Reconstruction From Projection TRIZ Cases

A tomographic imaging apparatus uses a scanning trajectory of spaced line segments to intersect a virtual reference surface around the specimen.

Axially segmenting positron emission tomography data into discrete bins and a transition region during continuous table motion.

Asymmetric regularization adjusts voxel weights by neighboring data completeness to suppress truncation artifacts in cone-beam CT images.

A medical imaging device selects base transforms for iterative optimization to reconstruct images.

K-space spatial frequency domain filtering generates accurate B1 and B0 maps for MRI systems.

Frequency decomposition separates overlapping structures to reduce artifacts, while iterative feedback refines image quality.

A spatially varying regularization parameter balances data and smoothing terms during iterative reconstruction to maintain uniform noise distribution.

A hybrid computed tomography system merges photon counting and energy integrating detectors to reconstruct high-fidelity images.

SSIFT method generates tissue images using incremental area under curve values from varying diffusion MRI times.

An algebraic reconstruction technique dynamically adjusts threshold parameters between iterations to eliminate artifacts in limited data scenarios.

A fiduciary marker combines radio-opaque and radioactive sections to overlay nuclear medicine data onto x-ray images.

A radiation detection apparatus uses a scintillator to convert incident radiation into light for photoelectric conversion by pixel arrays.

Pre-calculating weighting coefficients compensates for geometric distortions in non-rigid C-arm systems, reducing backprojection time.

A cone beam artifact reduction method extracts and subtracts artifact components from projection data to improve volume data quality.

Pixel-specific attenuation coefficients resolve accuracy issues in scattered ray removal and energy subtraction caused by uniform coefficient assumptions.

A deep learning algorithm refines PET sinograms to generate simultaneous emission and attenuation images without additional scanning hardware.

A neural computed tomography algorithm uses a signed distance function to represent object boundaries, reducing motion artifacts without explicit estimation.

Image processing apparatus applies suppression and enhancement to scan data based on radiation detection levels.

An iterative reconstruction method refines tomographic images by adjusting forward projection functions to enhance spatial resolution.

Three domain processors refine coefficients via iterative loops to enhance tissue identification accuracy.

A medical image processing device combines standard and high-frequency emphasis reconstruction filters to generate clear stent images.

Compressed sensing reconstruction recovers synthetic focusing channel data from random apodization transmissions, balancing spatial resolution and frame rate.

The FEMOS algorithm applies ordered subsets and super-resolution to accelerate computed tomography image reconstruction while reducing processing time.

A trained machine learning module predicts output correction data to refine iterative image reconstruction steps.

Segmented central and annulus scans with interpolated padding resolve overlap artifacts for accurate extended field-of-view imaging.

An energy discriminating detector counts photons per element to determine material composition, reducing beam-hardening artifacts in heterogeneous objects.

Time-gated truncation of cross-correlated signals overcomes thermal diffusion limits to achieve high axial resolution.

Extracting boundary position and magnitude data from projection information enables precise scattered X-ray correction, reducing tomographic image artifacts.

A tomographic image processing apparatus generates preview images by applying multiple filters to selected cross-sections of raw data.

An unsupervised deep learning method trains neural networks using under-sampled k-space data without ground truth images.

Circuitry calculates position-based weights from adjacent elements to resolve spatial resolution loss caused by oblique X-ray incidence.

Continuous color spectrum maps direction-angles to channels, resolving ambiguous structural domain identification in seismic data.

Adapting reconstruction parameters to the X-ray source trajectory geometry resolves blurring in thicker slice images.