DEEP LEARNING FOR SLIDING WINDOW PHASE RETRIEVAL

    公开(公告)号:US20230260172A1

    公开(公告)日:2023-08-17

    申请号:US18015739

    申请日:2021-07-05

    CPC classification number: G06T11/006

    Abstract: An image processing system (IPS) and related method for supporting tomographic imaging. The system comprises an input interface (IN) for receiving, for a given projection direction (pi), a plurality of input projection images at different phase steps acquired by a tomographic X-ray imaging apparatus configured for dark-field and/or phase-contrast imaging. A machine learning component (MLC) processes the said plurality into output projection imagery that includes a dark-field projection image and/or a phase contrast projection image for the said given projection direction.

    PHOTON COUNTING SPECTRAL CT
    2.
    发明申请

    公开(公告)号:US20210382188A1

    公开(公告)日:2021-12-09

    申请号:US17053890

    申请日:2019-05-07

    Abstract: A radiation detector (100) adapted for detecting leakage currents is disclosed and comprises a direct conversion material (101) for converting incident radiation, at least one first electrode (108) and a plurality of second electrodes (103) connected to surfaces of the direct conversion material (101) for collecting each generated charges upon application of an electric field, at least one current measurement device (201), and a plurality of signal processing chains (210, 220, 230). Each signal processing chain comprises a readout unit (215, 216, 217, 218, 219) for discriminating between energy values with respect to the incident radiation, and a switching element (214) for sending signals on a first signal path (2141) electrically connecting one of the plurality of second electrodes with the readout unit, or on a second signal path electrically connecting the one of the plurality of second electrodes with an input to one of the at least one current measurement devices. A plurality of switching elements is configured to send signals on the second signal path for measuring a leakage current received at a corresponding plurality of second electrodes of the detector in the absence of incident radiation.

    PROJECTION-DOMAIN MATERIAL DECOMPOSITION FOR SPECTRAL IMAGING

    公开(公告)号:US20240127500A1

    公开(公告)日:2024-04-18

    申请号:US18276902

    申请日:2022-02-15

    CPC classification number: G06T11/006 G06T5/50 G06T5/70 G06T2211/408

    Abstract: The present invention relates to a method (1), resp. a device, system and computer-program product, for material decomposition of spectral imaging projection data. The method comprises receiving (2) projection data acquired by a spectral imaging system and reducing (3) noise in the projection data by combining corresponding spectral values for different projection rays to obtain noise-reduced projection data. The method comprises applying (6) a first projection-domain material decomposition algorithm to the noise-reduced projection data to obtain a first set of material path length estimates, and applying (7) a second projection-domain material decomposition algorithm to the projection data to obtain a second set of material path length estimates. The second projection-domain material decomposition algorithm comprises an optimization that penalizes a deviation between the second set of material path length estimates being optimized and the first set of material path length estimates.

    SYSTEM FOR X-RAY DARK FIELD, PHASE CONTRAST AND ATTENUATION TOMOSYNTHESIS IMAGE ACQUISITION

    公开(公告)号:US20220146439A1

    公开(公告)日:2022-05-12

    申请号:US17435049

    申请日:2020-02-27

    Abstract: The present invention relates to a system (10) for X-ray dark field, phase contrast and attenuation tomosynthesis image acquisition. The system comprises an X-ray source (20), an interferometer arrangement (30), an X-ray detector (40), a control unit (50), and an output unit. A first axis is defined extending from a centre of the X-ray source to a centre of the X-ray detector. An examination region is located between the X-ray source and the X-ray. The first axis extends through the examination region, and the examination region is configured to enable location of an objection to be examined. The interferometer arrangement is located between the X-ray source and the X-ray detector. The interferometer arrangement comprises a first grating (32) and a second grating (34). A second axis is defined that is perpendicular to a plane that is defined with respect to a centre of the first grating and/or a centre of the second grating. The control unit is configured to control movement of the X-ray source and/or movement of the X-ray detector to provide a plurality of image acquisition states, wherein the X-ray source and X-ray detector are configured to operate to acquire image data. For each of the plurality of image acquisition states the first axis extends through the examination region at a different angle. The control unit is configured to control movement of the first grating or movement of the second grating in a lateral position direction perpendicular to the second axis. For each of the acquisition states the first grating or second grating is at a different lateral position of a plurality of lateral positions. The output unit is configured to output one or more of: dark field image data, phase contrast image data, and attenuation image data.

    DETECTION AND/OR CORRECTION OF RESIDUAL IODINE ARTIFACTS IN SPECTRAL COMPUTED TOMOGRAPHY (CT) IMAGING

    公开(公告)号:US20200027253A1

    公开(公告)日:2020-01-23

    申请号:US16470995

    申请日:2017-12-19

    Abstract: A system (300) includes input/output configured to receive line integrals from a contrast enhanced spectral scan by an imaging system. The system further includes (300) a processor (326) configured to: decompose (334) 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 (332) the Compton scatter and photo-electric effect images; identify (402) 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

    IMAGE NOISE ESTIMATION USING ALTERNATING NEGATION

    公开(公告)号:US20190385345A1

    公开(公告)日:2019-12-19

    申请号:US16463859

    申请日:2017-12-06

    Abstract: An imaging system (400) includes a radiation source (408) configured to emit X-ray radiation, a detector array (410) configured to detected X-ray radiation and generate projection data indicative thereof, and a first processing chain (418) 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.

    REDUCTION OF ARTEFACTS IN MEDICAL IMAGES
    7.
    发明公开

    公开(公告)号:US20240029214A1

    公开(公告)日:2024-01-25

    申请号:US18266071

    申请日:2021-12-03

    CPC classification number: G06T5/005 G16H30/40 G06T2207/10081

    Abstract: A mechanism for generating an artefact estimation image that represents the effect of cone-beam artefacts in a computed tomography (CT) image. This is achieved by identifying the position of gradients (being sudden changes of intensity) in an axis of the CT image parallel to a rotation axis of the CT system that generated the CT image, where each gradient represents a source of a cone-beam artefact. A look-up table is used to individually identify the effect of a cone-beam artefact on areas surrounding each identified position of the gradient, to generate an artefact estimation image.

    APPARATUS, SYSTEM, METHOD AND COMPUTER PROGRAM FOR RECONSTRUCTING A SPECTRAL IMAGE OF A REGION OF INTEREST OF AN OBJECT

    公开(公告)号:US20200348425A1

    公开(公告)日:2020-11-05

    申请号:US16962857

    申请日:2019-01-17

    Abstract: The invention relates to an image reconstruction apparatus comprising a detector value providing unit for providing detector values for each detector element of a plurality of detector elements forming a radiation detector and for each energy bin of a plurality of predefined energy bins, a correlation value providing unit for providing correlation values, wherein a correlation value is indicative of a correlation of a detector value detected by a detector element in an energy bin with at least one of a) a detector value detected by another detector element in the energy bin, b) a detector value detected by another detector element in another energy bin, and c) a detector value detected by the detector element in another energy bin, and a spectral image reconstruction unit for reconstructing a spectral image based on the detector values and the correlation values.

    SPECTRAL X-RAY MATERIAL DECOMPOSITION METHOD
    10.
    发明公开

    公开(公告)号:US20230309937A1

    公开(公告)日:2023-10-05

    申请号:US18024301

    申请日:2021-08-31

    CPC classification number: A61B6/4241 A61B6/032 A61B6/5205 A61B6/482

    Abstract: A method for material decomposition of an object based on spectral X-ray scan data for the object and based on application of a frequency split approach. The method comprises using two AI models in parallel to perform the material decomposition analysis based on input spectral X-ray data, wherein the models are configured such that one exhibits higher bias and lower variance (lower noise) than the other. The input spectral X-ray data is fed to both models. The output material composition data from the low bias model is low-pass filtered and the output material composition data from the low variance model is high pass filtered. The outputs from the two models are linearly combined, either before the filtering or after. The resulting combined material decomposition data has both lower bias and lower noise compared to the output generated if just one AI model were to be used.

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