73 results about "Enhanced ct" patented technology

A system and method are disclosed for reconstructing contrast-enhanced CT images that are substantially free of beam-hardening artifacts. An imaging system includes a radiation source configured to project radiation toward an object to be scanned and an energy discriminating detector assembly having a plurality of detector elements and configured to detect radiation emitted by the radiation source and attenuated by the object to be scanned. The imaging system also includes computer programmed to count a number of photons detected by each detector element and associate an energy value to each counted photon and determine a material composition of a CT view from the number of photons counted and the energy value associated with each counted photon. The computer is also programmed to apply a weighting to the CT view based on the material composition of the CT view and reconstruct an image with differential weighting based on the weighting of the CT view.

A method for single photon counting transmission computed tomography (CT) is described. The method is based on an apparatus consisting of a radiation source and detectors on an opposite side of the subject from the source. The radiation source is for example an X-ray tube. The detectors are independently connected to parallel, fast, low-noise processing electronics capable of recording and counting individual X-ray photons at very high rate. In one embodiment of the invention, said detector is made of a scintillator coupled to a photodetector. The photodetector can be an avalanche photodiode (APD). The method comprises the steps of: directing the low energy radiation source toward the subject; detecting the radiation transmitted through the subject towards the detectors and recording the position and energy of each individual X-ray photon; rotating the radiation source and detectors around the subject; recording data for each position of the radiation source and detectors around the subject to form projections; and creating a CT image from the recorded projection data. The proposed method allows enhancing CT image contrast and reducing radiation dose to the patient by counting individual X-ray photons.

CT imaging is enhanced by adaptively filtering x-ray attenuation data prior to image reconstruction. Detected x-ray projection data are adaptively and anisotropically filtered based on the locally estimated orientation of structures within the projection data from an object being imaged at a plurality of rotation positions. The detected x-ray data are uniformly low pass filtered to preserve the local mean values in the data, while the high pass filtering is controlled based on the estimated orientations. The resulting filtered data provide projection data with smoothing along the structures while maintaining sharpness along edges. Image noise and noise induced streak artifacts are reduced without increased blurring along edges in the reconstructed images. The enhanced image allows reduced x-ray dose while maintaining image quality.

The invention relates to a novel multifunctional ultrasound contrast agent and a preparation method thereof. The multifunctional ultrasound contrast agent of the invention is a nanoparticle lipid emulsion containing a core-shell structure, wherein materials of a shell membrane comprise lecithin, cholesterin and magnetic nanometer materials; the center is coated by lqiuid fluorocarbon; and magnetic nanoparticles are inlaid on the shell membrane. The ultrasound contrast agent of the invention is a specificity contrast agent of a reticuloendothelial system, thereby being capable of not only strengthening ultrasonoscopy, but also enhancing CT and MRI development, having good development effects, and having wide application prospects.

The invention discloses a coronary three-dimensional reconstruction calcified plaque removing method. The concrete steps of the method are divided into six steps: in the step S1, starting is performed; in the step S2, non-enhanced CT and enhanced CT image data of a patient are read; in the step S3, calcified plaque identification is performed on the non-enhanced image data; in the step S4, coronary segmentation and three-dimensional coronary model reconstruction are performed on the enhanced CT image; in the step S5, two three-dimensional spaces of the calcified plaque and a coronary grid are calibrated and the calcified plaque and the coronary grid are arranged in the same coordinate system; finally the obtained grid is a coronary blood three-dimensional image model of which the calcified plaque is removed; and the process enters the step S6 and ends. The non-enhanced CT and the enhanced CT are compared so that the non-enhanced identification calcified plaque can be removed from the enhanced CT coronary three-dimensional model, and an accurate coronary three-dimensional blood form model can be acquired; and support is provided for clinic narrow assessment and hydrodynamic blood dynamic analysis.

Methods for reconstructing an image can include, inter alia, (i) reconstructing a contrast-enhanced output CT image from a nonenhanced input CT image, (ii) reconstructing a nonenhanced output CT image from a contrast-enhanced CT image, (iii) reconstructing a dual-energy, contrast-enhanced output CT image from a single-energy, contrast-enhanced CT image, and/or (iv) reconstructing a full-dose, contrast-enhanced CT image from a low-dose, contrast-enhanced CT image.

A multiple modality imaging system 10 for cardiac imaging includes x-ray scanners 24,30 which acquires contrast enhanced CT projection data of coronary arteries with a laterally offset flat panel detector and SPECT imaging scanners 40a,40b, which share the same examination region and gantry as the x-ray scanners, acquires nuclear projection data of the coronary arteries. A CT reconstruction processor 34 generates a 3D coronary artery image representation, at least one planar coronary artery angiogram, and a 3D attenuation correction map from the acquired CT projection data. A SPECT reconstruction processor 44 corrects the acquired nuclear projection data based on the generated attenuation correction map and generates a SPECT image representation of the coronary arteries from the corrected nuclear projection data. A fusion processor 54 combines the nuclear image representation, the 3D vessel image representation, and the at least one planar vessel angiogram into a composite image.

The invention provides a pulmonary nodule determining method and device and a realization device. The method comprises that a CT image and position information of a suspected area in the CT image areobtained; the CT image is enhanced; according to the position information, image data of the suspected area is extracted from the enhanced CT image; the image data of the suspected area is normalized;and the normalized image data is input to a pre-established pulmonary nodule determining model which is trained via a 3D convolutional neural network, and whether the suspected area refers to a pulmonary nodule is determined according to an output result. According to the invention, the distinguishing characteristic between the pulmonary nodule and non-nodules can be amplified by image enhancement and normalization, the 3D convolutional neural network can be used determine whether the suspected area refers to the pulmonary nodule, and the pulmonary nodule can be identified more accurately.

The invention discloses a method for enhancing CT image quality and resolution based on deep learning. The method comprises the following steps: S1, preprocessing acquired clinical data to obtain a data set; S2, constructing a deep learning model comprising a generation network, a decision device network and a sensing network; S3, constructing a loss function; S4, updating parameters of the iterative generation network by using the data set and the loss function to obtain a trained deep learning model; and S5, inputting the low-quality low-resolution image into the trained deep learning modelto obtain a high-quality high-resolution image. According to the invention, a deep learning model is constructed based on deep learning, and clinical data is preprocessed to obtain a data set, so thatthe influence of dislocation of data acquired at different times in space due to patient displacement or other reasons can be reduced; by combining a deep learning model of a loss function, the end-to-end processing of two tasks of CT image quality improvement and super-resolution can be realized to directly obtain a final result.

The invention provides a method and a device for enhancing a CT image sequence. The method includes the following steps: converting each CT image in a to-be-enhanced CT image sequence into a gray image, scaling the gray images into images with a preset size, horizontally stacking and combining the images to get a 3D stereoscopic image, and vertically segmenting the 3D stereoscopic image to get a first preset number of to-be-enhanced 2D images with a first size; inputting the first preset number of to-be-enhanced 2D images with the first size to an image enhancement model in turn, and outputting a first preset number of enhanced 2D images with a second size; and vertically arranging and combining the first preset number of enhanced 2D images with the second size into an enhanced 3D stereoscopic image, horizontally segmenting the enhanced 3D stereoscopic image into a second preset number of target 2D images with the preset size, and taking the second preset number of target 2D images with the preset size as an enhanced CT image sequence corresponding to the to-be-enhanced CT image sequence. Enhancement of the to-be-enhanced CT image sequence is realized.

The invention provides a CT medical image data enhancement method for cholelithiasis based on a lightweight convolution neural network, which includes constructing CT medical image data enhancement convolution neural network for cholelithiasis, wherein The network consists of four convolution units; Firstly, CT medical image dataset of cholelithiasis is constructed as the input of neural network,and the image is enhanced by edge information and redundant information is removed. Then, the image is cut according to the segmentation threshold to form many image blocks, and the dataset is expanded by scaling, rotation and translation. Convolution neural network (CNN) is trained continuously with data set, and adaptive lifting neural network (ALN) is used to extract image features, perform stretch contrast, equalize histogram and reconstruct image. the Convolution neural network model which can be used to enhance CT medical images of cholelithiasis can be generated. The method can realizereal-time enhancement of cholelithiasis CT medical image data, and achieve good visual and medical effects.

The invention provides an enhanced RTS (ready to send)-enhanced CTS (clear to send) protocol method for supporting uplink MU-MIMO (multi-user multiple input multiple output). The method comprises the following steps that each source end in multiple source ends respectively sends enhanced RTS to the target end; the target end receives a plurality of enhanced RTSs, and the source ends corresponding to the enhanced RTS are determined on the basis of the enhanced RTS, in addition, the optimum source end combination of the uplink data is simultaneously sent according to the preset criterion, and the enhanced CTS is sent to the optimum source end combination. The method provided by the invention has the advantages that the target end selects the optimum source end combination for simultaneously sending the uplink data according to the preset criterion, in addition, the enhanced CTS is sent to the optimum source end combination, and each source end in the optimum source end combination sends data to the target end. The method provided by the invention has the advantages that a plurality of source ends are allowed to simultaneously send the source end data to the target end, the advantage of many target end antennas is sufficiently utilized, and the frequency spectrum efficiency is improved.

The invention relates to a pulmonary nodule automatic detection method, a pulmonary nodule automatic detection device and a computer system. The method comprises the following steps: acquiring a CT image to be detected; performing filtering enhancement processing on the CT image to be detected to obtain a lung enhanced CT image sequence; segmenting the CT image sequence by adopting a threshold method to obtain an image only containing a pulmonary parenchyma region; cutting the image obtained by the pulmonary parenchyma segmentation module into a plurality of image blocks, and obtaining a region of interest through a multi-scale feature fusion UNet network model; and carrying out automatic detection and identification on the region of interest by adopting a 3D CNN model to obtain a pulmonary nodule detection result. Compared with the prior art, the method has the advantages of high detection sensitivity and precision and the like.

The invention provides a deep learning-based CBCT image cross-modal prediction CTA image stroke risk screening method and system. The method comprises the steps of 1, constructing a cyclic adversarialresistance generation network model; 2, training a cyclic adversarial resistance generation network model through the CBCT images and the contrast image data corresponding to the CBCT images; 3, inputting a test image into the trained cyclic antagonism generation network model to generate an angiography CT image; and 4, predicting the stroke risk according to the form, the carotid artery stenosisdegree and the curvature of the carotid artery in the angiography CT image. According to the method, based on the deep learning model, the non-enhanced CBCT image is converted into the enhanced CT angiography image, carotid artery blood vessel segmentation and extraction are carried out, the carotid artery stenosis degree and curvature are quantitatively calculated, then the stroke risk is predicted, and a convenient, economical and efficient new way is provided for clinically obtaining the CTA image and diagnosing.

A CT medical image data enhancement method for cholelithiasis based on depth learning is provided. The method includes constructing CT medical image data enhancement convolution neural network for cholelithiasis. The network consists of four convolution units. Firstly, CT medical image dataset of cholelithiasis is constructed as the input of neural network, and the image is enhanced by edge information and redundant information is removed. Then, the image is cut according to the segmentation threshold to form many image blocks, and the dataset is expanded by scaling, rotation and translation.Convolution neural network (CNN) is trained continuously with data set, and adaptive lifting neural network (ALN) is used to extract image features, stretch contrast, equalize histogram and reconstruct image. Convolution neural network model can be used to enhance CT medical images of cholelithiasis. The method can realize real-time enhancement of cholelithiasis CT medical image data, and achievegood visual and medical effects.

The invention discloses an image processing method and device and a classification model training method and device. The image processing method comprises the steps: acquiring a CT medical image, wherein the CT medical image is an enhanced CT medical image in an arterial phase, an enhanced CT medical image in a venous phase or a plain scanned CT medical image; according to the CT medical image, obtaining a classification result that the CT medical image is the enhanced CT medical image in the arterial phase, the enhanced CT medical image in the venous phase or the plain scanned CT medical image through a classification model, wherein the classification model is used for determining the category of the CT medical image. Plain scanned CT, enhanced CT in the venous phase and enhanced CT in the arterial phase can be quickly, accurately and automatically distinguished.

Embodiments describe an accurate and rapid method for assessing spinal bone density on chest or abdominal CT images using post-processed colored images. Post-processing of CT images for the purposes of displaying the spine is followed by color enhancement of routine unenhanced or contrast enhanced CT images to improve diagnostic accuracy, inter-observer agreement, reader confidence and/or time of interpretation as it relates to assessing bone density of the spine. CT images are post-processed (without changes to the standard-of-care CT imaging protocol and without additional cost or radiation for the patient) to straighten the spine for improved visualization of multiple segments. The color-enhanced images can be displayable simultaneously with the grayscale images. Methods and systems are provided for performing opportunistic bone density screening.

The invention relates to an enhanced CT image rectal cancer staging auxiliary diagnosis system based on deep learning. The system comprises an enhanced CT image input module, an enhanced CT image and annotation database, an image preprocessing module, a rectal lesion area discrimination module, a lesion lymph recognition module, a focus feature extraction module, a comprehensive diagnosis module and a visualization module. Aiming at the problems that a rectum enhanced CT image is complex in data structure and difficult to distinguish cancerous areas and stages thereof, the rectum enhanced CT image rectal cancer data labeling and data set construction, rectal cancer lesion area discrimination based on a self-attention deep learning model and metastatic lymph node identification based on sequence adaptive feature fusion are researched. A rectal cancer staging intelligent auxiliary diagnosis system is designed and realized, and clinical application experiment verification is carried out, so that comprehensive data acquisition accuracy and efficiency before a rectal cancer operation are improved.

The invention discloses an enhanced CT image tracheal wall enhancement method, a system, a device and a medium, and the method comprises the steps: obtaining an enhanced CT image sequence, and carrying out the thresholding of the enhanced CT image sequence; performing three-dimensional region growth on the image subjected to thresholding processing to obtain lung and trachea masks; performing closed operation on lung and tracheal masks; performing three-dimensional region growth on the image obtained by the closed operation, and segmenting a trachea main body; calculating boundary pixel characteristics of each pixel point of the trachea main body; judging whether each pixel point of each image in the enhanced CT image sequence belongs to a tracheal wall based on the boundary pixel characteristics; and enhancing the pixel values of the pixel points belonging to the tracheal wall.

The invention discloses an intelligent recommendation method for an enhanced CT contrast agent injection scheme. The method comprises the following steps: (1) establishing a case data storing multiple groups of case information into the database to form the case database; (3) establishing an expert knowledge establishing the expert knowledge base according to the contrast agent injection routine information; (3) inputting a case: inputting case information of a patient to be injected, generating a default value and forming a screening case library; (4) outputting injection scheme parameters: generating different injection scheme parameters according to the default value quantity and default information in the case information of the patient to be injected; and (5) forming an injection scheme: forming the injection scheme on the basis of the contrast agent injection scheme parameters output in the step (4). Compared with the prior art, injection of the contrast agent can be more scientific and standardized, medical accidents can be effectively reduced, the health of patients is effectively guaranteed, the quality of CT images is improved, and the method is worthy of application and popularization in the field.

The invention relates to the technical field of medical detection and diagnosis equipment, and discloses an enhanced CT scanning diagnosis device with convenient neck fixation. The enhanced CT scanning diagnosis device comprises a machine body, wherein one side of the machine body is movably connected with a supporting plate, one side of the top of the supporting plate is provided with a fixing pad, the middle part of the top part of the fixing pad is provided with a fixing groove, the inside of the fixing pad is provided with an air inflation airbag, and one side of the fixing pad is fixedlyconnected with a fixing block. According to the reinforced CT scanning and diagnosis device with convenient neck fixation, the air can enter the inside of the gas adding airbag through the one-way air inlet hole by pressing the pressing block, and then the air is inflated in the air inflation airbag through the inflation pipe, the air discharge valve can release the air inside the air inflation airbag, the size of the fixing groove can be conveniently adjusted by inflating and discharging the air inflation airbag, so that the fixing groove can be better jointed with the neck of the patient, which is convenient to fix the neck of different patients, thus avoiding the unconscious disturbance of the body in the diagnosis of enhanced CT scan and affecting their diagnostic results.

The invention discloses an EGFR gene mutation detection method and system based on a chest CT image and a computer readable storage medium. The method comprises the following steps: receiving a chest CT image to be processed; extracting feature variables of a plurality of target imageomics features from the chest CT image, wherein each target imageomics feature does not have a saliency difference between chest plain-scan CT and chest enhanced CT on a first saliency level and has a saliency difference between EGFR mutation positive and EGFR mutation negative on a second saliency level; according to a regression model and the feature variables, obtaining a score value corresponding to the chest CT image to be processed; according to the score value, determining an EGFR gene mutation detection result of a lung cancer patient to which the chest CT image to be processed belongs, wherein the detection result reflects the probability of EGFR gene mutation. According to the method, EGFR gene mutation can be detected based on a chest plain-scan CT image or an enhanced CT image, and the clinical application range is wide.

The invention provides a method for calculating an ASPECTS score of a non-enhanced CT, and the method comprises the following steps: obtaining cerebral ischemia non-enhanced CT image data, and extracting a target region; judging whether ischemia occurs or not, and marking; designing a cerebral ischemia classification model based on deep learning, and training the classification model by using the marked cerebral ischemia non-enhanced CT image data, the input data are an image to be detected, mask images of corresponding areas of the left and right brains and a classification label for judging whether ischemia exists or not; and calculating an ASPECTS score according to the obtained classification result. The method not only considers the local comparison of the left and right sides of the same region, but also considers the global situation of the region in the whole brain tissue.

The invention provides an image processing method and an image processing device. The image processing method comprises the steps: determining focus segmentation data, lung blood vessel segmentation data and lung arteriovenous blood vessel segmentation data corresponding to a lung plain-scan CT image based on the lung plain-scan CT image; and determining reinforcement judgment information corresponding to the lung plain-scan CT image based on the plain-scan CT image, the focus segmentation data, the lung blood vessel segmentation data and the lung arteriovenous blood vessel segmentation data; and judging whether initial abnormal lesions exist in the lung or not by judging the strengthening state of the focus area. As the contrast agent can be prevented from being injected without enhanced CT examination, wounds and potential risks caused by injection of the contrast agent are avoided. Meanwhile, radiation to the patient during CT examination is prevented from being enhanced, and the doctor seeing cost of the patient is reduced.