Reconstruction From Projection TRIZ Cases

Tomographic reconstruction algorithms process multi-angle sensor images to resolve detection precision limits in uncontrolled environments.

Reconstructing pseudo-monoenergetic image data from multi-energy X-ray projection measurements to optimize contrast-noise ratio and reduce radiation dose.

A monitored tomographic reconstruction system uses adaptive stopping rules to determine sufficient image quality during scanning.

Selective data extraction from helical scans resolves scanning time and radiation exposure trade-offs while maintaining image quality.

Uniform redundancy weighting of projection data compensates for redundant sampling and truncated projections during short-scan off-center detector tomography.

A projection network maps neural outputs to a reference space for efficient classification.

An ultrasound diagnostic apparatus combines sectional and three-dimensional images at operator-defined ratios for simultaneous display.

A regularization convolutional kernel adjusts image data during iterative reconstruction to match target characteristics.

Iterative joint estimation reconstructs SPECT images using multi-energy emission data to compensate for photon attenuation.

Apply filters based on noise and image models to intermediate tomography images, reducing noise propagation while preserving edge clarity.

Inserting pseudo-slices into sinograms corrects positional errors from detector gaps without modifying reconstruction algorithms.

Parallel GPU processing reduces operation time for gamma-ray scattering estimation, enabling accurate 3D image reconstruction in positron emission tomography.

Modified statistical weights compress dynamic range to reduce image artifacts and accelerate convergence speed in computed tomography.

A tomosynthesis method reconstructs three-dimensional volume images from projection recordings acquired along a scanning path.

Computing unit calculates pixel-specific J coefficients to suppress differences in spatial resolution and noise characteristics across the measurement region.

A tomographic imaging device uses a planning unit to determine sampling positions based on an estimated object contour for optimized data acquisition.

Segmented rendering and parameter-changed storage manage network load and space constraints for medical images.

Directly inverts a Hessian matrix approximation to accelerate iterative image reconstruction convergence, reducing computational time and artifacts.

Segmenting the transducer matrix into independently controlled sub-arrays reduces electronic circuit complexity while maintaining high imaging resolution.

Spectral computed tomography data identifies moving structures using high-energy contrast to calculate precise motion vector fields.

Adjusts angular increments between radial k-space spokes to optimize sampling uniformity within restricted window sizes.

Composite pictures split panoramic video into sections with low-resolution backups, preventing playback disruption when view direction changes or data is lost.

Segmenting frequency bands during cone-beam CT reconstruction eliminates artifacts from data truncation and missing frequencies.

Differentiating and filtering cone-beam data along a variable-radius planar trajectory enables exact 3D reconstruction for non-circular imaging geometries.

Segmented filtered back-projection generates initial estimates while limited iterations correct quantitative errors.

A registration unit aligns a pre-acquired 3D vessel map to B-mode volumes for real-time overlay.

Optimized gantry positioning selects minimal projection angles, reducing radiation exposure while maintaining measurement precision.

MVW-PCA sorts PET and CT data into respiratory bins, resolving attenuation map mismatches caused by free breathing.

A processing system modifies recorded projection acquisition times using linear interpolation to determine accurate rotation angles for CT scanners.

Optical imaging and software correction replace heavy mechanical components to resolve the trade-off between spatial resolution and apparatus weight.

An iterative processing system updates error limiting information to refine magnetic susceptibility distribution maps.

Scanning device adjusts sampling intervals based on ray attenuation variations to optimize spatial resolution and scanning dose.

Energy-resolving detectors segment x-ray spectra into discrete bins to quantify target-specific contrast materials via K-edge imaging.

An X-ray dissectography module generates 3D feature sets from 2D radiographs using artificial neural networks.

Computational adaptive optics corrects aberrations in interferometric synthetic aperture microscopy using optimization procedures and filter functions.

A tomographic image generating apparatus performs extrapolating and smoothing processes on projection data to determine pixel values in non-detecting regions.

A passive nonlinear object generates excitations that time-reverse to reconstruct signals exclusively at its location.

A dynamic positron emission tomography reconstruction method applies spatially variant penalty functions to activity maps for adaptive smoothing.

A processing unit determines optimal radiopharmaceutical activity levels and target scan times based on patient-specific parameters to acquire targeted imaging information.

A 2.5-dimensional iterative reconstruction algorithm combines two-dimensional forward projection with three-dimensional stabilizing functions.

Virtual trajectory model corrects back projection positions to minimize motion artifacts while reducing computational load.

A SPECT imaging prediction model trained on paired short and standard acquisition images generates high-quality scans from rapid data.

Angled line pairs in a 3D resolution gauge visualize combined x-y and z-axis resolution, revealing how slice thickness limits actual CT image quality.

An OCT signal processing apparatus extracts depth region data from 3D motion contrast signals to display a confirmation screen.

Iterative adaptive thresholding generates selected coefficients to reconstruct data signals, reducing processing time and manual intervention.

An adaptive sampling mask generates optimized k-space data patterns using prior phase information to reconstruct high-fidelity medical images.

Segmenting the detector array into individual effective detecting ranges reduces data processing pressure and improves reconstruction accuracy.

Fills empty spaces in partial scan sinogram matrices with interpolated projection data to reduce reconstruction artifacts without increasing radiation dose.

Resolving activity-attenuation ambiguity by processing scattered photons to generate accurate attenuation maps for standalone PET systems.

A tomographic reconstruction algorithm applies shift-invariant filtering and backprojection to process x-ray cone-beam scan data.