388 results about "Anatomical configuration" patented technology

A medical system includes an elongate instrument, a tracking system, and one or more processors. The tracking system is disposed along at least a portion of the elongate instrument. The one or more processors are coupled to the tracking system and are configured to: generate a plurality of linked bounded-surface elements corresponding to a set of connected passageways of a patient anatomy; receive shape information from the tracking system, the shape information indicating a shape of at least a portion of the elongate instrument when the portion is disposed in the set of connected passageways; register the shape information to the plurality of linked bounded-surface elements; and based on the registering, display a composite image including an instrument image of the elongate instrument displayed within a model of the set of connected passageways.

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.

The invention provides a feature fusion method and system based on multi-modal medical data, and belongs to the technical field of medical data processing. According to the method, the structural relationship, such as a patient network, a biological pathway and an anatomical structure, in the medical data is modeled by using # imgabs0 #, the extracted structured features are input into # imgabs1 # to capture global context information, and a self-adaptive mechanism can dynamically adjust the feature weights output by # imgabs2 # and # imgabs3 # according to the data modality, so that more accurate feature fusion is realized. Experimental results prove that the model provided by the invention is superior to other baseline models on a plurality of multi-modal medical data sets.

A medical device-placing system including a medical-device tip-location sensor (“TLS”) configured for placement on a chest of a patient, an ultrasound probe, a console, and an alternative-reality headset. The ultrasound probe can be configured to emit ultrasound signals into the patient and receive echoed ultrasound signals from the patient. The console can be configured to transform the echoed ultrasound signals to produce ultrasound-image segments corresponding to anatomical structures of the patient, as well as transform TLS signals from the TLS into location information for a medical device within the patient. The alternative-reality headset can include a display screen through which a wearer of the alternative-reality headset can see the patient. The display screen can be configured to display over the patient a virtual medical per the location information for the medical device within objects of virtual anatomy corresponding to the ultrasound-image segments.

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.

Systems and methods for surgical robotic navigation include multiple small surgical markers which avoid interfere with line of sight and do not otherwise disturb the surgical staff, while providing a convenient and highly accurate methodology for tracking and compiling the markers. Multi-arm robotic surgery systems are described in various embodiments that hold surgical tools and navigation cameras and optimally make use of several small surgical markers placed on patient anatomy of interest, surgical tools and the robotic arms. The small surgical markers are mathematically compiled so that the navigation cameras see a larger “compiled” surgical marker, thus providing greater accuracy.

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.

A mobile bilateral robotic surgery system is provided. The system may be optimized for robotic spinal surgery. The system comprises a mobile cart configured to be deployable under a surgical table, and particular a spinal surgery table. The mobile cart does not need to be affixed to the floor or the surgical table. The cart comprises multiple robotic arms that can be folded beside or into the cart so as to allow for deployment under the surgical table. The robotic arms may be relatively short and light compared to known systems and are deployed to either side of the patient. This allows for the highly accurate performance of a wide range of surgical procedures. The mobile cart further comprises a central control unit that facilitates the system knowing the position of the robotic arms. The system further optionally comprises a robotic navigation capability that augments the abilities of the system in locating features of a patient's anatomy and tools being used in a surgical procedure.

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.

A delivery device includes a conveyor, one or more imaging devices configured to have an eye of a patient in a field of view, and an needle assembly. A staging assembly is mounted to the conveyor and includes one or more actuators configured to position the needle assembly relative to the eye of the patient. A controller is configured to receive one or more images from the one or more imaging devices; detect a location of anatomy of the eye of the patient in the one or more images; and activate the one or more actuators to drive a needle mounted to the needle assembly into a placement location on the eye of the patient according to the location of the anatomy. The controller activates the needle assembly to inject fluid into or draw tissue out of the eye.

The invention provides a clinical ultrasonic image assisted positioning method, which belongs to the technical field of clinical ultrasonic image processing, and comprises the following steps: acquiring an ultrasonic image sequence of a target part of a patient and carrying out image preprocessing; extracting anatomical structure feature points in the ultrasonic image sequence; identifying and classifying the anatomical structure feature points; calculating contribution values of the anatomical structure feature points to target part positioning; constructing a three-dimensional positioning coordinate system; calculating spatial position parameters of the target part in the three-dimensional positioning coordinate system; generating a target part positioning track; acquiring real-time spatial position information of the medical instrument; matching the real-time spatial position information of the medical instrument with the positioning track of the target part; establishing a relative position relationship between the medical instrument and the screened anatomical structure feature points; calculating a position deviation value of the medical instrument; and generating a medical instrument position adjustment instruction for adjustment. The technical problem of low accuracy of medical instrument positioning under ultrasonic guidance in the prior art is solved.

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.

Systems and methods for designing patient-specific medical devices are described herein. In some embodiments, a method includes obtaining patient data that includes image data and kinematic data of a patient's spine. A virtual model of the patient's spine is generated and can be manipulated until a target anatomical configuration is achieved. A patient-specific implant can then be designed based at least in part on the target anatomical configuration and the kinematics such that, when the patient-specific implant is implanted in the patient, the patient-specific implant provides the target anatomical correction while maintaining or improving the kinematics of the patient's spine.

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.

A system includes a display and a processor. The processor is configured to obtain a point cloud comprising a plurality of points representing different respective locations in a body of a subject, the points being marked as respectively corresponding to respective anatomical structures to which the locations belong. The anatomical structure includes a first structure and a plurality of second structures. The processor is further configured to compute a grid comprising a plurality of triangles representing the anatomical structure by applying a ball pivot algorithm to the point cloud, wherein a constraint condition is that none of the triangles comprises two of the points marked as corresponding to different respective ones of the second structures. Other examples are also described.

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.