Reconstruction From Projection TRIZ Cases

Miniaturized probe with photomultiplier tubes reduces system bulkiness while improving signal-to-noise ratio for accurate breast cancer treatment monitoring.

Acquires multiple coil sensitivity maps with distinct phase shifts to generate final images from undersampled magnetic resonance data.

Correlating projection datasets identifies truncation changes, correcting Hounsfield unit accuracy and contrast distribution errors.

Computes a weight function from one image volume to apply weighted forward projection through another volume for synthesized projection images.

A computer-implemented method uses non-linear Kalman filter approximations to reconstruct tomographic images from projection data.

Processor-based re-localization matches new scans to prior images, eliminating full environment re-scanning.

A CT console generates preview images during scanning to enable real-time parameter adjustments.

Area interpolation fills missing projection data from peripheral regions to synthesize tomographic images with high spatial resolution.

An embedded region-of-interest table segments detector output, applying binning to non-ROI areas to reduce data volume and resolve imaging speed bottlenecks.

A trained deep learning model generates high-definition two-dimensional images from tomosynthesis data by integrating projection and volume information.

Gradient projection-Barzilai-Borwein algorithm reconstructs cone-beam computed tomography images using iterative minima estimation.

Dynamic detector head positioning and system matrix updates resolve field-of-view obstructions in SPECT imaging.

A variance-reduced method corrects subset gradients using full gradient differences to accelerate computed tomography image reconstruction.

A TOF-PET signal waveform processing unit shifts gamma-ray detection signals to enable deep neural network error estimation.

Selective deposition of material precursors creates phantom structures with controlled internal optical properties.

An image processing system generates partial mammogram segments during scanning acquisition for immediate visual verification.

A measuring X-ray CT apparatus corrects projection images using stored geometric error data to generate accurate tomographic reconstructions.

A mobile terminal system corrects 360-degree image viewing angles by averaging tilt sensor data and gesture inputs for dynamic orientation adjustment.

Pre-calculated contrast recovery coefficients correct partial volume errors in reconstructed PET images.

Multi-neighbor correction applies phantom-derived factors to remove source leakage artifacts, restoring atomic number measurement accuracy.

Reduces detector complexity by optimizing material concentrations exclusively in the image domain, avoiding complex energy-sensitive hardware.

Processing circuitry controls X-ray tube voltage to generate separate projection data sets for high precision dual energy imaging.

A system tags 3D assets with unique identifiers to generate synthetic training images.

Image processing apparatus generates projection images of brain micro bleeds using minimum intensity projection within limited anatomical ranges.

Segmenting detector regions with a radiation absorber resolves the trade-off between miniaturized element size and focal point detection precision.

Segmented storage spaces decouple raw data from reconstruction images, resolving storage resource occupation while maintaining data consistency.

Spatially non-homogeneous element selection directs iterative updates to specific image regions.

Differentiated backprojector preconditioner accelerates iterative reconstruction convergence, reducing noise and computation time in low dose X-ray CT imaging.

A cone beam X-ray tomography system reconstructs images using full and short scan data.

Pre-computable block-diagonal denominator terms stabilize ordered subset maximum likelihood optimization for X-ray computed tomography image reconstruction.

Optimized ray consistency approach minimizes approximation errors and reduces reconstruction time in helical cone beam computed tomography systems.

The system merges separate scan processes into a single data acquisition step, reducing user workload and maintenance time while maintaining measurement precision.

An AI engine processes projection data through layered modules to reconstruct three-dimensional volumes.

Image processing device determines calibration data from X-ray recordings during rotation runs.

Segmented energy bins isolate Compton scatter events to restore spectral fidelity and dose efficiency.

Fractional detector shifts create non-uniform sampling patterns that resolve the trade-off between image resolution and system complexity.