299 results about "Anatomical configuration" patented technology

A holographic augmented reality ultrasound needle guide system and method includes an augmented reality display such as a headset wearable by a user. The augmented reality display is configured to depict a virtual ultrasound image. The augmented reality display is further configured to allow a user to select a desired reference point on the virtual ultrasound image. The system is configured to depict a holographic needle guide based on the selection of the desired reference point. The system is also configured to adjust a trajectory of the holographic needle guide to avoid intersecting undesired anatomical structures. The augmented reality display is further configured to stamp the holographic needle guide into a selectively locked trajectory and position.

A medical device-placing system includes an ultrasound probe, a medical-device detector, a console, and an alternative-reality headset. The ultrasound probe is configured to emit ultrasound signals into a patient's limb and receive echoed ultrasound signals from the patient's limb. The medical-device detector is configured for placement about the patient's limb. The console is configured to transform the echoed ultrasound signals to produce ultrasound-image segments corresponding to anatomical structures of the patient's limb, as well as transform magnetic-sensor signals from the medical-device detector into location information for a magnetized medical device within the patient's limb. The alternative-reality headset includes a display screen through which a wearer of the alternative-reality headset can see the patient's limb. The display screen is configured to display over the patient's limb a virtual medical device per the location information for the medical device within objects of virtual anatomy corresponding to the ultrasound-image segments.

Technology is described for augmenting medical imaging for use in a medical procedure. The method can include the operation of receiving an image of patient anatomy captured by a visual image camera during the medical procedure. An acquired medical image associated with the patient anatomy can then be retrieved. Another operation can be associating the acquired medical image to the patient anatomy. An augmentation tag associated with a location in one layer of the acquired medical image can be retrieved. A further operation can be projecting the acquired medical image and the augmentation tag using an augmented reality headset to form a single graphical view as an overlay to the patient anatomy in either 2D, 3D or holographic form.

The invention discloses an augmented reality intelligent navigation and operation quantitative evaluation method and system in an operating room, and the method comprises the steps: collecting multispectral image data and depth point cloud data of an operation region in the operating room in real time, and generating a multidimensional perception data set; performing feature extraction and spatial registration on the multi-dimensional perception data set, and decomposing the dynamic three-dimensional augmented reality model into an anatomical structure feature layer and an instrument interaction feature layer; performing dynamic matching and real-time rendering on the anatomical structure feature layer and the instrument interaction feature layer based on a digital twinning technology to generate augmented reality navigation information and an operation guidance scheme; and performing multi-dimensional quantitative analysis on the surgical operation process according to the augmented reality navigation information and the operation guidance scheme, and outputting a quantitative evaluation result. By utilizing the embodiment of the invention, augmented reality navigation guidance which is synchronous with a real operation scene in real time can be provided, meanwhile, accurate quantitative evaluation is carried out on the operation operation, and the accuracy, the safety and the trainability of the operation are improved.

A modular endoscope system comprises an imaging guidewire comprising an elongate shaft extending from a proximal end to a distal end, an imaging device located proximate the distal end of the elongate shaft and a lighting element located proximate the distal end of the elongate shaft, and an intervention accessory configured to slide along the elongate shaft to provide a medical intervention. A method of providing a medical intervention to an internal anatomic location comprises inserting an imaging guidewire into an anatomic passageway, viewing target anatomy with imaging capabilities of the imaging guidewire, pushing an intervention accessory along the imaging guidewire to the target anatomy, and operating the intervention accessory to provide an intervention on the target anatomy.

The invention provides an analysis processing method and system for a multi-source medical image, is applied to the technical field of medical image processing, and provides a basis for subsequent processing by acquiring medical image data. All the images are resampled to a unified spatial resolution, the images of the same patient at different time points are registered and aligned to the same spatial coordinate system, and after spatial unification and registration processing, anatomical structures in the images are aligned at different time points. At the moment, feature vectors capable of describing textures and shapes are extracted from the registered images, the feature vectors are subjected to standardization processing and then fused, and finally a quantitative analysis result with cross-time-point comparability is obtained and used for disease analysis, curative effect evaluation and other procedures. Therefore, the method has the advantages of processing multi-source heterogeneous medical image data, processing long-term follow-up visit medical image data and generating a cross-time-point comparable quantitative analysis result.

The invention involves a system and method for increasing positional accuracy of surgical systems that utilize magnetic or electromagnetic sensors to provide positional awareness to a surgeon or robot performing the surgery. The system takes advantage of electromagnetic tracking through sensors. These sensors are very accurate and repeatable, while being compact enough to not inhibit surgical procedures. The accuracy and repeatability of the sensors is <1 mm within a predetermined 6 inch×6 inch performance motion box. The system is constructed and arranged to map the distortion patterns of the sensor and, in real time, correct the distortion pattern to provide accurate location of anatomical structures for performance of a surgery.

The present invention relates to medical navigation. In order to provide facilitated information for navigation in hollow anatomical structures, a device (10) for navigation in hollow anatomical structures is provided that comprises a data input (12), a data processor (14) and an output interface (16). The data input is configured: to provide 3D image data of a hollow structure in a region of interest of a subject, wherein the 3D image data comprises a coordinate space; to provide a current pose of a tool with a tool tip inserted in the hollow structure. The data processor is configured: to transfer the estimated current pose of the tool tip to the coordinate space of the 3D image data based on the registration of the tool tip with the coordinate space of the 3D image data; and to generate, from the 3D image data, a rendered image showing a scene inside the hollow structure relating to the transferred estimated current pose of the tool tip. The output interface is configured to provide the rendered image to a user.

The invention belongs to the technical field of medical image reconstruction, and particularly relates to a CT reconstruction system and method based on discrete Gaussian representation. According to the technical scheme, the method comprises the steps that a Gaussian function set is constructed, and a three-dimensional voxel space is directly represented through learnable Gaussian parameters; carrying out parallel aggregation on Gaussian function contribution values to a three-dimensional voxel grid by adopting a discretization volume reconstruction technology; establishing a geometric projection transformation model to reversely project the three-dimensional reconstruction volume to a two-dimensional projection domain, and minimizing the difference of the projection domain through joint optimization of Gaussian parameters; and introducing an adaptive density control mechanism to dynamically adjust Gaussian distribution. Compared with an existing method, the method has the advantages that an end-to-end discretization Gaussian function representation mode is adopted, global optimization of any CT geometric configuration is supported, high-quality rapid reconstruction within 5-15 minutes is achieved while the detail integrity of the anatomical structure is kept, and a high-precision and high-robustness three-dimensional reconstruction solution is provided for low-dose CT scanning and emergency treatment scenes.

Systems, devices, and methods for integrating images from various sources and using the same to guide endoscopic procedures are disclosed. An endoscopic system comprises an endoscope to be positioned and navigated in a patient anatomy, and a processor configured to reconstruct a three-dimensional (3D) image of an anatomical target based on at least two images of the anatomical target. The at least two images can be calibrated and registered using respective landmarks. One or more secondary images can be integrated with the reconstructed 3D image. The reconstructed image or the integrated image, along with the endoscope navigation plan, can be displayed to a user. Based on the reconstructed or the integrated image, the processor can generate an endoscope navigation plan for use in an image-guided endoscopic procedure.

An automated adaptive radiotherapy system based on machine learning for designing and adapting personalized dosing plans and a corresponding system, the system comprising: • a data acquisition module to collect multimodal patient data such as anatomical images, genomics, physiological signals and electronic health records; • a preprocessing department that performs the normalization, alignment, segmentation and transformation of the acquired data into data structures suitable for predictive modeling; • a dose prediction engine containing at least one machine learning model trained to predict personalized three-dimensional radiotherapy dose distributions and dose-volume histograms from the preprocessed data; • an adaptation module to receive updated clinical data and automatically adapt the dosing schedule to intra-fraction and inter-fraction changes in patient anatomy and response to treatment; • a clinician dashboard that displays predicted dosing schedules, allows clinician interaction, and can make the model interpretable through explainable AI; • a data security and compliance layer that protects data through encryption, access control, and regulatory compliance.

A system configured for registration of arthroscopic video images to preoperative models includes one or more computing devices configured to identify an anatomical structure represented in diagnostic video data, register one of a plurality of anatomical structures in a three-dimensional (3D) anatomical model to the anatomical structure represented in the diagnostic video data, the 3D anatomical model being generated from preoperative image data, track the anatomical structure intraoperatively based on the registered one of the plurality of anatomical structures, generate a simulated projected view of the registered one of the plurality of anatomical structures from the 3D anatomical model based on a determined orientation of the arthroscope during capture of intraoperative video data, and output the simulated projected view scaled and oriented based on one or more landmark features of the anatomical structure extracted from the intraoperative video data.

The invention discloses a medical image generation method based on cyclic diffusion and target guide sampling. According to the method, existing source modal medical images are converted into other target modal medical images through cyclic diffusion. An additional attention mechanism is introduced into a noise prediction network of a diffusion model of the cyclic diffusion model, and anatomical structure information in the source modal medical image is fused with a current noise image; meanwhile, the anatomical structure segmentation network introduces anatomical structure shape loss. When the diffusion model is trained, a perceptual priority weighted P2W training strategy is adopted: by adjusting the weight of a loss function, the diffusion model preferentially learns the global structure of the image at a high noise level and optimizes the details of the image at a low noise level; meanwhile, a target-guided classifier for a sampling stage is trained. In a sampling stage, first, reverse sampling is performed from a source modal image to obtain a potential code; and under the guidance of the classifier, the sampling process is guided by utilizing the probability gradient information of the target modal, and the generation of a target modal image is completed.

The invention discloses a myopia image deep learning recognition model training method, particularly relates to the technical field of medical image processing and deep learning, and is used for solving the problem that an existing deep learning model lacks anatomical structure priori knowledge guidance in myopia eye bottom image analysis. The method comprises the following steps: acquiring a myopia eye bottom image and anatomical structure priori knowledge data, extracting a multi-scale feature map by using a deep learning model, analyzing the geometric morphology of a key anatomical component based on standard spatial relationship information, and generating a spatial constraint loss item; according to the method, key anatomical path topology coherence is evaluated based on topology connection information, topology constraint loss items are generated, a loss item fusion strategy is dynamically adjusted according to a training stage, finally, a model is iteratively trained to convergence through a gradient back propagation algorithm, and organic combination of medical priori knowledge and a deep learning model is realized. And the clinical rationality and reliability of model output are improved.

Systems and methods for determining insurance reimbursement are disclosed. A system may include at least one processor configured to access video frames captured during a surgical procedure on a patient and analyze the video frames to identify a medical instrument, an anatomical structure, and an interaction between the medical instrument and the anatomical structure. The processor may access a database of reimbursement codes correlated to medical instruments, anatomical structures, and interactions between medical instruments and anatomical structures and compare the identified interaction between the medical instrument and the anatomical structure with information in the database of reimbursement codes to determine a reimbursement code associated with the surgical procedure and output the reimbursement code for use in obtaining an insurance reimbursement for the surgical procedure.

The arrangements disclosed herein relate to receiving a data stream of a medical procedure performed with a robotic medical system; identifying, using the data stream and with one or more models trained with machine learning, a type of an anatomical structure on which the medical procedure is performed, and a type of an interaction with the anatomical structure; and determining an amount of force to be applied to the anatomical structure based at least in part on the type of the anatomical structure and the type of the interaction with the anatomical structure. Arrangements also relate to providing an indication of the amount of force to control performance of the medical procedure with the robotic medical system.

This disclosure relates to surgical systems, devices and methods for planning and implementing surgical procedures. The systems and methods disclosed herein may be utilized to establish physical anatomical models of anatomy. The physical anatomical model may include one or more features for viewing an interior of the model.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical landmark detection algorithm leveraging Deep Reinforcement Learning (DRL) techniques to automatically locate one or more anatomical landmarks associated with the patient specific anatomical feature within the medical images. A 3D surface mesh model may be generated representing the patient specific anatomical features including the located one or more anatomical landmarks. The located one or more anatomical landmarks may be used to guide placement of a 3D model of a medical device that may be fused with the 3D surface mesh model to generate a patient specific 3D model of the medical device.

This disclosure provides methods, devices, and systems for planning and performing medical procedures. The present implementations more specifically relate to techniques for determining the pose of a medical instrument within an anatomy. In some aspects, a controller for a medical system may estimate a pose of the medical instrument based on image data representing a three-dimensional (3D) model of the anatomy and generate a graphical user interface (GUI) that enables a user to fine-tune or correct the estimated pose via one or more user inputs. For example, the GUI may overlay or superimpose an interactive model of the instrument on a cross-section of the 3D model of the anatomy, where the interactive model has a pose associated with the estimated pose of the instrument. A user may adjust the interactive model, via the user inputs, to more accurately reflect the pose of the instrument.

Systems and methods for clinical decision support are provided. One or more input medical images of an anatomical object of a patient are received. Characteristics of the anatomical object are extracted from the one or more input medical images. A risk associated with a medical procedure to be performed on the anatomical object is determined based on the one or more input medical images and the extracted characteristics of the anatomical object using one or more machine learning based risk assessment models. A simulation of the medical procedure is performed on the anatomical object based on the one or more input medical images, the extracted characteristics of the anatomical object, and the risk associated with the medical procedure. One or more clinical decisions associated with the medical procedure are automatically made based on the risk associated with the medical procedure and results of the simulation. The one or more clinical decisions are output.

Systems and methods for intraoperative tracking and visualization are disclosed. A current minimally invasive surgical (MIS) instrument pose may be determined based on a live intraoperative input video stream comprising a current image frame captured by a MIS camera. In addition, an instrument activation state and at least one parameter value associated with the instrument may also be determined. Intraoperative graphic visualization enhancements may be determined based on the activation state of the instrument, and / or a comparison of parameter values with corresponding parametric thresholds. The visualization enhancements may be applied to a current graphics frame. The current graphics frame may also include visualization enhancements related to proximate anatomical structures with proximity determined from the instrument pose and an anatomical model. The current graphics frame may be blended with the current input image frame to obtain an output blended image frame, which may form part of an output video stream.

In an example, a system includes a processor that may receive instrument data from a surgical instrument related to operation of the surgical instrument during a surgical procedure; (ii) receiving, from the surgical navigation system, position data indicative of a position of the surgical instrument relative to the patient's anatomy during the surgical procedure; (iii) determining kinematic data based on the position data; (iv) determining a plurality of surgical performance indicators indicative of characteristics of the surgical performance using the instrument data and the kinematic data; (v) analyzing (a) kinematic data and instrument data with respect to (b) a plurality of surgical performance metrics; and (vi) causing the user interface to output information based on the analysis to provide feedback to a surgeon and / or stakeholder (e.g., a hospital administrator) relating to the performance of the surgical procedure.

Three dimensional virtual scene reconstruction of a medical procedure environment is provided. The system can include one or more processors, coupled with memory, to identify a frame that captures a scene in a medical procedure performed with a robotic medical system. The one or more processors can generate, using one or more models trained with machine learning, one or more masks that segment an anatomical structure in the frame, label the anatomical structure in the frame, and indicate a depth value for each pixel in the frame. The one or more processors can construct 3D mesh and texture data based on the one or more masks. The one or more processors can provide, in an interactive virtual environment, a virtual scene rendered using the 3D mesh and texture data generated from the frame that captures the scene in the medical procedure.

A surgical system is provided. The surgical system includes a robotic arm, a navigation system, and one or more controllers. The robotic arm includes a plurality of links and joints and is configured to support an end effector. The navigation system is configured to track a pose of an anatomy of a patient. The one or more controllers are configured to associate a target trajectory with the anatomy of the patient, define a trajectory selection zone associated with the target trajectory, operate the robotic arm in a free mode, whereby the robotic arm is freely moveable, responsive to the end effector being within the trajectory selection zone in the free mode, automatically select the target trajectory associated with the trajectory selection zone, and operate the robotic arm in an automatic mode, whereby the robotic arm is automatically moved to align the end effector with the target trajectory.

The invention discloses a clinical operation data analysis method based on virtual reality, and belongs to the field of virtual reality technology and medical skill training. Comprising the following steps: constructing a high-dimensional interaction data tensor covering space-time sequence features; establishing a three-dimensional space topological relation graph based on medical scene understanding, mapping various clinical operation skills into a three-dimensional model interaction sequence of a virtual medical device and an anatomical structure, and generating a multi-dimensional operation behavior feature vector matrix; virtual instrument operation sequence fragments with abnormal interaction efficiency or operation track deviation are identified, and dynamic loading priority weights of virtual medical scene resources are determined according to the virtual instrument operation sequence fragments; when the rendering frame rate of the virtual scene is lower than a specified threshold value, generating early warning information; and in a double-layer adaptive medical scene rendering optimization process, identifying performance bottleneck nodes and geometric processing conflicts in a three-dimensional rendering pipeline, and carrying out dynamic shader scheduling and model detail automatic switching according to a priority rule.

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 disclosure relates generally to a surgical system. The surgical system comprises one or more surgical instrument assemblies and a surgical navigation system. The surgical instrument assemblies comprises a tracking device capable of being tracked by the surgical navigation system. The surgical system is also configured to allow the user to define one or more alert zones relative to anatomical structures of the patient and / or a surgical pathway. The surgical system further comprises an alert device in communication with the surgical navigation system, such that the alert device is configured to provide a user-perceptible alert to the surgeon or medical professional based on the position of the surgical instrument, as determined by the surgical navigation system, relative to the defined alert zones and / or surgical pathway.

A method performed by a system having at least a processor and a memory therein to execute instructions for a self-supervised learning framework to learn anatomically consistent embeddings of anatomical structures in medical images of a plurality of patients across varying scales of anatomical structures in the plurality of patients receives a medical image as an input, and obtains a first cropped image and a second overlapping cropped image of the received medical image, each having respective representative global embeddings and corresponding overlapped patch embeddings and non-overlapped patch embeddings. The system calculates a global consistency loss using the respective representative global embeddings and calculates a local consistency loss using the corresponding overlapped patch embeddings and non-overlapped patch embeddings.

Embodiments for rendering an operative guidance overlay in association with a patient are disclosed. A method includes receiving image data indicating an anatomical structure of the patient and a structure of operative instruments; generating virtual models based on the image data; receiving sensor data associated with a user, the operative instruments, or the patient; dynamically generating guidance overlay data associated with the anatomical structure of the patient, or the operative instruments based on the virtual models and the sensor data; and rendering the operative guidance overlay in association with the patient based on the guidance overlay data using a wearable augmented reality interface device. The virtual models include a virtual anatomical model of the anatomical structure of the patient, or virtual instrument models associated with the operative instruments. The rendering comprises continuous alignment of the virtual anatomical model with the patient and the virtual instrument models with the operative instruments.

A method for tracking patient features in a surgical field includes capturing an image stream in a field of view demonstrating patient features of a patient anatomy. A first feature set is identified in a tracking region of the field of view of the image stream. The relative motion among a first plurality of the image features in the first feature set is monitored in the image stream over time. Moving features from the first features set are filtered to generate a second feature set. The second feature set defines tracking features. The location and orientation of the patient anatomy is then tracked based on the feature locations of the plurality of tracking features.