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.