23 results about "Bone imaging" patented technology

The application relates to the field of image processing and discloses a bone metastasis detection and positioning system and method based on SPECT bone imaging. The system extracts and processes multi-scale features of a to-be-detected image through a feature pyramid network to obtain a multi-scale feature map; then extracts metabolic features, morphological features and uptake mode features from the multi-scale feature map through a bone-specific feature model, and gradually fuses the metabolic features, the morphological features and the uptake mode features to obtain a bone-specific enhanced feature map; then extracts and fuses features of the bone-specific enhanced feature map through a mixed feature fusion model to obtain mixed fusion features; finally, the mixed fusion features are subjected to probability reasoning and double-uncertainty quantization processing through a probability reasoning network to determine a bone metastasis detection result, high-precision detection and positioning of bone metastasis lesions are realized, and the problems that existing technologies cannot effectively identify different size lesions and lack global perception ability are effectively solved.

This invention discloses a method and system for bone imaging based on dual-view depth-enhanced CT. Utilizing deep learning and multi-strategy fusion techniques, it achieves high-quality image restoration and bone microstructure analysis. The system comprises the following modules: a Cascade-SwinUNETR backbone module for feature extraction and enhancement, enabling efficient multi-scale feature aggregation; a dual-view feature fusion module that extracts and integrates complementary information by combining CT image data from different perspectives, improving image detail and structural accuracy; and an unsupervised domain adaptation (UDA) module to adapt to different data distributions without additional annotation, enabling cross-domain learning and model generalization, ensuring stable performance in various medical scenarios. This invention improves the clarity and accuracy of CT imaging, significantly enhancing the quality and efficiency of physicians' decisions in bone health assessments while reducing reliance on invasive sampling.

The application discloses a kind of whole body SPECT lung cancer bone imaging data processing method and system, it is related to image processing technical field, comprising: the whole body SPECT lung cancer bone imaging data is input into the improved UNet network after training, local texture feature is extracted by profile wave decomposition module, local texture feature is fused with original image, and the image feature is extracted by inputting fusion feature into UNet encoder;Using the UNet decoder of adding profile wave feature splicing and reconstruction strategy to the extracted image feature, obtains lung cancer bone metastasis lesion area.The application can improve the feature extraction capability of bone imaging lesion segmentation method under low resolution and fuzzy boundary condition.

The application relates to the technical field of bone imaging, and provides an ultrasonic cancellous bone imaging method based on full-wave inversion, which comprises the following steps: S1: setting an imaged bone phantom model, emitting an ultrasonic pulse signal, recording the ultrasonic pulse signal passing through the model, and sampling the signal as a simulation experiment wave field signal; S2: establishing a simulation wave field, setting an initial sound velocity model as a current sound velocity model; S3: based on the current sound velocity model, carrying out forward modeling on the simulation wave field, carrying out incremental extrapolation of the forward wave field signal along the propagation direction of the ultrasonic pulse signal, recording the simulation wave field signal after completing one round of emission-reception; S4: constructing a loss function, solving the loss function by using a conjugate gradient method, and updating the current sound velocity model; and S5: judging whether the loss function converges or not, and outputting the current sound velocity model if the loss function converges. High-resolution ultrasonic imaging of bone and microstructure is realized, and the demand of bone medical treatment and diagnosis with high precision requirements is met.

The invention relates to the technical field of multi-modal image fusion, in particular to a cross-modal data fusion method for bone development image segmentation, and the method comprises the steps: segmenting a bone development image of each modal into a plurality of image blocks according to the same image segmentation mode; obtaining the optimal fusion mode of each region according to the detail presentation richness of the image blocks of each mode of the same region; fusing the detail presentation richness and the edge fuzzy degree of the optimal fusion mode of other areas adjacent to each area to obtain the edge detail interference degree of each area, obtaining the edge detail loss probability of each area by combining the skeleton joint existence probability of each area, and obtaining the edge detail interference degree of each area by combining the detail presentation richness of the optimal fusion mode of each area. And obtaining the data fusion window size of each region. According to the method, the condition that details are fuzzy or lost during multi-modal data fusion can be avoided, so that the accuracy and the reliability of a final fusion result are improved.

This application discloses a method, display method, apparatus, device, and storage medium for determining jawbone features. The method includes: obtaining a radicular bone scan image; segmenting the jawbone based on the radicular bone scan image to determine a segmented jawbone image; using a neural network, taking both the radicular bone scan image and the segmented jawbone image representing the same jawbone location as input, to determine a jawbone feature image and a cancellous bone feature image; comparing the jawbone feature image and the cancellous bone feature image, and determining a cortical bone feature image based on the differences; and determining and outputting cortical bone feature information based on the cortical bone feature image. The method provided in this application can improve the accuracy and efficiency of jawbone feature determination.

The invention relates to the field of image processing, and discloses a bone metastasis detecting and positioning system and method based on SPECT bone imaging. The system performs multi-scale feature extraction and feature processing on a to-be-detected image through a feature pyramid network to obtain a multi-scale feature map; then, metabolic features, morphological features and uptake mode features are extracted from the multi-scale feature map through a bone specificity feature model, and progressive fusion is carried out on the metabolic features, the morphological features and the uptake mode features to obtain a bone specificity enhanced feature map; performing feature extraction and feature fusion on the bone specificity enhanced feature map through a mixed feature fusion model to obtain mixed fusion features; and finally, probabilistic reasoning and dual uncertainty quantification processing are carried out on the mixed fusion features through a probabilistic reasoning network, a bone metastasis detection result is determined, high-precision detection and positioning of bone metastasis lesions are achieved, and the problems that in the prior art, lesions of different sizes cannot be effectively recognized, and global perception ability is lacked are effectively solved.

The invention discloses a rapid bone scanning image noise reduction method based on a state space unsupervised diffusion network, and the method comprises the steps: collecting a noise-containing rapid bone scanning original image, carrying out the preprocessing, and constructing a non-diffusion module with a consistent period architecture; step-by-step noise adding is carried out on the conventional bone scanning image, a pure Gaussian noise image is generated, and the step-by-step noise adding process is based on a forward diffusion process of a Markov chain; fusing the pure Gaussian noise image and the rapid bone scanning image, and gradually denoising the fused image through a state space model; the denoising process is based on back diffusion of a Markov chain, and each back diffusion step is mapped through a state space model to output a high-quality conventional bone scanning image. According to the method, the rapid bone scanning image containing noise is converted into the high-quality image by utilizing the diffusion network based on the state space, the problem that the scanning time of a traditional whole-body bone scanning technology is long is solved, the application range is wide, and rapid, accurate, lossless and high-quality bone scanning image noise reduction can be achieved.

This invention provides a method, system, electronic device, and storage medium for displaying images of bone grinding states, relating to the field of image processing technology. It includes: establishing a three-dimensional model of a target bone based on a user's bone scan image, and obtaining the three-dimensional model data of the target bone; determining the position information of a first target registration point in the camera coordinate system of a surgical robot; determining an initial registration matrix based on the position information and the three-dimensional model data; determining a secondary registration matrix based on the initial registration matrix and a second target registration point, to obtain a target pose matrix based on the secondary registration matrix; transforming the bone prosthesis model into the coordinate system of the three-dimensional model of the target bone based on the pose matrix of the bone prosthesis model; determining the grinding state of the target grinding point in the coordinate system of the three-dimensional model based on the distance from the target grinding point on the three-dimensional model to the bone prosthesis model; and displaying the target grinding point on the three-dimensional model with a preset color corresponding to the grinding state.

The invention discloses a fixing device for bone imaging in nuclear medicine, and particularly relates to the field of fixing devices. Comprising a bottom plate, a first sliding groove is formed in the bottom plate, and one side of the first sliding groove penetrates through the upper surface of the bottom plate; the placing plate and the bottom plate are arranged at an interval, and two through holes are formed in the placing plate; the supporting spring is connected between the placing plate and the bottom plate; the two pedal plates are symmetrically and rotationally connected to the placing plate, the tiptoe side of each pedal plate is rotationally connected with the placing plate, the heel side of each pedal plate is located at the through hole of the placing plate, and a second sliding groove is formed in the bottom of each pedal plate; the two telescopic rods are connected between the first sliding grooves and the corresponding second sliding grooves in a sliding mode; and the driving assembly is used for pushing the two telescopic rods to move in opposite directions. By the adoption of the technical scheme, the problem that an existing fixing device cannot achieve simple and standardized adjustment of the body position in the examination process is solved, and the diagnosis efficiency is improved.

The invention relates to the technical field of radiopharmaceutical chemistry and clinical nuclear medicine, in particular to a D-proline modified alendronic acid derivative and application thereof. A radioactive preparation obtained by labeling the D-proline modified alendronate derivative with radionuclide can be prepared through a medicine box, is high in radiochemical purity, easy in chemical structure determination, good in stability, relatively high in uptake and retention at bones of normal mice, low in non-target organ or tissue uptake and good in target / non-target ratio, and can be applied to preparation of a medicine box. The bone imaging effect is good, the requirement of further clinical bone imaging can be met, and the bone imaging agent can be popularized and applied as a novel bone imaging agent.

This application discloses a tooth segmentation method, display method, apparatus, device, and storage medium. The tooth segmentation method includes: obtaining a root bone scan image; inputting the root bone scan image into a localization model to obtain first localization information for N teeth, the first localization information being used to determine the position information of the teeth in the root bone scan image; identifying the center information of different teeth in the root bone scan image to obtain second localization information for M teeth; N and M are positive integers greater than 1; determining the region of interest for tooth segmentation based on the first localization information of the N teeth and the second localization information of the M teeth; and inputting the image corresponding to the segmented region of interest into the segmentation model to obtain the tooth segmentation result. The tooth segmentation method provided in this application balances comprehensiveness and accuracy in segmentation.

The application provides a method for predicting the new crack initiation position and propagation direction of CFRP material, relates to the field of CFRP crack prediction, and comprises the following steps: collecting an original image of CFRP material, binarizing the original image, and performing bone imaging on the binarized image; strain rate intensity factors, main branch unit directions and branch lengths are used to form a strain rate intensity factor matrix, and a critical matrix and fracture toughness are established as a standard for judging crack initiation risk; the crack initiation risk is defined, and the crack initiation position is predicted according to the crack initiation risk; feature extraction is performed on the bone imaging image and the original image, and a tree heterogeneous tensor is constructed; the tree heterogeneous tensor is input into a decision tree, and the crack initiation angle predicted by the decision tree is output. The technical scheme of the application overcomes the problem in the prior art that the prediction of the new crack initiation position and propagation direction of CFRP material cannot take into account the accuracy and efficiency.

This invention relates to a bone imaging device, comprising: an infrared emitter (3) configured to emit infrared rays for detecting bones; and an infrared receiver, including a first receiver (7) for receiving the infrared rays emitted by the infrared emitter (3) and transmitted through the bones. The bone imaging device uses infrared rays to detect bones, which, compared to using X-rays to detect bones, improves upon the problem of significant radiation damage to the human body caused by radiation from existing bone imaging devices.

To provide a system and a method for interactive automated analysis of a bone scan image to detect metastasis.SOLUTION: A system and a method that enable improved computer-aided display and analysis of a nuclear medicine image are presented herein. Specifically, in a certain embodiment, the system and the method described herein provide an improvement to several image processing steps used in automated analysis of a bone scan image to assess a cancer status of a patient. For example, an improved method is provided for image segmentation, hotspot detection, automated classification of a hotspot as indicative of metastases, and calculation of risk indicators such as a bone scan index (BSI) value.SELECTED DRAWING: None

The invention discloses a fixing device for nuclear medicine bone imaging, and relates to the field of medical auxiliary devices.The fixing device comprises a scanning bed used for supporting a patient during nuclear medicine bone imaging and further comprises a main body mechanism used for assisting the scanning bed in supporting the patient; the adjusting mechanism is used for adjusting and supporting the legs of the patient, the adjusting mechanism is used for supporting and adjusting the feet of the patient, the limiting mechanism is used for limiting and fixing the feet of the patient, and through sliding of the adjusting frame and angle adjustment of the foot limiting frame, the patient can be helped to rapidly achieve and maintain the standard body position needed by nuclear medicine bone imaging; the body movement is effectively limited, image artifacts or blurs caused by improper postures are reduced, so that the imaging quality and the diagnosis accuracy are improved, and meanwhile, the device is flexible in structure and can meet personalized supporting and fixing requirements of patients with different body types.

Embodiments of the present application provide a classification model training method, an image classification method, a computing device, a computer readable storage medium and a computer program product. The classification model training method comprises: obtaining a pre-training image dataset; pre-training a shallow network and a deep network of a to-be-trained classification model using the pre-training image dataset to obtain a first classification model after pre-training; obtaining a SPECT bone imaging dataset and generating an expanded dataset based on the SPECT bone imaging dataset using an image generation technology; inputting the SPECT bone imaging dataset into the first classification model to train the deep network of the first classification model using the SPECT bone imaging dataset to obtain a second classification model; combining the SPECT bone imaging dataset and the expanded dataset into a synthetic dataset, and adjusting network parameters of the second classification model using the synthetic dataset to obtain a target classification model after training. The technical solution provided by the embodiments of the present application improves the classification accuracy.

Presented herein are systems and methods that provide improved computer-aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to a number of image processing steps for automating analysis of bone scan images to assess a patient's cancer status. For example, improved methods for image segmentation, hot spot detection, automated classification of hot spots as indicative of metastasis, and calculation of risk indices such as bone scan index (BSI) values are provided.

The present application discloses a root-bone feature determination method, a display method, an apparatus, a device, and a storage medium. The method comprises: obtaining a root-bone scan image; on the basis of the root-bone scan image, performing jawbone segmentation to determine a jawbone segmentation image; determining cortical bone feature information on the basis of the jawbone segmentation image; inputting the root-bone scan image into a localization model to obtain first localization information of N teeth, the first localization information being used for determining position information of the teeth in the root-bone scan image; identifying center information of different teeth in the root-bone scan image to obtain second localization information of M teeth, wherein N and M are positive integers greater than 1; obtaining a tooth segmentation result on the basis of the first localization information of the N teeth and the second localization information of the M teeth; and determining root-bone feature information on the basis of the cortical bone feature information and the tooth segmentation result. The method provided in the present application can improve the accuracy of root-bone feature determination.

The invention belongs to the field of medical image processing, and discloses an SPECT bone imaging bladder interference elimination method and system, and the method comprises the steps: obtaining an SPECT bone imaging image, segmenting a pelvic skeleton region in the SPECT bone imaging image through a pre-trained nnUNet network model, and generating a pelvic skeleton mask; judging whether bladder interference exists or not according to the radioactivity counting information in the external rectangular area of the pelvic skeleton mask; if bladder interference exists, generating a bladder mask in an external rectangular area of the pelvic skeleton mask according to the radioactivity counting information in combination with a foreground segmentation method and an edge optimization method; eliminating or replacing radioactivity counts in the bladder mask area to generate an SPECT bone imaging image after bladder interference is eliminated; outputting correction result information containing the SPECT bone imaging image after the bladder interference is eliminated; therefore, automatic and precise identification and elimination of bladder interference are realized.

This application relates to systems and methods for automated and interactive analysis of bone scan images to detect metastases. Presented herein are systems and methods that provide improved computer-aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to a number of image processing steps for automated analysis of bone scan images to assess the cancer status of a patient. For example, improved methods are provided for image segmentation, hot spot detection, automated classification of hot spots as indicative of metastases, and calculation of risk indices such as bone scan index (BSI) values.

The invention discloses a skeleton imaging method and device based on a three-dimensional-two-dimensional coupling generative adversarial network, and the method comprises the steps: carrying out the preprocessing of an initial load-bearing position lower limb data set, and obtaining a target load-bearing position lower limb data set; training the initial generative adversarial network according to the target load position lower limb data set to obtain a target generative adversarial network; a generator of the generative adversarial network comprises an orthogonal projection coupling layer, the orthogonal projection coupling layer is used for realizing conversion from a three-dimensional feature tensor to a two-dimensional feature map, a discriminator of the generative adversarial network comprises a bone anatomy attention module, and the bone anatomy attention module is used for enhancing the feature weight of a bone key region; and inputting the to-be-converted lower limb weight-bearing position tomography CT image into the target generative adversarial network to generate a two-dimensional long-leg skeleton spliced image. The method can accurately simulate a load-bearing position projection physical process and dynamically correct skeleton non-rigid deformation, generates an accurate two-dimensional long-leg skeleton splicing image, and can be widely applied to the technical field of computers.

The application discloses a kind of automatic segmentation methods of bone imaging based on improved U-Net and adaptive hybrid loss, for bone imaging image signal-to-noise ratio low, small lesion is difficult to extract and artificial delineation lesion time-consuming and so on.A multi-scale dense connection convolution block is used as a basic convolution unit, and an automatic segmentation network of bone imaging based on improved U-Net is proposed.In the encoding part of U-Net, a multi-scale dense connection convolution block and a maximum pooling layer are used to extract features from bone imaging lesions.In the decoding part, transposed convolution is used for upsampling.In the network training, adaptive hybrid loss is used to optimize the segmentation result.The automatic segmentation method of bone imaging lesions disclosed in the application is better than the original segmentation method, effectively improving the segmentation accuracy of bone imaging lesions.