Abstract: A method is for determining a patient weight and/or a body mass index of a patient. The method includes acquiring image data containing depth information of the patient; generating a surface model of the patient based upon the image data acquired; determining density information or X-ray attenuation information of at least part of the patient; and determining at least one of the total patient weight and the body mass index of the patient using the surface model generated and the density information or X-ray attenuation information determined.

Abstract: In an embodiment, a method includes providing a three-dimensional structure image including surface data relating to external contours and structure data relating to internal structures of a body region; recording the body region via a camera system, to produce a recording while a patient is situated in or on the medical imaging system; registering, at least locally and as a structure image, the three-dimensional surface image in relation to the three-dimensional structure image; producing an overlay image including at least part of the structure image registered including structure data of the three-dimensional structure image in a form of an overlay with at least one of the three-dimensional surface image and a real view of a corresponding part of the body region of the patient; and controlling the medical imaging system to record body regions of the patient based upon an arrangement of the structure data registered in the overlay image.

Abstract: A framework for sensor-based patient treatment support. In accordance with one aspect, one or more sensors are used to acquire sensor data of one or more objects of interest. The sensor data is then automatically interpreted to generate processing results. One or more actions may be triggered based on the processing results to support treatment of a patient, including supporting medical scanning of the patient.

Abstract: For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.

Abstract: A method of obtaining a medical image includes obtaining, via a camera, at least one surface image of a patient. A pose of the patient is determined from the at least one surface image of the patient using at least one spatial information module. The patient is positioned, via a moveable bed, to an imaging start position and a medical image of the patient is obtained using a medical imaging modality.

Abstract: An internal combustion engine, in particular a gas Otto-cycle engine, is provided. The internal combustion engine comprises a plurality of cylinders. Each cylinder is provided with a pre-chamber, and a pre-chamber gas supply conduit through which the pre-chamber can be supplied with fuel gas. The fuel gas is supplied to the pre-chambers by way of a pre-chamber gas valve associated with the respective pre-chamber. Also, an aperture is arranged between the pre-chamber gas supply conduit and the pre-chamber gas valve. At least one aperture associated with a pre-chamber gas valve has a through-flow coefficient such that pressure occurring at a maximum between combustion cycles in a volume between the pre-chamber gas valve and the aperture does not reach a pressure prevailing in the pre-chamber gas supply conduit.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Machine learning is used to train a network to predict the location of an internal body marker from surface data. A depth image or other image of the surface of the patient is used to determine the locations of anatomical landmarks. The training may use a loss function that includes a term to limit failure to predict a landmark and/or off-centering of the landmark. The landmarks may then be used to configure medical scanning and/or for diagnosis.

Abstract: A method is for determining a patient weight and/or a body mass index of a patient. The method includes acquiring image data containing depth information of the patient; generating a surface model of the patient based upon the image data acquired; determining density information or X-ray attenuation information of at least part of the patient; and determining at least one of the total patient weight and the body mass index of the patient using the surface model generated and the density information or X-ray attenuation information determined.

Abstract: A method of obtaining a medical image includes obtaining, via a camera, at least one surface image of a patient. A pose of the patient is determined from the at least one surface image of the patient using at least one spatial information module. The patient is positioned, via a moveable bed, to an imaging start position and a medical image of the patient is obtained using a medical imaging modality.

Abstract: A method and a device are disclosed for the perspective representation, via an output image, of at least one virtual scene component arranged within a real scene. In the method, depth image data of the real scene is captured from a first perspective via a depth image sensor, and 2D image data of the real scene is captured from a second perspective via a 2D camera. Further, a virtual three-dimensional scene model of the real scene is created with reference to depth information from the depth image data, and at least one virtual scene component is inserted into the three-dimensional virtual model. Finally, an output image is generated by way of perspective projection of the 2D image data corresponding to the second perspective onto the virtual three-dimensional scene model comprising the virtual scene component.

Abstract: The present embodiments relate to cinematic volume renderings and/or volumetric Monte-Carlo path tracing. By way of introduction, the present embodiments described below include apparatuses and methods for cinematic rendering of unfolded three-dimensional volumes. An image analysis algorithm is performed on an input volume to extract one or more structures of interest, such as a rib centerline, a liver surface or another three-dimensional volume. Based on the extracted three-dimensional structure(s), a geometric transformation is computed to generate an unfolded three-dimensional volume of the structure(s). Cinematic volume rendering techniques are used to generate a rendered image from the unfolded three-dimensional volume.

Abstract: In an embodiment, a method includes providing a three-dimensional structure image including surface data relating to external contours and structure data relating to internal structures of a body region; recording the body region via a camera system, to produce a recording while a patient is situated in or on the medical imaging system; registering, at least locally and as a structure image, the three-dimensional surface image in relation to the three-dimensional structure image; producing an overlay image including at least part of the structure image registered including structure data of the three-dimensional structure image in a form of an overlay with at least one of the three-dimensional surface image and a real view of a corresponding part of the body region of the patient; and controlling the medical imaging system to record body regions of the patient based upon an arrangement of the structure data registered in the overlay image.

Abstract: Machine learning is used to train a network to predict the location of an internal body marker from surface data. A depth image or other image of the surface of the patient is used to determine the locations of anatomical landmarks. The training may use a loss function that includes a term to limit failure to predict a landmark and/or off-centering of the landmark. The landmarks may then be used to configure medical scanning and/or for diagnosis.

Abstract: For human body representation, bone length or other size characteristic that varies within the population is incorporated into the geometric model of the skeleton. The geometric model may be normalized for shape or tissue modeling, allowing modeling of the shape without dedicating aspects of the data-driven shape model to the length or other size characteristic. Given the same number or extent of components of the data-driven shape model, greater or finer details of the shape may be modeled since components are not committed to the size characteristic.

Abstract: For human body representation, bone length or other size characteristic that varies within the population is incorporated into the geometric model of the skeleton. The geometric model may be normalized for shape or tissue modeling, allowing modeling of the shape without dedicating aspects of the data-driven shape model to the length or other size characteristic. Given the same number or extent of components of the data-driven shape model, greater or finer details of the shape may be modeled since components are not committed to the size characteristic.

Abstract: A method for determining at least one protocol parameter for a contrast agent-assisted acquisition of images of a region that is to be examined of an examination subject via a medical imaging device is described. In an embodiment of the method, an acquisition of external images of the exterior of the examination subject is first performed with the aid of an external image acquisition unit. At least one body dimension of the examination subject is then determined on the basis of the acquired external images. Finally, at least one contrast agent protocol parameter is determined on the basis of the at least one determined body dimension. Furthermore, an image acquisition parameter determination device is described. In addition, an imaging medical device is also described.

Abstract: The present embodiments relate to cinematic volume renderings and/or volumetric Monte-Carlo path tracing. By way of introduction, the present embodiments described below include apparatuses and methods for cinematic rendering of unfolded three-dimensional volumes. An image analysis algorithm is performed on an input volume to extract one or more structures of interest, such as a rib centerline, a liver surface or another three-dimensional volume. Based on the extracted three-dimensional structure(s), a geometric transformation is computed to generate an unfolded three-dimensional volume of the structure(s). Cinematic volume rendering techniques are used to generate a rendered image from the unfolded three-dimensional volume.

Abstract: A method and a device are disclosed for the perspective representation, via an output image, of at least one virtual scene component arranged within a real scene. In the method, depth image data of the real scene is captured from a first perspective via a depth image sensor, and 2D image data of the real scene is captured from a second perspective via a 2D camera. Further, a virtual three-dimensional scene model of the real scene is created with reference to depth information from the depth image data, and at least one virtual scene component is inserted into the three-dimensional virtual model. Finally, an output image is generated by way of perspective projection of the 2D image data corresponding to the second perspective onto the virtual three-dimensional scene model comprising the virtual scene component.

Abstract: For therapy response assessment, texture features are input for machine learning a classifier and for using a machine learnt classifier. Rather than or in addition to using formula-based texture features, data driven texture features are derived from training images. Such data driven texture features are independent analysis features, such as features from independent subspace analysis. The texture features may be used to predict the outcome of therapy based on a few number of or even one scan of the patient.