Reconstruction From Projection TRIZ Cases

A pseudo-projection method scans the focal plane through specimen thickness during single exposure to generate shadowgrams for 3D reconstruction.

Calculates temporal moments to resolve measurement ambiguity in backscatter geometry, enabling accurate fluorophore localization in diffusing tissues.

A weighting factor determining section calculates subject-specific values to reconstruct dual-energy images.

MRI system adapts reconstruction parameters to balance speed and resolution.

Simultaneously recording separable diffraction patterns at a detector enables coherent diffractive imaging without initial probe function estimation.

An anti-correlation filter processes noisy basis material line integrals using regularization terms to produce de-noised data.

A CT reconstruction method segments the object into a region of interest and surrounding area to apply distinct imaging resolutions.

Optimized k-q space sampling combined with deep learning generates reliable neurite orientation dispersion maps while reducing acquisition time and scan costs.

Parallel reconstruction and data preservation reduce first image display latency and standby intervals while maintaining uniform output intervals.

An integrated phantom synchronizes geometric correction and photography, eliminating separate pre-experiment steps that cause time loss.

A tomographic slice rendering method embeds volume data into 2D slices to enhance anatomical visualization.

Sequential inversions integrate anatomical priors into optical data, restoring fluorescence distribution accuracy.

Real-time wavelength feedback rearranges non-equidistant interference signals to maintain image resolution despite light source fluctuations.

Reconstruction system generates volumetric images from projection datasets using energy minimization techniques.

A noise estimation system calculates pseudo-standard deviation from featureless image regions to provide reliable automated assessment.

A processing circuit identifies polarized photon-counting detectors by analyzing flux linearity to flag distorted data before image reconstruction.

Segmenting motion and density fields preserves tissue density information lost during conventional respiratory compensation.

A CT image reconstruction system generates initial 3D images using convex optimization to reduce bias before iterative refinement.

Motion compensated image reconstruction algorithms estimate vector fields to correct projection data, reducing cardiac motion artifacts and radiation dose.

Automated crystal lookup table generation using normalized flood histograms and dynamic programming.

Sequence-based motion estimation stabilizes respiratory parameters across projection views, reducing artifacts from high noise and low resolution.

Arithmetic device updates images using reference image differences to reduce calculation load.

Segmenting focal spot blur and detector response into iterative steps improves image resolution and noise performance at low doses.

A nuclear medicine imaging method updates the system matrix using derived translation vectors to reconstruct projection data.

A dual portion data transmitting module connects RF coils to image processing units via electromagnetic and optical wireless links.

Iterative dispersion correction resolves the trade-off between imaging speed and resolution by adjusting parameters until quality metrics are met.

Removes low-frequency shading artifacts from truncated projection data by normalizing sinogram values with generator signals.

Dynamic regularization scaling adapts iterative reconstruction algorithms to projection data characteristics.

A medical image processing apparatus estimates a temperature field to correct thermography images.

A tomographic image generation method uses selective blurring on projection images to isolate high-contrast regions before subtracting processed data from original reconstructions.

A tomographic reconstruction method balances image fidelity with spatial and temporal compression to process 2D projection sequences.

Digital volumetric laminar tomography corrects systematic errors and optimizes voxel geometries to resolve image artifacts caused by aliasing.

Computational algorithm corrects x-ray intensity non-uniformity using anode interaction depth and angular direction data.

A control device evaluates radiation source parameters to identify projection images affected by arcing interference during X-ray acquisition.

Extends target regions to calculate vertical boundary truncation TEC values for three-dimensional ionospheric electron density modeling.

A computed tomography method applies image filters and numerical derivatives to enhance edge detection in reconstructed X-ray images.

A reconstruction unit selects specific data sets with minimum motion to generate fine cardiac reproductions.

Generating phase contrast images from dual-energy x-ray attenuation data to enhance spatial resolution and contrast without specialized hardware.

Segments reconstruction into low and high resolution stages to improve measurement precision without increasing storage space.

A cone-beam CT imaging system reconstructs high-temporal resolution images using a SMART RECON process with temporal deconvolution.

A controller monitors GPU status and redistributes tasks among functional processors to maintain real-time image reconstruction speeds during hardware failures.

A computed tomography system optimizes beamlet intensities using patient models to enhance dose efficiency.

Voxel-based segmentation of cosmic ray scattering patterns detects shielded nuclear materials without artificial radiation dose.

Spatially-varying attenuation sheets modulate x-ray beams to acquire simultaneous dual-energy data, eliminating motion artifacts from sequential scanning.

A cabinet x-ray system integrates a real-time camera to capture optical images alongside radiograms for immediate visual correlation.

Geometric models guide iterative reconstruction to resolve depth resolution bottlenecks in limited-angle tomography.

Multistatic GPR arrays suppress extraneous signals and identify short-extent defects by combining polarization data.

Tissue suppression removes trabecular bone from CT scans, resolving the trade-off between lesion sensitivity and axial resolution.

A half-ramp filter extracts missing frequency data from truncated cone-beam projections, reducing artifacts while preserving temporal resolution.