338results about "Radiation diagnostic image/data processing" patented technology

The invention provides an artificial intelligence auxiliary diagnosis method and system for a knee joint, and the method comprises the steps: synchronously collecting a multi-angle dynamic X-ray image and six-degree-of-freedom contact force distribution data in an articular cavity, dynamically tracking a patella sliding track, and generating a mechanical phase track; a personalized knee joint model is constructed in combination with the biological parameters of the lower limbs of the patient, the mechanical phase trajectory and the contact force data vector are superposed to generate a dynamic mechanical fingerprint, and then the dynamic mechanical fingerprint is mapped into a biomechanical attribute map; electromyographic signal time-frequency characteristics and tibia rotation angles in the movement process are collected, an electromyographic activation mode is constructed, and the electromyographic activation mode and a biomechanical attribute map are subjected to characteristic fusion to form a joint dynamics comprehensive contour; analyzing a stress wave conduction path by using a joint anomaly dynamic propagation model to identify a compensatory injury mode; a diagnosis map is generated according to the recognition result, a preset biomechanical constraint condition is matched to correct the diagnosis map, and an individualized rehabilitation strategy is formulated. The treatment effect is improved, and the recovery speed of a patient is increased.

A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components. The method also comprises applying machine learning to the determined radiomic characteristics associated with each candidate coronary artery calcified component to identify any calcifications that are located on a coronary artery, analysing the cardiac non-contrast CT data to identify at least one body component in the cardiac non-contrast CT data not associated with a coronary artery of the patient, and using the identified at least one body component in the cardiac non-contrast CT data to remove or avoid misclassification of calcifications on a coronary artery that are located on the at least one identified body component.

A system and method combine optical and radiographic data to enhance imaging capabilities. Specifically, the system combines visually obtained patient pose position information and radiographic image information to facilitate calibrated surgical navigation. A reconstruction of a 3D CT volume is generated from biplanar X-ray projections which are back projected into two separate volumes and then concatenated into a single volume along a new dimension and passed through a pretrained deep learning model to decode the concatenated volume into a single 3D volume.

A calibration target for use with a radiographic image detector includes a target body securable to the image detector and a plurality of radiopaque linear markers, e.g. wires, fixed to the target body, wherein access to the image detector by incident radiation is at least partially blocked by the plurality of linear markers. By using the geometric properties of wires and advanced detection techniques, a precise calibration suitable for high-quality, volumetric three-dimensional CT reconstruction from biplanar X-ray images is achieved.

The invention relates to an image registration method, an image registration device, an electronic device and an image registration system. According to a first mapping relation between an intra-operative two-dimensional image coordinate system and a reference coordinate system of an intra-operative two-dimensional image, the image registration method and the image registration device are obtained; obtaining a first registration matrix between the reference coordinate system and the preoperative three-dimensional image coordinate system according to a first mapping relation between the reference coordinate system and the preoperative three-dimensional image coordinate system and a second mapping relation between the preoperative three-dimensional image coordinate system and the intra-operative two-dimensional image coordinate system of the preoperative three-dimensional image; wherein the intraoperative two-dimensional image and the preoperative three-dimensional image both comprise a target object; a simulated two-dimensional image obtained after the preoperative three-dimensional image is projected to the two-dimensional space in a simulated mode is obtained; according to the similarity between the intraoperative two-dimensional image and the simulated two-dimensional image, adjusting parameters of the first registration matrix to obtain a second registration matrix; according to the invention, the wound exposure time can be reduced during image registration, and the operation is convenient.

A computer tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (116) configured to detect x-ray radiation and generate a signal indicative thereof, and a reconstructor (118) configured to reconstruct the signal and generate sequential spares time line perfusion volumetric image data. The computer tomography scanner further includes a processor (132) configured to process the sequential spares time line perfusion volumetric image data using a trained neural network of a perfusion data enhancing module (136) to produce sequential dense time line perfusion volumetric image data.

The invention relates to the technical field of medical image analysis, in particular to an image-based tumor curative effect prediction method and system, and the method comprises the following steps: based on CT image data of multiple time points before and after treatment, extracting inter-frame pixel position coordinates of the CT image data, calculating an offset vector of an inter-frame pixel position, and implementing inter-frame position calibration; and generating time sequence image correction data. According to the method, accurate calibration of inter-frame positions is achieved through inter-frame pixel position coordinate extraction and offset vector calculation of multi-time-point CT image data, and through a set expansion threshold value and area boundary fitting, the outer contour of a tumor area is recognized, and a pixel point coordinate set of the tumor area boundary is dynamically analyzed, so that accurate calibration of the tumor area is achieved. According to the method, the position change of the tumor in the time sequence is evaluated, key data and density distribution characteristic analysis are provided for predicting the treatment response of the tumor, evaluation of the structural uniformity of the tumor area is supplemented, a basis is provided for individualized treatment prediction, and the change trend of tumor treatment can be effectively predicted.

The embodiment of the utility model discloses a detector micro block and a detector block for emission computed tomography. The detector microblock may include detector cells arranged side-by-side in a second direction. Each detector cell may include a crystalline element and an array of optical sensors. The crystal elements may be arranged in a row of crystal elements in a second direction and in a column of crystal elements in a first direction perpendicular to the second direction. Each crystal element may include a first end and a second end and extend from the first end to the second end in a third direction perpendicular to the first direction and the second direction. The optical sensor array may include optical sensors arranged in a first direction.

The present disclosure relates to a method for imaging and an imaging system. The method includes obtaining a scanning protocol for an examination subject, which includes: obtaining calibration data associated with the scanning protocol in response to a commonly used scanning protocol calibration data set of a medical imaging system including the calibration data; or performing a calibration operation in response to a commonly used scanning protocol calibration data set of a medical imaging system not including calibration data associated with the scanning protocol, to generate calibration data associated with the scanning protocol. The method further includes scanning the examination subject based on the scanning protocol, to obtain raw imaging data, and performing reconstruction using the calibration data associated with the scanning protocol and the raw imaging data, to obtain a medical image of a scanned subject.

A calibration target for use with a radiographic image detector includes a target body securable to the image detector and a plurality of radiopaque linear markers, e.g. wires, fixed to the target body, wherein access to the image detector by incident radiation is at least partially blocked by the plurality of linear markers. By using the geometric properties of wires and advanced detection techniques, a precise calibration suitable for high-quality, volumetric three-dimensional CT reconstruction from biplanar X-ray images is achieved.

The invention discloses a PET image reconstruction method and device with a self-attenuation correction function, a medium and a product. The method comprises the steps that measurement data and the total tracer agent concentration corresponding to the measurement data are obtained, and the measurement data are obtained through a positron emission tomography imaging method based on flight time; on the basis of the measurement data, the total concentration, a pre-stored concentration map updating rule, a pre-stored attenuation sinogram updating rule and a pre-stored sensitivity matrix, the concentration map and the attenuation sinogram are alternately updated until iteration is finished, a target concentration map and a target attenuation sinogram are obtained, and the target concentration map serves as a target PET image; the concentration map update rule and the attenuation sinogram update rule are both determined based on a minimization model, the minimization model is used for minimizing a negative logarithm maximum likelihood function of a measurement data model, the minimization model comprises a concentration constraint term, and the measurement data model is used for constraining attenuation of measurement data in an exponential form. According to the embodiment of the invention, the PET image with high image quality can be reconstructed through self-attenuation correction.

The embodiments of the present disclosure provide a method and system for computed tomography (CT) imaging. The CT imaging method comprises: obtaining scanning data of a target subject, the scanning data being acquired by scanning the target subject using a CT device; determining data after air correction by performing air correction on the scanning data using an air correction table; determining projection data of at least two standard materials by performing basis material decomposition on the data after air correction; and generating a target image based on the projection data of the at least two standard materials.

Embodiments of the present disclosure may provide a system, a method, and a device for X-ray imaging. The method may include: obtaining a grayscale of a current frame of an object, wherein the current frame is obtained by performing X-ray imaging on the object based on current performing parameters; determining an updated equivalent phantom thickness of the object based on the grayscale of the current frame, the current performing parameters, and a pre-stored correspondence, wherein the pre-stored correspondence reflects dose values corresponding to a plurality of equivalent phantom thicknesses and a plurality of performing parameters; and determining target performing parameters of a next frame of the object based on the updated equivalent phantom thickness, a target grayscale, and the pre-stored correspondence.

The present invention relates to the technical field of radiology, and in particular to assisting radiologists in radiological examinations using artificial intelligence methods. The present invention relates to training a machine learning model and using the trained model to predict representations of an examination area after applications of different amounts of a contrast agent.

The invention provides an imaging method, device and equipment of an interventional instrument. The imaging method comprises the following steps: acquiring a movement track and a current position of the interventional instrument in an interventional environment; according to the motion trail and the current position, a local scanning visual field area of the imaging equipment is determined, and the local scanning visual field area at least comprises an expected advancing path of the interventional instrument; and imaging the interventional instrument according to the local scanning view area to obtain a local image of the interventional instrument. The radiation quantity in the imaging process of the interventional instrument is reduced, and the imaging accuracy is improved.

Systems and methods are provided for increasing a quality of computed tomography (CT) images. In one embodiment, a computed tomography (CT) detector system comprises a layer of energy integrating detectors (EID) arranged below a layer of photon counting (PC) sensors with respect to an incoming x-ray, where a number of the PC sensors exceeds a number of the EID detectors; and an image processing unit configured to correct PC data using EID data, and denoise and increase a resolution of an image reconstructed from EID data and PC data using a deep learning convolutional neural network (CNN) trained on pairs of images, each pair of images including a target image reconstructed from a first signal from the layer of PC sensors, and an input image reconstructed from a second signal from the layer of EID detectors, the EID data and PC data acquired concurrently from a same patient ray path.

The present disclosure provides Emission Computed Tomography (ECT) detector assemblies and related methods. The ECT detector assemblies may include a detector micro-block, a detector block, etc. The detector micro-block may include detector units arranged side by side along a second direction. Each of the detector units may include crystal elements and an optical sensor array. The crystal elements may be arranged in crystal element rows along the second direction and crystal element columns along a first direction perpendicular to the second direction. Each crystal element may have a first end and a second end and extending from the first end to the second end along a third direction perpendicular to the first direction and the second direction. The optical sensor array may include optical sensors arranged along the first direction.

A system and method combine optical and radiographic data to enhance imaging capabilities. Specifically, the system combines visually obtained patient pose position information and radiographic image information to facilitate calibrated surgical navigation. The process involves a data acquisition phase, a system calibration phase, a volume reconstruction phase, and a surgical navigation phase, all resulting in the alignment of instrument coordinates with the patient and reconstructed volume coordinates enabling tracking and navigation of surgical instruments within a reconstructed 3D volume of the patient anatomy, even if such anatomy is not exposed during a procedure.

The present invention relates to the technical field of radiology, in particular to the field of assisting radiologists performing radiological examinations by using artificial intelligence methods. The present invention also relates to the training of a machine learning model and the use of the trained model for predicting a representation for an examination region in one or more states of a sequence of states in a radiological examination.

The present invention relates to the technical field of radiology, in particular to the field of assisting radiologists performing radiological examinations by using artificial intelligence methods. The present invention also relates to the training of a machine learning model and the use of the trained model for predicting a representation for an examination region in one or more states of a sequence of states in a radiological examination.

The present invention relates to a medical image conversion method, and more particularly, to an image conversion method for converting medical images using a generative adversarial network (GAN). According to one embodiment of the present invention, there is provided a medical image conversion method that enables medical staff to perform medical examinations, diagnoses, and treatments with accurate information through conversion between medical images using a learning model that performs machine learning, thereby enabling patients to receive appropriate medical services.

A multi-spectral tomography imaging system includes one or more source devices configured to direct beams of radiation in multiple spectra to a region of interest (ROI), and one or more detectors configured to receive the beams of radiation. The system includes a processor configured to cause movement in at least one of the components such that a first beam of radiation with a first spectrum is directed to the ROI for less than 360 degrees of movement of the ROI. The processor is also configured to process data detected by the one or more detectors, where the data results at least in part from the first beam of radiation with the first spectrum that is directed to the ROI for less than the 360 degrees of movement of the ROI. The processor is further configured to generate an image of the ROI based on the processed data.

A radiation imaging apparatus comprising pixels, a driver controlling the pixels via driving lines, a readout circuit reading out, via column signal lines, signals from the pixels and a detector detecting irradiation information of radiation separately from a radiation image, is provided. Each of the column signal lines is connected to pixels arranged on two pixel columns. The pixels include a first pixel and a second pixel, whose sensitivities are different from each other. The first and second pixels are connected to a common column signal line and are connected to driving lines different from each other. When detecting the irradiation information, the driver drives the first and second pixels at timings different from each other, and the detection circuit detects the irradiation information based on signals output from the first and second pixels.

The invention relates to a method for medical imaging and a medical imaging system. The method comprises the following steps: acquiring a scanning protocol for an inspection object; aiming at the scanning protocol: responding to calibration data associated with the scanning protocol included in a common scanning protocol calibration data set of the medical imaging system, and obtaining the calibration data; or in response to the fact that the calibration data associated with the scanning protocol is not included in the common scanning protocol calibration data set of the medical imaging system, performing calibration operation to generate the calibration data associated with the scanning protocol; scanning the examination object based on a scanning protocol to obtain original imaging data; and using calibration data associated with the scanning protocol and the original imaging data for reconstruction to obtain a medical image of the scanned object.

The present invention relates to the field of image processing technology, and discloses a method, device, equipment and medium for determining the coronary blood flow reserve fraction, the method comprising: obtaining a sequence of coronary angiography images to be processed, coronary blood flow parameters and coronary artery ostium pressure parameters; extracting a reference image and a floating image from the sequence of coronary angiography images; performing three-dimensional reconstruction processing on the reference image and the floating image to generate a three-dimensional coronary artery image; extracting a coronary artery stenosis area from the three-dimensional coronary artery image; and determining the coronary blood flow reserve fraction of the coronary artery stenosis area based on the coronary blood flow parameters and the coronary artery ostium pressure parameters. The method for determining the coronary blood flow reserve fraction provided in this embodiment can objectively and accurately determine the coronary blood flow reserve fraction of the coronary artery stenosis area by determining the coronary blood flow parameters and the coronary artery ostium pressure parameters.

The application provides a femoral head necrosis staging judgment method, system, device and storage medium, comprising: acquiring femoral head necrosis X-ray images, using a target detection model to identify all femoral head necrosis X-ray images to obtain an identification confidence; using a target segmentation model to segment the femoral head necrosis X-ray images respectively to obtain a plurality of segmentation regions, and giving each segmentation region a region label; dividing the femoral head necrosis X-ray images into a plurality of regions of interest, giving all regions of interest corresponding region labels according to the positions of the regions of interest; calculating the segmentation confidence of each region of interest according to the region labels, and calculating the probability of the femoral head necrosis X-ray images in each stage according to the segmentation confidence and the identification confidence, and selecting the stage with the maximum probability as the femoral head necrosis stage. The application solves the problem of low staging accuracy caused by manual femoral head necrosis staging in the prior art.

The present invention relates to a dual-input four-chamber model for the evaluation of dynamic PET / CT imaging of hepatocellular carcinoma. The present invention collects HCC dynamic PET / CT data for pathological diagnosis, including 5 minutes of dynamic data supplemented by 1 minute of static data 60 minutes after injection and a single 5 minutes of dynamic data; secondly, the tracer time-radioactivity curve data of normal liver tissue, hepatic artery, portal vein, and hepatocellular carcinoma are extracted from the PET / CT images; then the gravitational search algorithm is used to solve the kinetic parameters of the liver 4-chamber model; finally, the correlation between the kinetic parameters K1~k3 of the two time groups and the difference in the tracer kinetic parameters of the hepatocellular carcinoma tissue image and the surrounding healthy liver tissue image are analyzed and compared, and compared with the commonly used liver 3-chamber model kinetic parameters, the present invention can better characterize the metabolism of HCC and normal liver tissue, and has certain value for clinical auxiliary diagnosis of HCC.

A technology is provided that enables image reconstruction or supports a reconstruction method without having to assume a specific periodicity for the movement of a recorded object. According to one embodiment, an information processing system for processing projection images acquired with a CT device is provided. The information processing system includes a feature acquisition unit, a spectral decomposition unit, and a synchronization processing unit. The feature acquisition unit is configured to acquire waveform data representing feature quantities obtained from the respective projection images.The spectral decomposition unit is configured to obtain decomposed waveform data by applying a predetermined analysis method to the waveform data, wherein the decomposed waveform data is data of waveforms obtained by decomposing the waveform data. The synchronization processing unit is configured to determine, based on a waveform represented by the decomposed waveform data, those of the projection images to be used for reconstruction.

The application provides a magnetocardiogram registration system and method based on optical scanning, and the system comprises a bed body registration module and a CT image registration module. The bed body registration module comprises a first arc-shaped marker and an L-shaped base, and the CT image registration module comprises a second arc-shaped marker which is consistent in shape with the first arc-shaped marker of the bed body registration module. Both the bed body registration module and the CT image registration module are made of non-magnetic photosensitive resin material. Before magnetocardiogram (MCG) acquisition, the two registration modules are digitized by using a handheld optical scanner. According to the bed body registration module, the sensor array coordinate system and the human body coordinate system are aligned. When CT scanning is performed, the image structure of the trunk part of a subject is obtained. According to the CT image registration module, the human body coordinate system and the CT coordinate system are aligned. Correspondingly, the application also provides a registration method corresponding to the system, which significantly improves the efficiency and accuracy of the MCG registration process and promotes the subsequent medical research and application of MCG.

To provide a data processing method for static computed tomography scanning and a device.SOLUTION: The data processing method for static computed tomography scanning includes: performing, in response to receiving a beam synchronization pulse signal, a time synchronization on N angle pulse signals and a time synchronization on N belt pulse signals by using the beam synchronization pulse signal, so as to obtain N synchronization angle pulse signals and N synchronization belt pulse signals, respectively (operation S210); generating N timestamps based on the N synchronization angle pulse signals and the N synchronization belt pulse signals, where the N timestamps correspond to N scanning imaging systems of a static computed tomography scanning device, respectively, and each of the N timestamps includes angle data and belt data (operation S220); and packaging beam data, detection data, the angle data, and the belt data corresponding to each of the N scanning imaging systems to obtain N data packets (operation S230).SELECTED DRAWING: Figure 2