Brainlab AG

By using the Al module, the method of the present invention calculates, i.e. predicts, the dependency C i (p i ) of a radiotherapy (RT) quality criterion C, from an adjustment of such a radiotherapy planning parameter p i . In this way, the decision making process in RT treatment plan optimization is streamlined by prediction of promising settings of one or more radiotherapy planning parameters p, before the actual time intensive iterative optimization process is carried out. This is achieved by applying an Al module, which has been trained to predict the specific behaviour of the dose optimization algorithm, i.e. the optimizer, with respect to geometric patient data, dose prescription and treatment indication data. Thus, a computer-implemented medical method of predicting a dependency C i (p i ) of a radiotherapy (RT) quality criterion C i from an adjustment of a radiotherapy planning parameter p, is presented. The method comprises the following steps of providing geometric patient data geometrically describing an area of a patient, which is to be irradiated according to a radiotherapy treatment plan (step S 1 ), providing dose prescription data and treatment indication data for said patient (step S 2 ), and predicting with a trained Artificial Intelligence (Al) module the dependency C i (p i ) of the radiotherapy quality criterion C i from the radiotherapy planning parameter p, when adjusting said radiotherapy planning parameter p i , thereby using the geometric patient data, the dose prescription data and the treatment indication data as input for the Al module (step S 3 ).

The disclosed method encompasses receiving an input video stream showing at least a part of at least one device which is in use in a medical environment. A device has a region of interest (ROI). The area of the input video stream which shows the region of interest of a device is extracted from the input video stream and provided as an ROI video stream. The ROI for example comprises a display, such that the information shown on the display is provided in the ROI video stream.

Provided is a computer-implemented method for assessing a medical image of an object imaged with an X-ray system. The method proposes to generate one or more positioning data sets, which differ in at least one positioning parameter. The X-ray system images medical images of the object with the one or more positioning data sets. The obtained medical images were assessed by measuring a quality measure in the medical images and comparing the measured quality measures with an evaluation criterion. A positive assessed medical image with the corresponding positioning data is then provided for further processing.

A computer-implemented method determines the position of a stimulation electrode. The method encompasses determination of the orientation of a medical stimulation electrode from medical image data showing an image of the electrode. A first scan is generated from which the position of the electrode is determined, and a second scan with optimized imaging parameters is generated using the same or a different imaging modality. The second scan shows a more precise image of the electrode and is used to determine the rotational orientation of the electrode along its longitudinal axis.

The present invention relates to a method for registering a virtual representation of a patient anatomy with a coordinate system of a physical patient anatomy, comprising displaying first overlap data in a head-mounted device, wherein the first overlap data describe from a first perspective onto the physical patient anatomy a first visual overlap between the virtual representation of the patient anatomy and the physical patient anatomy, identifying at least a first area in the first visual overlap and/or at least a first anatomical feature of the patient anatomy in the first visual overlap having at least a minimum degree of alignment between the virtual representation and the physical patient anatomy, displaying second overlap data in the head-mounted device, wherein the second overlap data describe from a second perspective onto the physical patient anatomy a second visual overlap between the virtual representation of the patient anatomy and the physical patient anatomy, and taking into account, during the displaying of the second overlap data, the alignment of the identified first area and/or anatomical feature of the patient anatomy that was identified in the first visual overlap as having at least a minimum degree of alignment between the virtual representation and the physical patient anatomy.

Disclosed is a computer-implemented method which encompasses registering a tracked imaging device such as a microscope having a known viewing direction and an atlas to a patient space so that a transformation can be established between the atlas space and the reference system for defining positions in images of an anatomical structure of the patient. Labels are associated with certain constituents of the images and are input into a learning algorithm such as a machine learning algorithm, for example a convolutional neural network, together with the medical images and an anatomical vector and for example also the atlas to train the learning algorithm for automatic segmentation of patient images generated with the tracked imaging device. The trained learning algorithm then allows for efficient segmentation and/or labelling of patient images without having to register the patient images to the atlas each time, thereby saving on computational effort.

This document relates to technologies of determining a configuration of a medical imaging system comprising an x-ray source and an x-ray detector mounted on a gantry and an x-ray source collimator for shaping the x-ray beam emitted by the x-ray source, wherein at least one of the x-ray source and the x-ray detector is movable along the gantry. The configuration of the medical imaging system comprises the position of the x-ray source, the position of the x-ray detector and the settings of the x-ray source collimator and is to be used for capturing a marker image, wherein the position of the marker device can be calculated using the marker image.

The present invention relates to a method for determining the spatial position of objects, in particular medical objects. First position data is acquired that describes a spatial position of an object in a first coordinate system. First transformation data is acquired that transforms the object's position from the first coordinate system to a second coordinate system. Based on the foregoing data, second position data is acquired that specifies the spatial position of the object in the second coordinate system. Second transformation data is acquired that transforms the object's position from the second coordinate system to an inertial coordinate system. Based on the second position data and the second transformation data, inertial position data is determined that specifies a position of the object in the inertial coordinate system.

A computer-implemented method plans a position of a tracking reference device for referencing a position in a medical environment. The method includes a determination of avoidance regions in which a tracking reference device should not be placed so as to safeguard proper tracking of the tracking reference device and/or an instrument tracking reference device which is attached to a medical instrument. The avoidance region is a region lying, from the point of view of a tracking device for tracking the tracking reference device, in the shadow of an envelope surrounding at least one medical instrument. Additionally or alternatively, an avoidance region may lie in between the position of the tracking device and the envelope to avoid a shadowing, by the tracking reference device, of an instrument tracking reference device attached to the medical instrument. Information describing the position of the at least one avoidance region is displayed to a user, and also information about the position of a region which is suitable for placement of the tracking reference device can be displayed to the user.

An articulated robotic platform is provided as an end-actuator intended to be fitted to an articulated robotic arm. One use of the articulated robotic platform is to more accurately guide surgical tools during a surgical intervention making it possible to achieve positional accuracy on the level of millimeters or fractions of a millimeter. The articulated robotic platform includes support members attached to the patient or anchoring the articulated robotic platform onto a clamp. The support members of the articulated robotic platform are capable of changing in length, and this change can be monitored by an extension measurement sensor fitted to one or more of the support members. The controller, when provided with feedback information of the extension measurement and direction of several of the legs, can calculate accurately the position of the articulated robotic platform relative to an intervention area of a patient.