Abstract: An X-ray imaging system (100) includes an X-ray source (110), an X-ray detector (120), a depth camera (130), and a processor (140). The processor (140) receives depth camera image data from the depth camera, projects the depth camera image data onto a radiation-receiving surface of the X-ray detector (120), from a perspective of the X-ray source (110), and generates an image representation (170) of the projected depth camera image data on the radiation-receiving surface of the X-ray detector (120), from a perspective of the depth camera (130).

Abstract: The present invention relates to a system (10) and related method for planning a tomographic image acquisition of an object to be imaged by a tomographic imaging scanner (20). The system comprises an input (12) for receiving a 3D pre-scan image of the object and a camera system (14) for capturing 3D image information of the object. The system comprises a processor (16) for determining corresponding image features in the camera image and the pre-scan image. The processor determines an image transformation that relates the corresponding image features to each other, such that the object as represented by the pre-scan image can be transformed to the orientation, position and/or deformation of the object represented in the camera image. The processor plans the image acquisition, in which the pre-scan image is used to determine parameters including scan range. An output (18) outputs a signal representative of the determined plan.

Abstract: A cortical malformation identification method includes quantitatively evaluating, using a processor of a computer that includes the processor and a memory, digital image data from a magnetic resonance imaging (MRI) scan on a cerebral cortex to produce quantified scan data. The method also includes automatically detecting a cortical malformation based on the quantified scan data. An image of the cerebral cortex may be color-coded so that the cortical malformation is shown in a different color than the remainder of the cerebral cortex in the image, based on the quantified scan data. Additionally or alternatively, a 3-dimensional representation of the cerebral cortex may be mapped to the quantified scan data to produce a mapped image of the cerebral cortex including the detected cortical malformation.

Abstract: The invention provides for a medical apparatus (100, 300, 400) comprising a subject support (102) configured for moving a subject (106) from a first position (124) to a second position (130) along a linear path (134). The subject support comprises a support surface (108) for receiving the subject. The subject support is further configured for positioning the subject support in at least one intermediate position (128). The subject support is configured for measuring a displacement (132) along the linear path between the first position and the at least one intermediate position. Each of the at least one intermediate position is located between the first position and the second position. The medical apparatus further comprises a camera (110) configured for imaging the support surface in the first position.

Abstract: There is provided a computer implemented method (200) for medical image processing. The method comprises providing (202) a database of medical images and providing (204) an initial machine learning model which is trained for segmenting or classifying a medical feature in the medical images. The method also comprises extracting (206) a subset of medical images from the database based on a similarity score of the medical images and training (208) the machine learning model using the extracted subset of medical images in order to provide a refined machine learning model.

Abstract: A seizure characterization method includes correlating locations of electrodes placed around a brain and used to produce sequential electroencephalography (EEG) signals with a three-dimensional anatomical brain model derived from magnetic resonance imaging (MRI). The sequential EEG signals are modelled from the electrodes placed around the brain in three dimensions using cortical and sub-cortical brain regions included in the brain model to define constraints for the numerical solution. Amounts of the sequential EEG signals are quantified in three dimensions relative to the brain regions included in the brain model. The method also includes establishing, based on the quantifying, at least one propagation pattern of the sequential EEG signals in time relative to the brain regions in the brain model.

Abstract: The present invention relates to an X-ray radiograph apparatus (10). It is described to placing (110) an X-ray source (20) relative to an X-ray detector (30) to form an examination region for the accommodation of an object, wherein, a reference spatial coordinate system is defined on the basis of geometry parameters of the X-ray radiography apparatus. A camera (40) is located (120) at a position and orientation to view the examination region. A depth image of the object is acquired (130) with the camera within a camera spatial coordinate system, wherein within the depth image pixel values represent distances for corresponding pixels.

Abstract: The appropriate positioning of a patient in an X-Ray imaging system can present difficulties for medical professional owing, on one hand to the small size of important anatomical aspects which need to be captured in X-Ray images, and on the other hand to the significant movements in a field of view presented by a typical patient. The present application proposes to obtain an image of the position of a patient in the field of view at approximately the same time that an initial X-Ray image is obtained. If it proves necessary to obtain a subsequent X-Ray image with updated field of view settings (for example, collimation parameters), the movement of the patient at the point of taking the second image is factored into the provision of updated field of view settings.

Abstract: A method and apparatus for segmenting a two-dimensional image of an anatomical structure includes acquiring (202) a three-dimensional model of the anatomical structure. The three-dimensional model includes a plurality of segments. The acquired three-dimensional model is adapted to align the acquired three-dimensional model with the two-dimensional image (204). The two-dimensional image is segmented by the plurality of segments of the adapted three-dimensional model.

Abstract: The invention provides for a medical instrument (100) comprising a processor (134) and a memory (138) containing machine executable instructions (140). Execution of the machine executable instructions causes the processor to: receive (200) a first magnetic resonance image data set (146) descriptive of a first region of interest (122) of a subject (118) and receive (202) at least one second magnetic resonance image data set (152, 152?) descriptive of a second region of interest (124) of the subject. The first region of interest at least partially comprises the second region of interest. Execution of the machine executable instructions further cause the processor to receive (204) an analysis region (126) within both the first region of interest and within the second region of interest.

Abstract: A planning tool, system and method include a processor (114) and memory (116) coupled to the processor which stores a planning module (144). A user interface (120) is coupled to the processor and configured to permit a user to select a path through a pathway system (148). The planning module is configured to upload one or more slices of an image volume (111) corresponding to a user-controlled cursor point (108) guided using the user interface such that as the path is navigated the one or more slices are updated in accordance with a depth of the cursor point in the path.

Abstract: There is provided a computer implemented method (200) for medical image processing. The method comprises providing (202) a database of medical images and providing (204) an initial machine learning model which is trained for segmenting or classifying a medical feature in the medical images. The method also comprises extracting (206) a subset of medical images from the database based on a similarity score of the medical images and training (208) the machine learning model using the extracted subset of medical images in order to provide a refined machine learning model.

Abstract: The invention provides for a medical apparatus (100, 300, 400) comprising a subject support (102) configured for moving a subject (106) from a first position (124) to a second position (130) along a linear path (134). The subject support comprises a support surface (108) for receiving the subject. The subject support is further configured for positioning the subject support in at least one intermediate position (128). The subject support is configured for measuring a displacement (132) along the linear path between the first position and the at least one intermediate position. Each of the at least one intermediate position is located between the first position and the second position. The medical apparatus further comprises a camera (110) configured for imaging the support surface in the first position.

Abstract: The present invention relates to an X-ray radiograph apparatus (10). It is described to placing (110) an X-ray source (20) relative to an X-ray detector (30) to form an examination region for the accommodation of an object, wherein, a reference spatial coordinate system is defined on the basis of geometry parameters of the X-ray radiography apparatus. A camera (40) is located (120) at a position and orientation to view the examination region. A depth image of the object is acquired (130) with the camera within a camera spatial coordinate system, wherein within the depth image pixel values represent distances for corresponding pixels.

Abstract: The present invention relates to medical image editing. In order to facilitate the medical image editing process, a medical image editing device (50) is provided that comprises a processor unit (52), an output unit (54), and an interface unit (56). The processor unit (52) is configured to provide a 3D surface model of an anatomical structure of an object of interest. The 3D surface model comprises a plurality of surface sub-portions. The surface sub-portions each comprise a number of vertices, and each vertex is assigned by a ranking value. The processor unit (52) is further configured to identify at least one vertex of vertices adjacent to the determined point of interest as an intended vertex. The identification is based on a function of a detected proximity distance to the point of interest and the assigned ranking value. The output unit (54) is configured to provide a visual presentation of the 3D surface model.

Abstract: A system and method are provided for selecting an acquisition parameter for an imaging system. The acquisition parameter at least in part defines an imaging configuration of the imaging system during an imaging procedure with a patient. A depth-related map is accessed which is generated on the basis of sensor data from a camera system, wherein the camera system has a field of view which includes at least part of a field of view of the imaging system, wherein the sensor data is obtained before the imaging procedure with the patient and indicative of a distance that parts of the patient's exterior have towards the camera system. A machine learning algorithm is applied to the depth-related map to identify the acquisition parameter, which may be provided to the imaging system.

Abstract: The appropriate positioning of a patient in an X-Ray imaging system can present difficulties for medical professional owing, on one hand to the small size of important anatomical aspects which need to be captured in X-Ray images, and on the other hand to the significant movements in a field of view presented by a typical patient. The present application proposes to obtain an image of the position of a patient in the field of view at approximately the same time that an initial X-Ray image is obtained. If it proves necessary to obtain a subsequent X-Ray image with updated field of view settings (for example, collimation parameters), the movement of the patient at the point of taking the second image is factored into the provision of updated field of view settings.

Abstract: A method for processing image data includes obtaining a first set of 3D volumetric image data. The 3D volumetric image data includes a volume of voxels. Each voxel has an intensity. The method further includes obtaining a local voxel noise estimate for each of the voxels of the volume. The method further includes processing the volume of voxels based at least on the intensity of the voxels and the local voxel noise estimates of the voxels. An image data processor (124) includes a computer processor that at least one of: generate a 2D direct volume rendering from first 3D volumetric image data based on voxel intensity and individual local voxel noise estimates of the first 3D volumetric image data, or registers second 3D volumetric image data and first 3D volumetric image data based at least one individual local voxel noise estimates of second and first 3D volumetric image data sets.

Abstract: The invention provides for a medical instrument (100) comprising a processor (134) and a memory (138) containing machine executable instructions (140). Execution of the machine executable instructions causes the processor to: receive (200) a first magnetic resonance image data set (146) descriptive of a first region of interest (122) of a subject (118) and receive (202) at least one second magnetic resonance image data set (152, 152?) descriptive of a second region of interest (124) of the subject. The first region of interest at least partially comprises the second region of interest. Execution of the machine executable instructions further cause the processor to receive (204) an analysis region (126) within both the first region of interest and within the second region of interest.

Abstract: A cortical malformation identification method includes quantitatively evaluating, using a processor of a computer that includes the processor and a memory, digital image data from a magnetic resonance imaging (MRI) scan on a cerebral cortex to produce quantified scan data. The method also includes automatically detecting a cortical malformation based on the quantified scan data. An image of the cerebral cortex may be color-coded so that the cortical malformation is shown in a different color than the remainder of the cerebral cortex in the image, based on the quantified scan data. Additionally or alternatively, a 3-dimensional representation of the cerebral cortex may be mapped to the quantified scan data to produce a mapped image of the cerebral cortex including the detected cortical malformation.