Abstract: A computer-implemented method for a medical imaging device is disclosed. In an embodiment, the computer-implemented method includes receiving at least one item of user information describing a desired visualization format of the image dataset; providing at least one item of request information; describing required input data of at least one evaluation algorithm to be used; determining at least one first processing dataset corresponding to the visualization format according to the user information and at least one second processing dataset usable as input data for the respective evaluation algorithm according to the request information; applying the at least one evaluation algorithm to the respective second processing dataset to determine evaluation information and outputting the first processing dataset and the evaluation information.

Abstract: A computer-implemented method for evaluating an image data set of an imaged region comprises: determining, from the image data set, at least two processed data sets having different image data content; applying a first sub-algorithm, of an evaluation algorithm, to a first of at least two processed data sets to determine a first intermediate result relating to image data content of the first of the at least two processed data sets; applying a second sub-algorithm, of the evaluation algorithm, to a second of the at least two processed data sets to determine a second intermediate result relating to image data content of the second of the at least two processed data sets; determining quantitative evaluation result data by a third sub-algorithm of the evaluation algorithm, wherein the third sub-algorithm uses both the first intermediate result and the second intermediate result as input data.

Abstract: A computer-implemented method for a medical imaging device is disclosed. In an embodiment, the computer-implemented method includes receiving at least one item of user information describing a desired visualization format of the image dataset; providing at least one item of request information; describing required input data of at least one evaluation algorithm to be used; determining at least one first processing dataset corresponding to the visualization format according to the user information and at least one second processing dataset usable as input data for the respective evaluation algorithm according to the request information; applying the at least one evaluation algorithm to the respective second processing dataset to determine evaluation information and outputting the first processing dataset and the evaluation information.

Abstract: In one embodiment, a computer-implemented method is for providing lymph node information. The method includes receiving medical imaging data; receiving atlas data spatially relating lymph node stations to at least one non-lymphatic anatomical structure; determining a lymph node position in the medical imaging data; generating the lymph node information, the lymph node information being indicative of a lymph node station, to which the lymph node position is anatomically allocated, by applying an algorithm onto the medical imaging data, the atlas data and the lymph node position; and providing the lymph node information.

Abstract: An embodiment of the invention relates to a scanning device. The scanning device includes a scanning unit to detect radiation received during a scanning operation on an object. An imaging unit is arranged to reconstruct an image for a location on the object based on the detected radiation. A texture analysis unit receives an indicated area of interest of a medical image and computes at least one texture metric for the area of interest. An image comparison unit receives a plurality of texture metrics for a common area of interest within respective medical images and outputs a change metric indicating a measure of variation over time for the area of interest based on a comparison of the plurality of texture metrics.

Abstract: In one embodiment, a computer-implemented method is for providing lymph node information. The method includes receiving medical imaging data; receiving atlas data spatially relating lymph node stations to at least one non-lymphatic anatomical structure; determining a lymph node position in the medical imaging data; generating the lymph node information, the lymph node information being indicative of a lymph node station, to which the lymph node position is anatomically allocated, by applying an algorithm onto the medical imaging data, the atlas data and the lymph node position; and providing the lymph node information.

Abstract: An embodiment of the invention relates to a scanning device. The scanning device includes a scanning unit to detect radiation received during a scanning operation on an object. An imaging unit is arranged to reconstruct an image for a location on the object based on the detected radiation. A texture analysis unit receives an indicated area of interest of a medical image and computes at least one texture metric for the area of interest. An image comparison unit receives a plurality of texture metrics for a common area of interest within respective medical images and outputs a change metric indicating a measure of variation over time for the area of interest based on a comparison of the plurality of texture metrics.

Abstract: A method includes receiving an image data set of an examination volume including a tumor. A tumor region is segmented in the image data set. Furthermore, a texture distribution of the tumor region is determined, the texture distribution in each case assigning a texture parameter to image points in the tumor region, and each of the texture parameters being based on a spatial distribution of first intensity values of the image data set. Furthermore, a biopsy position within the tumor region is determined by applying an optimized position-determining algorithm to the texture distribution, the optimized position-determining algorithm being based on training texture distributions and first training positions, and one of the first training positions being assigned to each of the training texture distributions. In embodiments, the use of an optimized position-determining algorithm enables all relevant influencing variables for the determination of the biopsy position to be taken into account.

Abstract: A method includes receiving an image data set of an examination volume including a tumor. A tumor region is segmented in the image data set. Furthermore, a texture distribution of the tumor region is determined, the texture distribution in each case assigning a texture parameter to image points in the tumor region, and each of the texture parameters being based on a spatial distribution of first intensity values of the image data set. Furthermore, a biopsy position within the tumor region is determined by applying an optimized position-determining algorithm to the texture distribution, the optimized position-determining algorithm being based on training texture distributions and first training positions, and one of the first training positions being assigned to each of the training texture distributions. In embodiments, the use of an optimized position-determining algorithm enables all relevant influencing variables for the determination of the biopsy position to be taken into account.