37 results about "Intraoperative imaging" patented technology

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The application discloses a Mueller matrix polarization imaging device based on polarization state synchronous monitoring, and aims to solve the problem of slow imaging speed in traditional Mueller matrix polarization imaging technology.The device comprises a light source module, a polarization state generation module, a sample module, a polarization state analysis module, a photoelectric detection module, a synchronous monitoring module and an upper computer module.In the application, the polarization modulation elements of the polarization state generation module and the polarization state analysis module are set to continuous modulation, and the photoelectric detection module is set to continuous acquisition; the polarization state of the polarization modulation elements and the acquisition state of the photoelectric detection module are monitored with high precision through the synchronous monitoring module; and the complete Mueller matrix image of the measured sample is calculated in the upper computer module.The application realizes high-speed dynamic polarization imaging by utilizing continuous polarization state modulation, has high spatial resolution and high measurement precision, and is suitable for real-time polarization observation in the fields of biomedicine, material and industrial detection.

A system receives a set of two-dimensional projection images associated with respective projection angles from an imaging device. The projection images include projections of an anatomical structure, an anatomical target, and possibly fiducial markers, with a flexible elongate device disposed therein. The system can reconstruct a three-dimensional image from the set of two-dimensional projection images. In some examples, prior to reconstruction, the system can detect and repair pixels (within the projection images) associated with the fiducial markers, the flexible elongate device, and / or other objects. The system can enhance the reconstruction using a style transfer model. The system or another system can train the style transfer model by generating two-dimensional projections for a training set based on three-dimensional ground truth images.

A system and computer-implemented method for intraoperative assessment of nerve health. A processor determines a nerve function parameter from an electrically evoked muscle response to stimulation proximate to a nerve. The processor generates an individualized, predictive target range by accessing a database of historical patient data, identifying a subset of patients with pre-operative characteristics similar to those of the subject, and defining the target range based on intraoperative nerve function measurements associated with successful post-operative outcomes in that subset. A display presents the determined nerve function parameter relative to the individualized target range to provide real-time guidance. The system can integrate intraoperative imaging to concurrently display anatomical and functional information. A predictive model used to generate the target range is refined over time by incorporating outcomes from additional patients, enabling continuous improvement of the assessment.

Disclosed in the present application are a method and apparatus for tracking an intra-operative OCT imaging position, a surgical microscope system, and a storage medium. The method for tracking an intra-operative OCT imaging position comprises: according to a historical position of a surgical instrument in a first coordinate system at a first moment, determining a predicted position of the surgical instrument in a second coordinate system at a second moment, wherein the first coordinate system is used for OCT imaging, and the second coordinate system is used for microscope imaging; according to the predicted position, determining a detected position of the surgical instrument in the second coordinate system at the second moment; and according to the detected position, determining a mapped position of the surgical instrument in the first coordinate system at the second moment, so as to track the intra-operative OCT imaging position.

Described herein are systems and methods for generating an intraoperative fluorescence image of a subject. A system receives an intraoperative white light image of the subject and generates a semantic segmentation mask outlining a tumor in the intraoperative white light image. The semantic segmentation mask is generated by a machine learning model trained using a plurality of training image pairs in which each training image pair includes an intraoperative white light training image and an intraoperative fluorescence training image of a same tissue. An outline of the tumor is extracted from the mask, and an intraoperative fluorescence image is generated based on the outline of the tumor from the mask. An area inside the outline in the generated intraoperative fluorescence image is represented by a first color, and an area outside the outline in the generated intraoperative fluorescence image is represented by a second color.

A surgical robotic system, the surgical robotic system comprising: a first robotic arm having a first instrument and a first sensor; a second robot arm, which may include a second instrument and a second sensor; a laparoscopic camera configured to capture intraoperative imaging data; and a surgeon console including a handle controller configured to receive a user input for actuating at least one of the first robotic arm or the second robotic arm. The system also includes a controller configured to: control at least one of the first robot arm or the second robot arm based on the user input and a control algorithm; receiving a plurality of data streams, which may include sensor data from the first sensor and the second sensor, operational data, user input data, external visual data, and / or internal imaging data from the laparoscopic camera; predicting an occurrence of an event based on the plurality of data streams; and adjusting the control algorithm based on the predicted occurrence of the event.

A method for intraoperative guidance during a surgical procedure involving a nerve includes receiving real-time image data from an intraoperative imaging modality, the real-time image data depicting anatomical structures within an intracorporeal treatment area including the nerve; determining at least one nerve function parameter indicative of nerve health; and controlling a display device to concurrently present a visual output comprising both a visual representation derived from the real-time image data and a visual indication of the determined at least one nerve function parameter. The concurrently presented visual output facilitates assessment of the nerve relative to the anatomical structures during the surgical procedure.

A system for intraoperative medical instrument recognition may receive, from an imaging device positioned inside of a patient, a stream of intraoperative three-dimensional (3D) imaging data that includes anatomy of a patient and a medical instrument. The system may apply a material recognition algorithm to identify one or more objects formed of a predetermined material present in the intraoperative 3D imaging data. The system may apply a shape recognition algorithm to the one or more identified objects to identify the medical instrument. The system may generate a modified intraoperative imaging data stream in real-time with the receiving of the stream of intraoperative 3D imaging data. The modified intraoperative imaging data stream may include a visual characteristic, e.g., a colorization, applied to a region of the intraoperative 3D imaging data corresponding to the at least one identified medical instrument.

A system for intraoperative medical instrument recognition may receive, from an imaging device positioned inside of a patient, a stream of intraoperative three-dimensional (3D) imaging data that includes anatomy of a patient and a medical instrument. The system may apply a material recognition algorithm to identify one or more objects formed of a predetermined material present in the intraoperative 3D imaging data. The system may apply a shape recognition algorithm to the one or more identified objects to identify the medical instrument. The system may generate a modified intraoperative imaging data stream in real-time with the receiving of the stream of intraoperative 3D imaging data. The modified intraoperative imaging data stream may include a visual characteristic, e.g., a colorization, applied to a region of the intraoperative 3D imaging data corresponding to the at least one identified medical instrument.

A system and method for improving a quality of images for local registration where a position of a sensor is monitored to determine whether a breath hold plateau has been achieved, and following imaging is monitored to determine whether the sensor position has returned to a pre-breath hold position.

A system includes a display system, an elongate device, a medical instrument, and a control system. The control system is configured to: display intra-operative imaging data of a patient anatomy received from an external imaging system in a graphical user interface (GUI) on the display system; displaying a graphical indicator superimposed on the intraoperative imaging data; registering the intraoperative imaging data with the elongation device; transforming the graphical indicator from the intra-operative imaging data frame of reference to the elongated device frame of reference based on the registration; and providing navigational guidance to guide the elongate device to a target location in the patient anatomy based on the target location of the distal tip of the medical instrument displayed via the graphical indicator. The target location of the distal tip of the medical device is within the deployment range of the device when the elongate device is located at the target location.

A system for robot-assisted minimally invasive surgical navigation with real-time instrument localization and tissue mapping, comprising: a multi-jointed robotic arm with an end effector configured to hold at least one surgical instrument; a plurality of sensor units operatively connected to the surgical instrument and the robotic manipulator, the plurality of sensor units comprising at least one optical sensor unit, at least one electromagnetic sensor unit, and at least one inertial sensor unit; an intraoperative imaging unit configured to acquire real-time image data of the surgical field; and a data acquisition unit configured to receive signals from the plurality of sensor units and the intraoperative imaging unit.a signal conditioning unit configured to calibrate, synchronize, and reduce noise in the received signals; a processing unit configured to determine the spatial position and orientation of the surgical instrument by fusing data from the optical sensor unit, the electromagnetic sensor unit, and the inertial sensor unit; a registration unit configured to compare the intraoperative image data with the preoperative image data to create a unified anatomical reference; a tissue mapping unit configured to generate a three-dimensional representation of tissue structures based on the intraoperative image data; a navigation control unit configured to calculate instrument trajectories based on the spatial position, orientation, and generated three-dimensional representation;and a visualization unit configured to display in real time the spatial position of the surgical instrument in relation to the mapped tissue structures.

The application discloses a kind of based on binocular camera's graph convolution enhancement abdominal organ real-time tracking method, comprising the following steps: collecting data and being marked, preoperative model is constructed and is trained, reconstructs three-dimensional model and is optimized, adopts co-rotating finite element modeling, constructs biomechanical model, carries out preoperative initial registration, eliminates the change between preoperative and intraoperative imaging of patient, extracts a variety of abdominal organ features and matches feature points, carries out intraoperative deformation registration and dynamically tracks feature points, and verifies experimental results;The application efficiently solves the problem of complex deformation of soft tissue, accurately selects key feature points through deep learning algorithm, and creates displacement vector field, provides boundary conditions for biomechanical model in time, can effectively simulate various complex deformations, and can adaptively track the dynamic changes of soft tissue during operation, significantly improve the capture and modeling ability of complex deformation behavior of soft tissue.

A system includes one or more processors, a user display, and memory having computer readable instructions stored thereon. The computer readable instructions, when executed by the one or more processors, cause the system to obtain pre-operative image data of anatomical passages of a patient, wherein the pre-operative image data includes a target, display the pre-operative image data including the target in a graphical user interface on the user display, receive intra-operative image data from an imaging system, identify a portion of the intra-operative image data corresponding to an instrument disposed in the anatomical passages of the patient, receive a user selection of a portion of the intra-operative image data corresponding to the target, and update the display of the pre-operative image data including the target in the graphical user interface based on the intra-operative image data.

A method and system provide intra-operative visualization in robotic-assisted and minimally invasive surgeries through augmented reality (AR) overlays using dithering techniques. The system integrates pre-operative and intra-operative imaging directly into the surgeon's field of view on a stereoscopic 3D screen without obstructing critical anatomical views. Traditional alpha blending methods for AR overlays are computationally intensive. The disclosed dithering approach alternately displays augmented and real pixels to create a semi-transparent effect, preserving the endoscopic view and ensuring patient safety. Transparency is dynamically adjustable using hand controllers, foot pedals, eye tracking, and voice commands. This innovation provides surgeons with critical subsurface anatomical information during procedures, reducing cognitive load and improving surgical outcomes by minimizing the risk of damaging critical structures. The system is compatible with existing surgical robotic platforms, offering an efficient and safe solution for integrating AR in surgical environments.

Described herein are systems and methods for refraction correction of OCT images configured for intraoperative use, including: a deep-learning model configured to receive a plurality of OCT images; and an imaging device configured to generate the plurality OCT images during an intraoperative procedure; wherein the deep-learning model is configured to map, in real-time, the plurality of OCT images to a two-dimensional vector field over a spatial domain of the plurality of OCT images; wherein the mapping is configured for real-time correction of refraction errors on the plurality of OCT images by employing Snell's law to generate a plurality of refraction-corrected OCT images via the deep-learning model. Other examples are described including features of the deep-learning model uniquely configured for interoperative usage such as an OCT image processing speed of 20Hz-50Hz.

A system for executing a three-dimensional (3D) intraoperative scan of a patient is disclosed. A 3D scanner controller projects the object points included onto a first image plane and the object points onto a second image plane. The 3D scanner controller determines first epipolar lines associated with the first image plane and second epipolar lines associated with the second image plane based on an epipolar plane that triangulates the object points included in the first 2D intraoperative image to the object points included in the second 2D intraoperative image. Each epipolar lines provides a depth of each object as projected onto the first image plane and the second image plane. The 3D scanner controller converts the first 2D intraoperative image and the second 2D intraoperative image to the 3D intraoperative scan of the patient based on the depth of each object point provided by each corresponding epipolar line.

A non-transitory, computer-readable medium storing instructions thereon that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising: generating a point cloud of coordinate points based, at least in part, on positional sensor data captured using a position sensor, wherein the positional sensor data is associated with one or more positions of a biomedical device within an anatomic region of a patient; receiving first image data of patient anatomy captured using an image capture device positioned within the anatomic region; receiving second image data of the anatomic region, wherein the second image data is generated based, at least in part, on preoperative or intraoperative imaging of the anatomic region; generating a registration between at least a portion of the point cloud with at least a portion of the second image data; and updating the registration based, at least in part, on the first image data.

A method of producing high quality 3D CT medical imaging models, real-time, from 2D X-ray images acquired from convention imaging equipment, such as X-ray or C-arm devices, already present in an operating room, thereby obviating the need to bring in, and sterilize, additional equipment presently used for 3D image navigation.

The application discloses a kind of near-infrared long afterglow luminescent nano probes, its preparation method and application, belong to near-infrared long afterglow material field. With mesoporous silica as template, with zinc acetate, gallium nitrate, tin chloride, chromium acetate, chromium nitrate and yttrium nitrate as doped metal raw materials, long afterglow luminescent core is obtained by reaction and calcination;The outer layer of long afterglow luminescent core is coated with manganese dioxide, to obtain pre-product;Pre-product is surface modified, and coupling target molecule plerixafor, i.e. near-infrared long afterglow luminescent nano probe. The near-infrared long afterglow luminescent nano probe has good biological safety, can be effectively enriched in the tumor area in vivo after mouse tail vein injection, can realize high-sensitivity imaging of multiple small tumors in vivo under red light LED irradiation, and the imaging time is short, suitable for intraoperative imaging, can be used for the imaging of breast cancer tumor model to guide tumor resection.

The invention discloses a fracture reduction path planning system and method based on image data, and the method comprises the steps: carrying out the fine anatomical structure segmentation of high-resolution preoperative image data, firstly constructing a three-dimensional bone block model and a three-dimensional high-risk soft tissue model, carrying out the meshing of the three-dimensional bone block model and the three-dimensional high-risk soft tissue model based on anatomical map data, and carrying out the risk assignment, a predictive static probability risk field network is obtained; in the operation process, 2D-3D registration and tracking are conducted through intraoperative imaging data, real-time displacement information of a key tracking point is obtained, then the static probability risk field network is dynamically updated, and an updated probability risk field network capable of reflecting current soft tissue real deformation is formed; and finally, based on the updated risk field and the current pose of the robot, self-adaptive adjustment of a local path is carried out, so that a safe and efficient fracture reduction path is accurately planned. In this way, the precision and safety of the fracture reduction surgery are remarkably improved.

A method for intraoperative guidance during a surgical procedure involving a nerve includes receiving real-time image data from an intraoperative imaging modality, the real-time image data depicting anatomical structures within an intracorporeal treatment area including the nerve; determining at least one nerve function parameter indicative of nerve health; and controlling a display device to concurrently present a visual output comprising both a visual representation derived from the real-time image data and a visual indication of the determined at least one nerve function parameter. The concurrently presented visual output facilitates assessment of the nerve relative to the anatomical structures during the surgical procedure.

The invention provides a real-time panoramic photoacoustic endoscopic navigation method and device based on three-dimensional image stitching, and relates to the technical field of data processing.The method comprises the steps that the six-degree-of-freedom pose of a probe is solved in real time based on a feature corresponding relation, and a vascular elastic parameter obtained through photoacoustic signal rising time estimation is used as a region weight; spatial transformation and superposition are carried out on each frame of point cloud according to the pose, local registration and voxel fusion are carried out in an overlapping region, and a continuous and consistent three-dimensional photoacoustic image is generated; in the scanning process, when it is detected that a similar structure exists in a current image and a historical image, loop detection is executed, and global optimization is carried out, so that an optimized three-dimensional panoramic image is obtained; and displaying the optimized three-dimensional panoramic image in real time, and superposing the pose information of the probe or the surgical instrument to form an intraoperative navigation interface. According to the invention, the stability and the space consistency of intraoperative imaging are obviously improved.

The present disclosure describes a hybrid navigation system comprising two or more data acquisition apparatuses of different types that addresses limitations of current methods. In some embodiments, the two or more data acquisition apparatuses include at least one structured light three-dimensional scanner-based navigation system and at least one other navigation system (e.g., including but not limited to stereoscopic, ultrasound, electromagnetic, and / or intraoperative imaging). The hybrid approach improves real-time anatomical tracking, reduces the invasiveness of pin-based optical trackers, and maintains compatibility with established workflows. The principles of this disclosure extend across diverse surgical applications, offering enhanced accuracy, flexibility, and efficiency in both traditional and next-generation navigation environments.

A system receives a set of two-dimensional projection images associated with respective projection angles from an imaging device. The projection images include projections of an anatomical structure, an anatomical target, and possibly fiducial markers, with a flexible elongate device disposed therein. The system can reconstruct a three-dimensional image from the set of two-dimensional projection images. In some examples, prior to reconstruction, the system can detect and repair pixels (within the projection images) associated with the fiducial markers, the flexible elongate device, and / or other objects. The system can enhance the reconstruction using a style transfer model. Further, the system can receive shape data associated with the flexible elongate device in a sensor / instrument coordinate system. The system can compute a transformation between an imaging coordinate system and the instrument coordinate system, identify the target within the reconstructed image, and update the target position in the instrument coordinate system using the computed transformation.

A method for intraoperative guidance during a surgical procedure involving a nerve includes receiving real-time image data from an intraoperative imaging modality, the real-time image data depicting anatomical structures within an intracorporeal treatment area including the nerve; determining at least one nerve function parameter indicative of nerve health; and controlling a display device to concurrently present a visual output comprising both a visual representation derived from the real-time image data and a visual indication of the determined at least one nerve function parameter. The concurrently presented visual output facilitates assessment of the nerve relative to the anatomical structures during the surgical procedure.

The present disclosure relates generally to medical imaging, and more specifically to machine-learning techniques to generate intraoperative fluorescence images of a subject (e.g., to aid a surgery, to aid diagnosis and treatment of diseases). The system can receive an intraoperative white light image of the subject, input the intraoperative white light image of the subject into a generator of a trained generative adversarial network (GAN) model trained. In some examples, the GAN model is trained using a plurality of training image pairs, and each training image pair comprises an intraoperative white light training image and an intraoperative fluorescence training image of a same tissue. The system can obtain, from the generator, the generated intraoperative fluorescence image of the subject and display, on a display, the generated intraoperative fluorescence image of the subject.

Systems and methods for navigating during a medical procedure are described. The processing unit receives, from the sensing system, an indication of a position in a first coordinate system of a first sensor and a second sensor disposed at the flexible elongate device. The processing unit also receives imaging data from the imaging unit and determines an indication of a position of the reference of the flexible elongate device in the second coordinate system based on the imaging data. The reference is disposed at the flexible elongate device at a separation distance from the first sensor. The processing unit then registers the first coordinate system to the second coordinate system based on the received imaging data and the received sensor system data.

The invention belongs to the field of image guidance, and particularly relates to a robot treatment energy delivery system and method for image guidance, and the system comprises a robot delivery platform, a preoperative planning module, an intraoperative imaging module and a control and navigation unit. The robotic delivery platform includes a robotic arm integration base. The preoperative planning module generates a 3D treatment plan based on a medical image, the intraoperative imaging module provides a real-time image of a target volume in the treatment process, and the control and navigation unit performs spatial registration on the preoperative plan and the current position of a patient and continuously compares the real-time image with the plan, so that the 3D treatment plan is obtained. It dynamically generates and transmits a correction signal to the multi-axis robotic arm to compensate for any deviation or movement of the target volume to ensure delivery of therapeutic energy with high accuracy.