Reconstruction From Projection TRIZ Cases

A transformation matrix shortens magnetic resonance fingerprinting dictionary entries to match acquired signal lengths.

Groups projections by similar attenuation path lengths to reduce artifacts and improve resolution in emission tomography.

An image analyzing unit assigns colors to ultrasonic pixels based on staining commencement time to generate contrast progress images.

A tomosynthesis control device manages image data reject status through automated setting means and output control circuits.

Interpolating pre- and post-scan background data removes dark current noise, reducing ring artefacts in reconstructed CT images.

Segmenting operations by priority allows functioning GPUs to sustain image generation speed when other units malfunction.

A PET image reconstruction method uses photon energy factors to build system response models for accurate nuclide distribution mapping.

Helical scanning extracts vertical and non-vertical ray data to generate focused projection images from volumetric CT datasets.

Daily quality control assesses pixel stability to identify unstable detector pixels, reducing image artifacts and unnecessary maintenance calls.

Expands cone beam projection data in the z-direction to enhance axial coverage for iterative reconstruction algorithms.

Adaptive RF flip angles minimize misalignment artifacts from patient motion during segmented k-space acquisition, improving signal-to-noise ratio.

A TOF PET scatter estimation method generates projection data from standard and low energy windows to calculate a distribution ratio for accurate scatter correction.

A radiation detection system adjusts detector gain using intrinsic energy window counts to stabilize the signal peak position.

Iterative correction of CT projection images using gantry angle differences to normalize radiation dose variations across scan angles.

Image artifact extent predictor projects reconstruction areas onto display units for visual assessment.

A 3D X-ray reconstruction method using compressed sensing and algebraic registration to produce high-quality images from limited projections.

Synchronizing CT scans with respiratory cycles generates 3D tumor motion data without requiring patients to hold their breath.

An adaptive metal template expansion method reduces artifacts in computed tomography images by adjusting processing levels based on pixel proportions.

Adaptive baseline updates via electrical impedance tomography compensate for elastomer rebound elasticity to achieve real-time sensing.

Two-pass CT reconstruction with localized filtering reduces jagged edges and undershoots without increasing memory requirements.

Adaptive methods select contributing viewing angles to optimize nuclear data acquisition, reducing radiation exposure while maintaining signal-to-noise ratio.

Estimating mean random sinograms from prompt data reduces image noise and artifacts during continuous bed motion PET acquisitions.

A lensless imaging system uses an optical pinhole to generate diffraction signals for computational image reconstruction.

A method determines three-dimensional image datasets by processing multiple two-dimensional X-ray projections from different directions.

Segmenting the scanning field with multiple micro-focus bulbs and detectors resolves the trade-off between large field of view and high spatial resolution.

A CT image reconstruction method uses a polychromatic physical model with single-variable optimization to decompose sinograms into basis components.

Asymmetric dual-source CT detectors optimize cardiac scan coverage and time resolution.

A causal device identifies relationships between variables to correct PET/MRI attenuation errors and improve triage prediction accuracy.

Confidence maps identify MRI error regions, excluding unreliable data from parametric maps to reduce systematic measurement errors.

Angle-specific gain maps correct pixel value differences in tomosynthesis imaging, improving contrast noise ratio and reducing x-ray dose penalties.