Brainlab Ag

Ivana Ivanovska

Hagen Kaiser

Pablo Aponte

Jochen Veigel

Pascal Bertram

Birte Domnik

Elisa Garcia Corsico

First and second skeleton model data is determined based on first and second surface data of a patient. Each of the skeleton model data describes geometries of rigid anatomic structures of a patient at a different point in time. Skeleton difference data is determined describing differences between the geometries of the rigid anatomic structures. In a next step, movement instruction data is determined which describes movement to be performed by the rigid anatomic structures to minimize the differences, i.e. to correct the posture of the patient. The movement instruction data is for example determined based on anatomy constraint data which describes anatomical movement constraints for the rigid anatomic structures (e.g. range of motion of a joint). An instruction is displayed (e.g. using augmented reality), guiding the user how to move the rigid anatomic structures so as to correct the patients posture.

Patient positioning using a skeleton model

A method for determining the relative position between a first camera and a second camera used in a medical application, wherein the first camera captures a 2D image of a phantom, the second camera emits light onto the phantom and analyzes the reflected light, thus generating a 3D point cloud representing points on the surface of the phantom, and the phantom has a planar surface forming a background on which a plurality of 2D markers are formed, wherein one of the background and the 2D markers is reflective, thus reflecting light emitted by the second camera back to the second camera, and the other one is non-reflective, thus not reflecting light emitted by the second camera back to the second camera, the method involving that a) the first camera captures a 2D image of the phantom, b) the second camera generates a 3D point cloud representing the planar surface of the phantom, c) the 2D markers are identified in the 2D image, thus obtaining 2D marker data representing the locations of the 2D markers in the 2D image, d) the 2D markers are identified in the 3D point cloud using the property that points on a non-reflective part of the planar surface are identified as having a larger distance to the second camera than points on a reflective part of the planar surface, thus obtaining 3D marker data representing the locations of the 2D markers in a reference system of the second camera, and e) finding the relative position between the first camera and the second camera by applying a Perspective-n-Points algorithm on the 2D marker data and the 3D marker data.

Determining the relative position between a point cloud generating camera and another camera

Disclosed is a computer-implemented method for determining the pose of an anatomical body part of a patient's body for planning radiation treatment, a corresponding computer program, a non-transitory program storage medium storing such a program and a computer for executing the program, as well as a system for determining the pose of an anatomical body part of a patient's body for planning radiation treatment, the system comprising an electronic data storage device and acquire surface tracking data the aforementioned computer.

Surface-guided x-ray registration

A medical data processing method, performed by a computer (), for determining error analysis data describing the registration accuracy of a first elastic registration between first and second image data (A, B) describing images of an anatomical structure of a patient, comprising the steps of: &#x2014;acquiring the first image data (A) describing a first image of the anatomical structure, &#x2014;acquiring the second image data (B) describing a second image of the anatomical structure, &#x2014;determining first registration data describing a first elastic registration of the first image data (A) to the second image data (B) by mapping the first image data (A) to the second image data (B) using a registration algorithm, &#x2014;determining second registration data describing a second elastic registration of the second image data (B) to the first image data (A) by mapping the second image data (B) to the first image data (A) using the registration algorithm, &#x2014;determining error analysis data describing the registration accuracy of the first elastic registration based on the first registration data and the second registration data.

Determination of registration accuracy

First and second skeleton model data is determined based on first and second surface data of a patient. Each of the skeleton model data describes geometries of rigid anatomic structures of a patient at a different point in time. Skeleton difference data is determined describing differences between the geometries of the rigid anatomic structures. In a next step, movement instruction data is determined which describes movement to be performed by the rigid anatomic structures to minimize the differences, i.e. to correct the posture of the patient. The movement instruction data is for example determined based on anatomy constraint data which describes anatomical movement constraints for the rigid anatomic structures (e.g. range of motion of a joint). An instruction is displayed (e.g. using augmented reality), guiding the user how to move the rigid anatomic structures so as to correct the patient's posture.

Disclosed is a computer-implemented medical data processing method for supporting positioning a patient for treatment by at least one of radiotherapy or radiosurgery, the method comprising executing, on at least one processor of at least one computer, steps of: a) acquiring (S), at the at least one processor, planning image data describing a digital planning image of an anatomical body part of a body () of the patient; b) acquiring (S), at the at least one processor, planning image alignment data describing a relative position between the anatomical body part and a predetermined reference position at the point in time when the planning image data was generated; c) acquiring (S), at the at least one processor, thermal reference image data describing a thermal reference image of the anatomical body part; d) acquiring (S), at the at least one processor, thermal imaging device reference position data describing a relative position between a thermal imaging device () used for taking the thermal reference image and the predetermined reference position; e) determining (S), by the at least one processor and based on the planning image data and the planning image alignment data and the thermal image data and the thermal imaging device position data, image projection data describing a projection of the image representation of at least the anatomical body part described by the planning image data onto the image representation of the anatomical body part described by the thermal reference image data.

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Aschheim, Germany

Ivana Ivanovska