Koninklijke Philips Corporation N.v.

Koninklijke Philips Electronics N.v.

Gilad Shechter

Liran Goshen

Bernhard Johannes Brendel

Thomas Koehler

Kevin Martin Brown

Mordechay Pinchas Freiman

Amir Livne

Reuven Levinson

Guy A Lavi

Galit Naveh

Yoav Bar

Asher Gringaus

A system (SYS) and related method for motion artifact reduction in X-ray imaging. The system (SYS) comprises an input interface (IN) for receiving a first input image (i1, I1) of an object (PAT) reconstructed from a first set of projection data, and a second input image (i2, I2) of the object reconstructed from a second set of projection data. The second set is smaller than the first set. A motion analyzer (MA) establishes an estimate for motion corruption based on the two input images. A selective combiner (Σ) computes an image value for an enhanced image (I1+I2, i1+i2), based on the motion estimate and on image information in the first input image (i1, I1) and/or the second input image (i2, I2).

Suppression of motion artifacts in computed tomography imaging

A method includes generating a material landmark images in a low and high energy image domain. The material landmark image estimates a change of a value of an image pixel caused by adding a small amount of a known material to the pixel. The method further includes generating an air values image in the low and high energy image domain. The air values image estimates a value for each image pixel where a value of a pixel is replaced by a value representing air. The method further includes extracting from de-noised low and high images generated from the low and high line integrals, a material composition of each image pixel based on the material landmark images and air values image. The method further includes generating a signal indicative the extracted material composition.

Multi-energy (spectral) image data processing

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

The invention relates to an X-ray imaging device () for an object, an X-ray imaging system () for an object, an X-ray imaging method for an object, and a computer program element for controlling such device or system and a computer readable medium having stored such computer program element. The X-ray imaging device () comprises a receiving unit () and a processing unit (). The receiving unit () is configured to receive attenuation data representing attenuation properties of the object for at least two different X-ray spectra. The processing unit () is configured to decompose the attenuation data into decomposed data, to reduce noise in the decomposed data to obtain de-noised data, to back-convert the de-noised data into back-converted attenuation data, to combine back-converted attenuation data and the attenuation data into combined attenuation data, and to decompose the combined attenuation data into combined decomposed data.

X-Ray imaging device for an object

This application describes an approach to filter, solely in the projection domain, correlated noise from (or de-noise) spectral/multi-energy projection data. As described herein, this can be achieved based at least on variances of the basis material line integrals and a covariance there between, based on multiple correlation coefficients and hyper-planes that describe the noise correlation between different basis material line integrals, and/or otherwise.

Projection data de-noising

A method includes generating landmarks, for different compositions of materials in scanned structure, wherein a landmark represents a location of a composition of materials, distorted by beam hardening, in a multi-energy Hounsfield Unit space and using the landmarks to perform an image domain material separation and quantification free of beam hardening artifacts.

Multi-energy imaging

A method includes de-noising projection data from a lower dose scan in the projection domain, reconstructing the de-noised projection data to generate volumetric image data, and de-noising the volumetric image data in the image domain. A system includes a projection domain processor () that de-noises projection data from a lower dose scan, a reconstructor () that reconstructs the de-noised projection data and generates image data, and an image domain processor () that de-noises the image data such that a noise level of the de-noise image data is substantially the same as an estimated image data noise level of image data for a higher dose scan.

Method and system for noise reduction in low dose computed tomography

A method includes extracting a fully sampled fixed kVp sinogram for a pre-determined kVp of interest from an under-sampled mixed kVp sinogram generated from a switched kVp computed tomography scan. A system includes a fixed sinogram extractor that extracts a fully sampled fixed kVp sinogram from an under-sampled mixed kVp sinogram from a switched kVp computed tomography scan. A method includes de-noising at least one of a fully sampled fixed kVp sinogram extracted from an under-sampled mixed kVp sinogram or the under-sampled mixed kVp sinogram by smoothing lower kVp measurements of the sinograms and sharpening higher kVp measurements of the sinograms.

Under-sampled, multi-energy computed tomography (CT) data acquisition data processing

A system for generating a reconstruction filter for an imaging scanner comprises a model bank that includes a model for generating the reconstruction filter, a filter criteria bank that includes filter criteria for generating the reconstruction filter, and a filter generator that generates the reconstruction filter based on the filter model and the filter criteria. In one non-limiting instance, the model is based on minimizing a cost function that includes predetermined filter criteria such as image contrast. In another non-limiting instance, the cost function includes terms relating to spatial resolution, noise and a signal visual perception in the presence of noise.

System and method for generating a tomographic reconstruction filter

A fan-to-parallel beam re-binner includes a time deflection determiner that determines time deflections for a plurality of integration periods for different detector pixels of an imaging system, a data shifter that shifts fan beam data acquired for the integration periods by the system by time offsets corresponding to the time deflections; and a data re-sorter that parallelizes the data.

Interpolation free fan-to-parallel beam re-binning

A reconstruction system reconstructs projection data generated by a detector array of an imaging system having at least one truncated row of detector modules. The reconstruction system includes a projection data generator that augments the projection data with projection data created for the at least one truncated row of detector modules. The reconstruction system further includes a convolver that convolves the augmented projection data. The reconstruction system further includes a back-projector back-projects the augmented projection data and generates volumetric image data indicative thereof.

Image reconstruction for truncated detector array

Projection data () acquired during a tomographic examination of a periodically moving object is used to reconstruct a plurality of image layers (). The image layers () are combined to generate image data at a desired phase of motion. To generate a weighting function used to combine the image layers, a reference weighting function () is generated at the desired phase. The image layers () are weighted to approximate the first weighting function (). The number of image layers and the size of a sub-region of interest are advantageously selected so that the various image layers can be stored in a relatively high speed memory portion of a computer.

Dynamic computed tomography imaging

A subject is imaged at a preselected phase point () which occurs in one or more successive cardiac cycles as the subject moves in an examination region (). An illumination window () illuminates each reconstructed voxel (A, B, C) of the subject in phase point windows () adjacent surroundingly the preselected phase point (). A temporal window () is determined which includes an integer number N of successive cardiac cycles, in which the phase point windows () are entirely included in the illumination window (). The illumination window () is truncated in accordance with the determined temporal window (). First and second regions () are determined along an axial direction (Z), the first and second regions () alternating with one another. A temporal weighting profile processor () generates a projection dependent temporal weighting profile (). A temporal window processor () applies the normalized backprojection weights for each voxel which lies in an associated first region. A varying weight processor () continuously smoothly weights readings for each voxel lying in an associated second region (). A backprojection processor () three-dimensionally backprojects the weighted readings into a volumetric image representation.

Bands artifact reduction for cardiac CT imaging

A tomographic apparatus includes a radiation source () having a plurality of focal spots (F, F, F, F) and a detector () which generates output signals indicative of radiation received along a plurality of rays. A height rebinner () performs a height rebinning of the acquired rays to generate height interpolated readings. A transverse rebinner () performs a two-dimensional transverse interpolation of the height interpolated rays in a canonical space to generate a plurality of transversely interpolated readings (). The transversely interpolated readings () are reconstructed to generate a human readable image.

Rebinning for computed tomography imaging

In a diagnostic imaging system () two-dimensional projection data is collected in a data memory (). The data is sorted into the data sets collected during selected cardiac phases. A re-binning processor () re-bins the projection data into a parallel ray format. An adaptive filter () filters the parallel ray format data with each of a plurality of different filter channels based on a calculated photonic noise of each reading and assuming an arbitrary noise reduction factor, different for each filter channel. A convolver () convolves the data filtered with each of the filter channels. A noise reduction factor processor () determines the actual noise reduction factor occurring due to the weighted averaging of readings contributing to each voxel &#x3bd; at an angle &#x3b8;&#x3b5;[0, 7&#x3c0;). A weighting processor () weights the convolved data from each of the filter channels based on the channels' noise reduction factors and the actual noise reduction factor. A backprojector () backprojects the combined weighted sum of projections to build an image representation that can be displayed in a human readable format.

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Haifa, Israel

Gilad Shechter