Abstract: A periodic time-varying model of a guide wire, other device, or vessel is created from higher contrast CINE acquisition. A multivariate curve regression is used for periodic, phase dependent motion modeling. This model is then used to locate or track the guide wire, other device or vessel in lower contrast fluoroscopy images during intervention.

Abstract: An operating method assists a diagnosing practitioner with describing the location of a target structure in a tomosynthesis image data record of a compressed breast of a patient. The target structure is localized by a first spatial information item. The method includes: a) ascertaining a first shape information item describing at least one first, compressed breast shape in the tomosynthesis image data record, b) determining a second shape information item describing the breast in a non-compressed breast shape, from the first shape information item, c) mapping the position of the target structure from the compressed breast shape and to the non-compressed breast shape using at least the second shape information item for ascertaining a second spatial information item relating to the non-compressed breast shape, and d) transforming the second spatial information item into a pictogram information item facilitating an abstracted pictorial representation and/or describing the latter.

Abstract: A pair of medical images is analyzed, the pair including a first image, which is a contrasted scan of a part in a human or animal body, and a second image, which is a native scan of the same part of the human or animal body. Anatomic structures are identified within both the first image and the second image. By using those anatomic structures, centerlines of vessels in the first image are mapped to the second image. Candidate calcified plaques are extracted in the second image, and calcified plaques out of the candidate calcified plaques are identified by a machine learning classifier. The positional information of the centerlines in the second image is used for extracting the candidate calcified plaques in the second image and/or for identifying the calcified plaques out of the candidate calcified plaques by the machine learning classifier.

Abstract: A method of assigning first localization data of a breast of a patient derived from first image data of the breast, the first image data being the result of a first radiological data acquisition process, to second localization data of the same breast derived from second image data, the second image data being the result of a second radiological data acquisition process, or vice versa. Thereby, the first localization data are assigned to the second localization data by intermediately mapping them into breast model data representing a patient-specific breast shape of the patient and then onto the second image data—or vice versa, thereby deriving assignment data. An assignment system performs the above-described method.

Abstract: A method image scans a female breast in the context of projective digital mammography or tomosynthesis with prefiltered x-ray radiation. From a single scanning two spatially superimposing images are created on the basis of pixel-by-pixel or pixel-group-by-pixel-group different x-ray energy spectra. An x-ray mammography system contains a radiator-detector system having an x-ray tube with a filter for generating a prefiltered x-ray radiation having a first radiation spectrum and a radiation detector containing a multiplicity of partial areas which generate individual image pixels. The radiator-detector system is configured to the effect that the partial areas receive pixel-by-pixel or pixel-group-by-pixel-group different radiation spectra, such that the two images from different x-ray spectra are received with a single scanning of the female breast.

Abstract: A method for generating a projection image from tomographic images first captures (S101) a number of stacked two-dimensional tomographic images representing a tomographic volume. The pixels of the stacked two-dimensional tomographic images are weighted (S102) along a number of projection beams through the tomographic volume with weighting factors that are chosen under the constraint that the sum of all squared weighting factors for each individual projection beam is between a given lower limit and a given upper limit. Then the weighted pixels are summed up (S103) along the number of projection beams for generating the two-dimensional projection image.

Abstract: A method and system for the diagnosis of 3D images are disclosed, which significantly cuts the time required for the diagnosis. The 3D images are for example an image volume dataset of a magnetic resonance tomography system which is saved in an RIS or PACS system. In at least one embodiment, the diagnostic finding are partially automatically generated, and details of the position, size and change in pathological structures are compared to previous diagnostic findings are generated automatically. As a result of this automation the diagnostic work of radiologists is significantly reduced.

Abstract: A method for generating a synthetic two-dimensional mammogram with enhanced contrast for structures of interest includes acquiring a three-dimensional digital breast tomosynthesis volume having a plurality of voxels. A three-dimensional relevance map that encodes for the voxels the relevance of the underlying structure for a diagnosis is generated. A synthetic two-dimensional mammogram is calculated based on the three-dimensional digital breast tomosynthesis volume and the three-dimensional relevance map.

Abstract: A method is disclosed for segmentation of a calcified blood vessel in image data. An embodiment of the method includes providing a vesseltree representation of the blood vessel; providing a number of preliminary boundary representations of a number of cross-sections of the blood vessel; providing a number of intensity profiles in the image data in the number of cross-sections; determining a calcification in the cross-section based on the intensity profile; and correcting each preliminary boundary representation into a corrected boundary representation which excludes the calcification from an inner part of the blood vessel. A segmentation system is also disclosed.

Abstract: A method and a segmentation system are disclosed. An embodiment of the method includes providing an image representation of the structure; providing a start surface model, including a mesh with a plurality of vertices connected by edges; defining for each vertex a ray normal to the surface model at the position of the vertex; assigning more than two labels to each vertex, each label representing a candidate position of the vertex on the ray; providing a representation of likelihoods for each candidate position the likelihood referring to whether the candidate position corresponds to a surface point of the structure in the image representation; and defining a first order Markow Random Field with discrete multivariate random variables, the random variables including the labels of the candidate positions and the representation of likelihoods, finding an optimal segmentation of the structure by using an maximum a posteriori estimation in this Markow Random Field.

Abstract: An embodiment of the method is disclosed for non-invasive lesion candidate detection in a patient's body includes generating a number of first medical images of the patient's body. The method further includes identifying lesion-like geometrical regions inside the first medical images of the patient's body by applying image processing methods, whereby the identification is at least partly controlled by a number of patient-specific context features which are not directly extractable from the first medical images. In addition, the method includes selecting a number of the identified lesion-like geometrical regions as lesion candidates.

Abstract: A method for generating a combined projection image from a medical inspection object, includes steps of capturing a set of initial projection images; reconstructing a first and a second three-dimensional volume from the set of initial projection images; generating a first re-projection image from the first three-dimensional volume and a second re-projection image from the second three-dimensional volume; weighting the first re-projection image and the second re-projection image; and combining the weighted first re-projection image and second re-projection image for generating the combined projection image.

Abstract: A method for generating a projection image from tomographic images first captures (S101) a number of stacked two-dimensional tomographic images representing a tomographic volume. The pixels of the stacked two-dimensional tomographic images are weighted (S102) along a number of projection beams through the tomographic volume with weighting factors that are chosen under the constraint that the sum of all squared weighting factors for each individual projection beam is between a given lower limit and a given upper limit. Then the weighted pixels are summed up (S103) along the number of projection beams for generating the two-dimensional projection image.

Abstract: A pair of medical images is analysed, the pair including a first image, which is a contrasted scan of a part in a human or animal body, and a second image, which is a native scan of the same part of the human or animal body. Anatomic structures are identified within both the first image and the second image. By using those anatomic structures, centerlines of vessels in the first image are mapped to the second image. Candidate calcified plaques are extracted in the second image, and calcified plaques out of the candidate calcified plaques are identified by a machine learning classifier. The positional information of the centerlines in the second image is used for extracting the candidate calcified plaques in the second image and/or for identifying the calcified plaques out of the candidate calcified plaques by the machine learning classifier.

Abstract: A second form of image data is determined from a first form of image data of an examination object in a radiological imaging system. A set of a defined plurality of input pixels in the image data of the first form is determined. In addition, a set of target form parameters of a target form model with a defined plurality of target form parameters is prognostically determined by way of a data-driven regression method from the plurality of input pixels. The number of target form parameters is smaller than the number of input pixels. The second form of image data is determined from the set of target form parameters. There is also described a method in radiological imaging for determining the geometric position of a number of target objects in a second form of image data and an image processing workstation for determining a second form of image data from a first form of image data as well as an imaging device.

Abstract: A periodic time-varying model of a guide wire, other device, or vessel is created from higher contrast CINE acquisition. A multivariate curve regression is used for periodic, phase dependent motion modeling. This model is then used to locate or track the guide wire, other device or vessel in lower contrast fluoroscopy images during intervention.

Abstract: A method and system for on-line learning of landmark detection models for end-user specific diagnostic image reading is disclosed. A selection of a landmark to be detected in a 3D medical image is received. A current landmark detection result for the selected landmark in the 3D medical image is determined by automatically detecting the selected landmark in the 3D medical image using a stored landmark detection model corresponding to the selected landmark or by receiving a manual annotation of the selected landmark in the 3D medical image. The stored landmark detection model corresponding to the selected landmark is then updated based on the current landmark detection result for the selected landmark in the 3D medical image. The landmark selected in the 3D medical image can be a set of landmarks defining a custom view of the 3D medical image.

Abstract: A method and apparatus for automatic detection and labeling of 3D spinal geometry is disclosed. Cervical, thoracic, and lumbar spine regions are detected in a 3D image. Intervertebral disk candidates are detected in each of the spine regions using iterative marginal space learning (MSL). Using a global probabilistic spine model, a separate one of the intervertebral disk candidates is selected for each of a plurality of labeled intervertebral disk locations.

Abstract: A method and system for fully automatic segmentation the prostate in multi-spectral 3D magnetic resonance (MR) image data having one or more scalar intensity values per voxel is disclosed. After intensity standardization of multi-spectral 3D MR image data, a prostate boundary is detected in the multi-spectral 3D MR image data using marginal space learning (MSL). The detected prostate boundary is refined using one or more trained boundary detectors. The detected prostate boundary can be split into patches corresponding to anatomical regions of the prostate and the detected prostate boundary can be refined using trained boundary detectors corresponding to the patches.

Abstract: A method is disclosed for segmentation of a calcified blood vessel in image data. An embodiment of the method includes providing a vesseltree representation of the blood vessel; providing a number of preliminary boundary representations of a number of cross-sections of the blood vessel; providing a number of intensity profiles in the image data in the number of cross-sections; determining a calcification in the cross-section based on the intensity profile; and correcting each preliminary boundary representation into a corrected boundary representation which excludes the calcification from an inner part of the blood vessel. A segmentation system is also disclosed.