Reconstruction From Projection TRIZ Cases

Direct derivative calculation on filtering lines eliminates interpolation errors in x-ray image reconstruction.

A tomosynthesis system generates synthetic projection images to align low and high energy scan angles for accurate difference image reconstruction.

Dynamic 3D reconstruction identifies regions of interest to guide targeted re-acquisitions, reducing radiation dosage while maintaining diagnostic precision.

Patch-wise computations in a learned iterative scheme reduce memory requirements while correcting streaking artifacts in cone beam computed tomography.

A multi-modality imaging system combines X-ray CT, neutron CT, and electromagnetic tomography with machine learning to estimate material properties in large rock cores.

Calculates material-specific noise from attenuation data to correct beam hardening artifacts during iterative tomography.

Reprojecting 3D markers in standard projection images confirms radiological signs while reducing access time to clinical information.

A framelet-based iterative maximum-likelihood algorithm reconstructs spectral images using standard energy-integrating detectors.

Residual convolutional neural networks estimate physiological artifacts in multi-shot echo planar imaging sequences to reconstruct high isotropic resolution images.

Viewing point conversion isolates relative internal body motion from proximal and distant tissue interference for precise heart function evaluation.

Replacing k-edge artifacts in spectral attenuation curves with approximations improves signal-to-noise ratio and effective atomic number estimates.

Information processing apparatus performs basis conversion on signal data to calculate multiple bases.

Merges circular trajectory efficiency with linear trajectory completeness to eliminate cone beam artifacts in medical imaging.

Gradient stiffness catheters navigate tortuous vessels while maintaining stability, reducing tissue damage and procedural time.

A poly-energetic iterative Filtered Backprojection algorithm decomposes attenuation coefficients to reconstruct artifact-free cross-sectional images.

Deep learning algorithms reconstruct tomographic images from raw data, eliminating noise and artifacts without iterative optimization.

Segmenting event data into portions allows the system to generate intermediate images quickly while refining composite data for better quality.

Combining a neural network with compressed sensing reconstructs high-quality MRI images from undersampled data, mitigating artifacts and noise.

A method processes four-dimensional angiography data sets by establishing static time parameter sets and applying window functions to create mask data sets.

Rendering algorithms enhance tissue visibility in 2D pre-scan projection images for computed tomography systems.

A medical image processing apparatus generates color image data from time series contrast agent signals to visualize blood vessel dynamics.

A magnetic resonance imaging method removes k space values exceeding threshold criteria to generate cleaner measurement datasets.

Reconstructs parametric images from continuous bed motion PET data using recorded position-time coordinates.

Machine learning discriminates border regions to generate a confidence map that indicates missing projection directions and reduces limited angle artifacts.

Segment volumetric data into visible partial surfaces to eliminate internal structures and improve registration accuracy.

A stationary 3D x-ray imaging system uses multiple two-dimensional detectors to capture simultaneous projections for volumetric reconstruction.

An artificial neural network processes multi-spectral x-ray projections to determine material composition using equivalent thickness of basis materials.

In-line 4D cone beam CT reconstruction algorithm processes projection images during acquisition to enable rapid phase determination and back-projection.

Deriving background projection data for areas outside the targeted field of view to reconstruct tomographic images.

A parametric model fits boundary curves to projection data using material-specific parameters.

Modified Green's function accounts for conducting surface distortions, resolving inversion artifacts and improving image accuracy.

A radioopaque marker support corrects geometric aberrations in dental tomography caused by patient motion during long scan times.

Image generation device calculates optimum dose profile using noise propagation algorithms.

Forward and reverse phase encoding gradients acquire complementary k-space halves, correcting geometric distortion and reducing Gibbs ring artifacts in MRI.

A magnetic resonance imaging method determines kernel data from 3-D surface coil acquisitions to reconstruct unaliasing images.

Green functions map spatial coordinates to detector faces for fluorophore distribution reconstruction.

Constrained weighting factors homogenize noise in tomographic projection images, preserving diagnostic information lost by standard AIP and MIP methods.

A robotic arm moves an X-ray emitter and detector along a curvilinear path to capture 2D views while tracking object motion.

Filtered back-projection reconstructs images from straight-line scans without rebinning interpolation.

A galvanometer optical scanner directs excitation light across a large field of view to enable rapid in vivo small animal imaging.

A PET image reconstruction method recovers intra-crystal Compton scattering events by matching energy and time criteria between scan data sets.

Adaptive upsampling stabilizes cone beam weighting functions in helical CT reconstruction.

Iterative reconstruction model separates target object from background using sparse Kaczmarz algorithm to recover image data.

An external database system stores raw CT scan data independently of the scanner to enable flexible image reconstruction using third-party software.

A control device manages radiation image data transfer with specific retransmission timeout settings for the final data packet in each imaging mode sequence.

An adaptive optimization module automatically adjusts regularization parameters to resolve ill-posed reconstruction problems in magnetic particle imaging.

Landmark auxiliary variables capture beam hardening distortions in dual-energy CT, reducing noise-induced bias during spectral image reconstruction.

Enlarges voxels and detectors in projection data to iteratively reconstruct images, reducing artifacts while managing computational complexity.

Iterative loops update direct parametric reconstruction parameters using motion-corrected frame emission images to maintain tracer kinetics accuracy.