Reconstruction From Projection TRIZ Cases

Phase-correlated difference imaging corrects truncation artifacts by expanding projections, resolving undersampling issues in cone-beam CT scans.

EKG trigger synchronizes C-arm X-ray image capture with cardiac cycles, reducing patient radiation dose during solid model generation.

Bidirectional estimation connects modalities to improve anatomic localization accuracy while reducing total scanning time.

Modulating light transmittance with a sub-pixel mask increases signal information, enabling accurate high-resolution reconstruction of fine structures.

Pre-trained models estimate later-stage imagery from incomplete scans, resolving the trade-off between scan duration and image quality.

Deep learning predicts deformation fields for PET-CT alignment, reducing artifacts from respiratory motion while improving computational efficiency.

Rank minimization separates consistent data subsets to eliminate motion artifacts and lower radiation dose in time-resolved imaging.

A control device derives a common region across multiple projection images to acquire consistent tomographic data.

Estimates region features from raw list mode data to correct motion artifacts, eliminating external sensors and reducing algorithm complexity.

A machine learning model corrects X-ray detector data affected by transient response deterioration, reducing overshoot and undershoot errors in CT images.

A computer program generates a metal mask and performs weighted interpolation to reconstruct clean CT images.

Aggregating multiple repetition images via deep sets improves reconstruction quality while reducing computational time and memory requirements.

Ordered subsets with momentum terms accelerate X-ray CT image reconstruction convergence, reducing computational costs and enabling faster diagnosis.

Voxel classification replaces metal pixels with bone density values, eliminating streak artifacts from high attenuating objects.

A processing circuitry segments reconstruction tasks into priority queues to generate early reference images.

Stationary detector captures intensity images under multiple illumination angles to reconstruct three-dimensional object information.

Angle indicator module adds visual cues to MRI images depicting magic angle orientation relative to the static magnetic field.

A 4D image processing system selects a reference gate using pixel intensity metrics and registers other gates to align respiratory motion data.

Segmenting the C-arm trajectory into multi-planar paths to intersect projection cones, expanding the reconstructed 3D volume beyond single-orbit limits.

A dynamic multi-source image reconstruction apparatus uses staged processing to produce high-quality three-dimensional output images.

A medical image processing system predicts section images from scout scans to display real-time scanning parameters for operator review.

Pre-calculated parameter tables resolve the trade-off between image quality and computational complexity during iterative reconstruction.

A medical image diagnosis apparatus adjusts successive approximation parameters based on scanning region information to optimize reconstruction speed.

Iterative algorithms reconstruct volumetric images from incomplete X-ray measurement data collected during high-speed object transport.

Acoustic wave device detects photoacoustic signal width and constrains display size to eliminate tissue-obscuring artifacts.

Fibonacci phyllotaxis arranges k-space sample points to resolve scan order definition difficulties in compressed sensing while reducing artifacts.

Segmenting geometric calibration from final volume generation reduces computation time while enhancing robustness of motion artifact compensation in dental DVT.

Weighted raysum processing suppresses streak artifacts from metal objects while preserving diagnostic information in reconstructed images.

Multi-transmit RF coils measure electrical coupling scattering to extract subject motion signals, eliminating external hardware and reducing scan time.

Direct triangulation connects adjacent sampling points to generate uniformly sampled sinograms for positron emission tomography reconstruction.

A scatter scaling processor adjusts single-scatter simulation sinograms using Monte Carlo ratios to correct imaging data.

Spectral CT data generates a virtual 511 KeV attenuation map, correcting quantification errors near high-density materials.

A space-time scattering network models light propagation as a discrete tensor field to enable efficient optical component design.

A separable quadratic surrogate function accelerates iterative positron emission tomography reconstruction through Nesterov acceleration and ordered subsets.

Image reconstruction unit calculates scattered ray components using atom number density and atomic number for precise tomographic imaging.

A neural network models CT imaging as a linear layer to reconstruct scene density, eliminating intensive sampling requirements for dynamic scenes.

Machine learning system generates multiple hypotheses for missing sensor values to ascertain classification results.

Symmetric and asymmetric scatter kernels estimate radiation interference in radiographic projections to correct pixel data.

Jointly estimates deformation fields from respiratory gated data to correct attenuation mismatch and improve quantitative integrity.

An X-ray CT scanner extracts pixel values from phantom images to generate frequency spectra for quantitative vibration assessment.

Computed tomography reconstruction applies a view-weighting function to down-weight motion artifacts, reducing algorithmic complexity.

A CT image reconstruction method aligns X-ray projection data with dynamic sensor information using embedded timestamps.

A detection values processing apparatus applies component decomposition to separate K-edge attenuation from additional components for image reconstruction.

A radiodiagnostic apparatus corrects truncated projection data using body contour detection and extrapolation algorithms.

Fan-shaped k-space blades position desired echo signals in low spatial frequency regions, correcting contrast loss from non-orthogonal measurement arrangements.