Koninklijke Philips Corporation N.v.

Kevin Martin Brown

Stanislav Zabic

Thomas Koehler

Dominic J Heuscher

Michael Grass

Roland Proksa

Bernhard Johannes Brendel

Jens Wiegert

Frank Bergner

Manindranath Vembar

Liran Goshen

Randall Peter Luhta

Axel Thran

Claas Bontus

Gilad Shechter

Mordechay Pinchas Freiman

Rolf Dieter Bippus

Matthias Bertram

Reuven Levinson

Asher Gringauz

Steven J Utrup

Patrick J Kling

Cristian Buerger

A method for use in a material decomposition procedure applied to dual-energy CT projection data. Material decomposition is often done using pre-computed lookup tables (LUTs) for mapping input projection data, acquired with particular X-ray source parameters, to material data values. However, the X-ray source parameters can vary over the course of a scan, making the results inaccurate. Embodiments are based on determining in advance a plurality of sets of basis LUTs for each of a plurality of different possible ranges of values over which the X-ray source parameter may vary during a scan. The basis LUTs in each set are devised as being LUTs which represent the majority contribution to the overall material decomposition function for the time-varying energy spectrum. When performing a material decomposition procedure, an appropriate set of the basis LUTs is chosen, based on an identified range of variation of the X-ray source parameter during the scan, and a linear combination of the basis LUTs is defined, using weightings which depend upon instantaneous values of the X-ray source parameter during the scan period.

Method for improving ct-based material decomposition

An imaging system () includes a reconstructor () configured to reconstruct obtained cone beam projection data with a voxel-dependent redundancy weighting such that low frequency components of the cone beam projection data are reconstructed with more redundant data than high frequency components of the cone beam projection data to produce volumetric image data. A method includes reconstructing obtained cone beam projection data with a voxel-dependent redundancy weighting such that low frequency components are reconstructed with more redundant data than high frequency components to produce volumetric image data. A computer-readable storage medium, storing computer executable instructions, which when executed by a processor of a computer cause the processor to: obtain cone beam projection data and reconstruct the cone beam projection data with a voxel-dependent redundancy weighting such that low frequency components are reconstructed with more redundant data than high frequency components to produce volumetric image data.

Cone beam artifact correction for gated imaging

A reconstruction system includes a decomposer () configured to decompose at least two sets of projection data generated via kVp switching between at least two radiation source voltages. Each set corresponds to a different one of the at least two radiation source voltages. The system further includes a spectral channel () configured to process the at least two sets of projection data and generate spectral image data. The system further includes a non-spectral channel () configured to process the at least two sets of projection data and generate non-spectral image data for a predetermined reference kVp.

Imaging system configured to generate non-spectral volumetric image data from a KVP switching multi-energy acquisition

A system includes a reconstructor () configured to reconstruct cone beam projection data to generate cone beam artifact corrected short scan cone beam volumetric image data. A method includes reconstructing, with a reconstructor, cone beam projection data to generate cone beam artifact corrected short scan cone beam volumetric image data. A computer-readable storage medium storing computer executable instructions which when executed by a processor of a computer cause the processor to: reconstruct cone beam projection data to generate cone beam artifact corrected short scan cone beam volumetric image data.

An imaging system () includes an X-ray radiation source () configured to emit radiation that traverses an examination region, a detector array () configured to detect radiation that traverses an examination region and generate a signal indicative thereof, wherein the detected radiation is for a 3-D pre-scan, and a reconstructor () configured to reconstruct the signal to generate a 2-D pre-scan projection image. The imaging system further includes a console () wherein a processor thereof is configured to execute 3-D volume planning instructions () in memory to display the 2-D pre-scan projection image () and a scan plan or bounding box () for planning a 3-D volume scan of a region/tissue of interest based on a selected protocol for a 3-D volume scan of a region/tissue of interest being planned and receive an input confirming or adjusting the scan plan box to create a 3-D volume scan plan for the 3-D volume scan of the region/tissue of interest.

Protocol-dependent 2-D pre-scan projection image based on 3-D pre-scan volumetric image data

A system () includes an unlogger () configured to unlog logged data, to produce unlogged clipped data. The logged data includes attenuation line integrals and clipping-induced bias. The system further includes a mean estimator () configured to estimate a mean value of the unlogged clipped data. The system further includes a correction determiner () configured to determine correction to the clipping-induced bias based on the estimated mean value of the unlogged clipped data. The system further includes an adder () configured to correct the logged data with the correction to produce corrected logged data.

Imaging system detector clipping-induced bias correction

A medical imaging system () includes a masking unit (), an image registration unit (), a motion estimator () and a motion compensating reconstructor (). The masking unit constructs a mask for each reconstructed volumetric phase image of a plurality of reconstructed volumetric phase images that masks portions of a corresponding image external to an anatomical model fitted to a segmented at least one anatomical structure, 5 wherein the plurality of reconstructed volumetric phase images include a target phase and a plurality of temporal neighboring phases reconstructed from projection data. The image registration unit registers the masked reconstructed volumetric phase images. The motion estimator estimates motion between the target phase and the plurality of temporal neighboring phases according to the model based on the registered masked reconstructed 10 volumetric phase images. The motion compensating reconstructor reconstructs a motion compensated medical image from the projection data using the estimated motion of the registered masked reconstructed volumetric phase images.

Model regularized motion compensated medical image reconstruction

An imaging system includes a rotating gantry with a bore, and an X-ray radiation source supported by the rotating gantry, which rotates around the bore and emit X-ray radiation that traverses at least a portion of the bore. A detector array supported by the rotating gantry, located opposite the X-ray radiation source, detects the X-ray radiation having traversed an object located within the bore and generate projection data indicative of the detected X-ray radiation, wherein the projection data comprises a sinogram. A processor estimates a portion of the object truncated in the sinogram by fitting a curve to data sampled from a plurality of views of the object in the sinogram adjacent to a set of truncated views of the object in the sinogram, and reconstructs an image of the object based on the estimated portion of the object truncated and the generated projection data.

Imaging system extended field-of-view

A system () includes input/output configured to receive line integrals from a contrast enhanced spectral scan by an imaging system. The system further includes () a processor () configured to: decompose () the line integrals into at least Compton scatter and a photo-electric effect line integrals; reconstruct the Compton scatter and a photo-electric effect line integrals to generate spectral image data, including at least Compton scatter and photo-electric effect images; de-noise () the Compton scatter and photo-electric effect images; identify () residual iodine voxels in the de-noised Compton scatter and the photo-electric effect images corresponding to residual iodine artifact; and produce a virtual non-contrast image using the identified residual iodine voxels.

Detection and/or correction of residual iodine artifacts in spectral computed tomography (CT) imaging

An imaging system () includes a radiation source () configured to emit X-ray radiation, a detector array () configured to detected X-ray radiation and generate projection data indicative thereof, and a first processing chain () configured to reconstruct the projection data and generate a noise only image. A method includes receiving projection data produced by an imaging system and processing the projection data with a first processing chain configured to reconstruct the projection data and generate a noise only image. A processor is configured to: scan an object or subject with an x-ray imaging system and generating projection data, process the projection data with a first processing chain configured to reconstruct the projection data and generate a noise only image, process the projection data with a second processing chain configured to reconstruct the projection data and generate a structure image, and de-noise the structure image based on the noise only image.

Image noise estimation using alternating negation

A method includes manipulating segmented structure of interest, which is segmented from first reconstructed image data at a reference motion phase of interest, that is registered to second reconstructed image data at one or more other motion phases. The method further includes updating initial motion vector fields corresponding to the registration of the segmented structure of interest to the second reconstructed image data based on the manipulation. The method further includes reconstructing the projection data with a motion compensated reconstruction algorithm employing the updated motion vector fields.

Moving structure motion compensation in imaging

A system includes memory () with instructions for at least one of processing spectral CT projection data to mitigate at least one of noise of the spectral CT projection data or a noise induced bias of the spectral CT projection data or generating a decomposition algorithm that mitigates the noise induced bias of the spectral CT projection data. The system further includes a processor () that executes the instructions and at least one of processes the spectral CT projection data or generates the decomposition algorithm and decomposes the spectral CT projection data to basis materials. The system further includes a reconstructor () that reconstructs the basis materials, thereby generating spectral images.

Spectral imaging

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A system includes obtaining de-noised reconstructed image data and edge improving a sub-set of the de-noised reconstructed image data corresponding to edges of structure represented in the de-noise reconstructed image data. A system () includes an edge detector () that detects an edge map of edge locations within de-noised reconstructed image data, a noise image data generator () that generates noise image data by subtracting the reconstructed image data by the de-noised reconstructed image data, a noisy edge image data generator () that generates noisy edge image data by multiplying the noise image data and the edge map, and an edge improver () that generates edge improved de-noised image data by adding the noisy edge image data and a product of a weight and the de-noised reconstructed image data.

De-noised reconstructed image data edge improvement

A method includes scaling a regularization term of an update algorithm of an iterative reconstruction algorithm with regularization with a scaling value. The scaling value is variable in at least one dimension, thereby varying the regularization of the iterative reconstruction in the least one dimension. The method further includes iteratively reconstructing an image based at least on the update algorithm, the varying scaled regularization term, and projection data.

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Chardon, OH, United States of America

Kevin Martin Brown