550results about "Radiation diagnostic image/data processing" patented technology

Disclosed herein is a method of assisting finding region matching in medical images, the method comprising: receiving a second medical image generated at a different time and containing anatomical information about a body part of a patient that is the same as that of a first medical image; receiving a user input for a first finding region, unmatched with second finding regions identified in the second medical image, among first finding regions identified in the first medical image; and for the unmatched first finding region, providing a user with one or more matching finding region candidates among the second finding regions for matching with the unmatched first finding region.

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.

There is provided a computed tomography imaging system including a gantry including a rotating member on a rotating side, a stationary member on a stationary side, and a data communication system. The rotating member on the rotating side includes an X-ray source configured to emit X-rays, an X-ray detector configured to generate detector data, a data storage unit configured to store the detector data, and processing circuitry configured to process at least part of the stored detector data to generate a processed data set. The stationary member on the stationary side is communicatively coupled to the rotating member on the rotating side, and the data communication system is configured to transfer the processed data set from the rotating member on the rotating side to the stationary member on the stationary side.

The invention provides a lung CT lesion and related factor correlation analysis method, which comprises the following steps: establishing a dynamic compensation model according to blood glucose level fluctuation data of a patient, adjusting CT scanning parameters by monitoring a blood glucose value in real time, and compensating tissue density change in a scanning process to obtain corrected original image data; performing microvascular structure enhancement on the preliminarily reconstructed CT image, extracting blood vessel features, calculating the probability that each voxel belongs to a blood vessel structure, and performing enhanced display on a microvascular network in combination with local structure tensor analysis; performing fine segmentation of the leaflet spacing and the microbronchus wall on the image after the microvessel enhancement, extracting a fine structure of the lung through multi-scale feature fusion, and performing post-processing to optimize a segmentation result; and performing anomaly detection on the whole lung area, extracting characterization characteristics of normal and abnormal tissues, and accurately positioning a suspicious focus area in combination with a multi-instance learning strategy, thereby providing auxiliary diagnosis information for early lung cancer screening.

The invention discloses a PICC (Peripherally Inserted Central Catheter) positioning examination method based on an X-ray chest radiograph and a related device. The method comprises the following steps: acquiring an X-ray chest radiograph image of a target patient; establishing an X-ray chest radiography PICC positioning model to identify an anatomical structure and a PICC catheter path in the X-ray chest radiography image; the X-ray chest radiography PICC positioning model comprises an anatomical structure identification unit, a catheter path identification unit and a position evaluation unit; the anatomical structure identification unit is used for identifying an anatomical structure in the X-ray chest radiography image; the catheter path identification unit is used for identifying a PICC catheter path in the X-ray chest radiography image, and the PICC catheter path comprises a starting point, a walking path and a tail end position of a catheter; the position evaluation unit is used for evaluating the position of the tail end of the catheter according to the anatomical structure and the PICC path and generating a position evaluation result; and examining the positioning of the PICC according to the position information text of the tip of the PICC and the position evaluation result. The accuracy and efficiency of PICC positioning examination are improved through the X-ray chest radiography PICC positioning model.

The embodiments of the present disclosure provide a method for image generation. The method may comprise: obtaining a three-dimensional (3D) base image of a target subject, the 3D base image being captured by performing a 3D scan on the target subject with a preset posture; obtaining one or more two-dimensional (2D) scout images of the target subject, the one or more 2D scout images of the target subject being captured by performing a scout scan on the target subject with the preset posture after an internal structure of the target subject changes; generating a 3D predicted image of the subject based on the 3D base image and the one or more 2D scout images, the 3D predicted image indicating the internal structure of the target subject when the one or more 2D scout images are captured.

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.

The invention relates to an X-ray fluoroscopic image generation method and device and an X-ray fluoroscopic examination system. The generation method comprises the steps that a target sight vector for a target organ is determined according to observation requirements and the target position where the target organ of a living body needing to be detected is located; determining a primary rotation angle and a secondary rotation angle of an imaging arm in the X-ray fluoroscopy equipment based on the target sight line vector, and determining a primary rotation matrix and a secondary rotation matrix; determining a target projection matrix conforming to a target projection geometrical relationship based on the primary rotation matrix and the secondary rotation matrix; and based on the target projection matrix and pre-acquired three-dimensional computed tomography (CT) data of the target organ, performing volume rendering to obtain a two-dimensional target X-ray perspective image under the target observation view angle. According to the generation method and device of the X-ray perspective image and the X-ray perspective inspection system, the X-ray perspective image under the expected observation view angle can be freely generated based on the three-dimensional CT data obtained in advance.

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 present invention relates to method for medical imaging, the method being characterized in that it comprises the implementation, by a data processor (11b) of a second server (1b), of steps of: (a) Obtaining at least one candidate pre-contrast image and a candidate contrast image respectively depicting a body part prior to and after an injection of a first dose of contrast agent, wherein said first dose is higher than a predetermined low dose, the predetermined reduced dose being lower than a predetermined standard dose; a convolutional neural network, CNN, being trained to reconstruct, from at least a pre-contrast input image and a low dose contrast input image respectively depicting a body part prior to and after an injection of said low dose of contrast agent, a standard dose contrast image depicting said body part after an injection of said standard dose of contrast agent; (b) Simulating an injection of a second dose of contrast agent which is higher than the standard dose, wherein said simulating comprises generating a synthetic contrast image by applying the CNN to the at least one candidate pre-contrast image and the candidate contrast image.

The invention discloses a brain-computer interface implantation accuracy judgment method based on combination of images and electroencephalogram signals, and belongs to the field of biological cross scientific research. By combining an image processing technology and fine electroencephalogram signal analysis, the accuracy of electrode implantation is evaluated from two independent and complementary angles. Compared with a traditional method depending on postoperative histological analysis, the method has the advantages that multiple real-time feedback mechanisms are provided, and the accuracy and reliability of electrode positioning in an operation are remarkably improved. The technical innovation not only shortens the operation time and reduces the operation risk, but also improves the success rate of the whole operation process. Through real-time image and signal analysis, an operation team can adjust an operation strategy in real time, an electrode implantation path is optimized, and it is ensured that an electrode accurately reaches a target brain area. Besides, the delay and uncertainty of relying on postoperative slice analysis are avoided, so that the correctness of the electrode position can be confirmed during the operation, and the overall safety and effect of the brain-computer interface operation are enhanced.

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.

A computer-implemented method for position control of an object under investigation before acquiring a projective X-ray image of a region of interest, the computer-implemented method comprising: receiving object information of the object under investigation in an examination pose; retrieving an object model relating to the examination pose and the region of interest; adapting the object model to the object under investigation based on the object information; and evaluating the examination pose based on the adapted object model.

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.

Provided is a management apparatus including: a storage that stores quality control information, which is information on an imaging capability of a radiography apparatus; and a hardware processor that determines whether dynamic imaging by the radiography apparatus is possible in response to a dynamic imaging request, based on the quality control information.

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.

The invention discloses a CT real-time 4D gating imaging method based on projection data. The method comprises the following steps: 1) acquiring a CT projection of a target to be detected and dividing the CT projection into a plurality of groups according to an acquisition angle; carrying out pixel combination on each CT projection and then adopting an n-dimensional vector for representation; an original matrix is constructed according to the vectors corresponding to the same angle group, principal component information is extracted from the matrix based on a PCA method, and superposed signals containing respiration and heartbeat are obtained; 2) separating breathing and heartbeat signals from the superposed signals; performing respiratory gating processing on the CT projection according to the respiratory signal, and performing gating division on the CT projection in the respiratory movement period; carrying out heartbeat gating processing on the CT projection in the breathing stationary period according to the heartbeat signal; and 3) reconstructing the CT projection groups after breathing and heartbeat gating processing to obtain a 4D gating image of the to-be-detected target. The real-time performance of CT gating imaging is achieved, and meanwhile the gating information extraction precision is improved.

The invention relates to the field of intelligent assistance, and particularly discloses a gastrointestinal surgical operation assistance system and method, and the system comprises the steps: obtaining a gastrointestinal CT scanning image of a target patient object, carrying out the image preprocessing of the gastrointestinal CT scanning image through employing an image recognition and processing algorithm based on computer vision and AI, and obtaining a gastrointestinal CT scanning image; and then, extracting a trunk semantic feature and a boundary semantic feature from the preprocessed image, and intelligently generating an image semantic segmentation result containing the marked tumor boundary based on fine-grained enhanced fusion representation between the two features. Therefore, small changes which are difficult to identify by human eyes can be captured, so that tumor boundary information can be identified and labeled more accurately, and intuitive visual assistance is provided for doctors. In addition, the automatic processing flow significantly reduces the manual analysis time of doctors, and improves the diagnosis speed and efficiency.

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.