelity.