Reconstruction From Projection TRIZ Cases

A magnetic resonance diagnostic apparatus derives apparent diffusion coefficients from multiple images to estimate pixel values for higher b-factor imaging.

Short-wave infrared tomography reconstructs accurate optical absorption maps by reducing light scattering in tissue, improving resolution and reliability.

Segmenting reconstruction into phases isolates metal regions, reducing artifacts while preserving underlying tissue information.

A medical image reconstruction system corrects 3D spatial positions of CT images using geometry correction algorithms.

A tomographic imaging method applies three-dimensional scanning electron microscopy imagery to constrain the solution space during iterative mathematical reconstruction.

A projection data acquisition apparatus uses Fibonacci sequences to determine rotational positions, ensuring homogeneous angular distribution of sampled data.

Bloch equation simulations provide reference data for inverse problem optimization, reducing noise amplification in parallel imaging.

Dual processor PET reconstruction applies multiplicative and additive error corrections to volumetric data for faster image generation.

Multi-source inverse geometry CT rebins projection data via z and trans-axial methods to resolve incomplete dataset artifacts.

A tilted helical image reconstruction method calculates ray channel parameters and slice numbers for each pixel position to perform three-dimensional back-projection.

Iterative parameter adjustment compensates for mechanical inaccuracies, reducing artefacts in reconstructed 3D volumes.

A curved planar reconstruction method generates cross sections along a center line path to visualize internal structures.

A fast reprojection method modifies Fourier spectral support via digital image coordinate transformations to optimize computational processing.

Standardizing reconstruction weights via angle-specific normalization eliminates sum-of-weight differences that cause tomographic artifacts.

A three-dimensional reconstruction means generates data from endoscopic images while a matching means aligns this data with a pre-existing model.

Segmenting projection data into distinct view angle ranges enables weighted image addition for improved temporal resolution.

Optimal density compensation filter calculates Nyquist distance weights to reconstruct undersampled radial k-space data accurately.

Spatial frequency domain filtering of tagged MRI data generates accurate B1 maps through inverse trigonometric calculations.

A projection image generation apparatus adjusts the viewpoint based on motion information between three-dimensional volume data sets.

Automated framework processes log event and state data to generate graphical representations for medical imaging equipment.

A detachable object housing isolates the specimen from external temperature and humidity changes, preventing grating deformation and reconstruction noise.

Randomized coil sketching lowers computation time and memory usage by selecting a subset of coils for data consistency steps.

A system generates estimated image data for regions outside the imaging field of view to create virtual projection data.

Iteratively determining refractive indices from multi-orientation spatial deviations corrects OCT image distortion caused by layer refraction.

A medical imaging apparatus generates a composite image and applies boundary conditions to compensate signal levels.

One-dimensional homographic resampling transforms recast continuous functions to enable arbitrary precision in medical image reconstruction.

Iteratively re-estimating the probe and object functions to reconstruct high-resolution images from scattered radiation intensity data.

Least squared error estimation calculates tissue electrical properties from segmented magnetic resonance images.

Automated image processing aligns X-ray Talbot reconstructed images by detecting grating directions and rotating them to a reference orientation.

Dynamic point subset refinement concentrates sampling density on detected signal features during iterative reconstruction.

Simplified iterative reconstruction using the Alvarez-Macovski model corrects polychromatic X-ray attenuation errors.

Multi-head nuclear medicine imaging systems segment regions of interest to optimize detector sweeping configurations.

Iterative forward and backward projection of interpolated voxel values resolves vessel overlap in angiography while minimizing radiation exposure.

Estimating time-of-flight scatter distribution using non-TOF projection data reduction and unbiased reconstruction algorithms.

A quantitative volume reconstruction method generates voxel values corresponding to material composition using iterative processing and calibration data.

A cone beam CT system corrects detector parallax errors using a component-weighted projection matrix derived from singular value decomposition.

Extracting spectral intermediate data reduces transmission volume while preserving diagnostic image quality.

A font generation network decouples character content from style attributes to create new typefaces without manual design.

Segmented x-ray sources enable parallel acquisition to resolve the trade-off between image contrast and slow data acquisition during radiation therapy.

Pre-scan noise maps and organ masks guide automatic exposure control to reduce patient radiation dose while maintaining image quality.

A computed tomography system switches between axial and helical acquisition modes using real-time EKG signals.

Clustering dynamic frames by similar reconstruction parameters to share scatter and random estimations across groups.

Computes virtual intraoral X-ray images from defined sub-volumes of 3D volumetric data for precise diagnostic views.

Combines analytical and iterative image reconstruction to generate a hybrid output with reduced shading artifacts.

Sensitivity weighting compensates for measurement gradients in filtered back-projection, reducing cupping artifacts and computational load.

Segmenting energy integrating and discriminating detectors resolves the contradiction between imaging speed and manufacturing precision.

A universal image processing platform leverages the Cell Broadband Engine to share computation and visualization across diverse client devices.

Segmenting projection images into blocks enables local movement estimation that reduces computational complexity while correcting motion artifacts.

Segmented iterative reconstruction updates outside data estimates to resolve truncation artifacts while minimizing harmful X-ray exposure.

Segmenting the region of interest into static and moving zones allows targeted 4D imaging, reducing radiation dose while maintaining diagnostic completeness.