Abstract: A method for image processing of mammography image data from a mammography system, comprises: acquiring mammography image data of a breast of a patient; and determining corrected mammography image data based on the mammography image data, wherein the corrected mammography image data is modified to at least partially compensate for the influence of the cutout on the representation of the breast in the mammography image data.

Abstract: For denoising medical imaging data, a first imaging dataset and a second imaging dataset are decomposed according to spatial frequency bands to generate high-frequency datasets corresponding to a high-frequency band and low-frequency datasets corresponding to a low-frequency band. A trainable denoising algorithm is trained by carrying out an optimization that uses at least one parameter of the denoising algorithm as an optimization variable and an objective function that depends on a denoised high-frequency dataset and the high-frequency dataset of the second imaging dataset. The denoised high-frequency dataset is generated by applying the denoising algorithm to the high-frequency dataset of the first imaging dataset. The trained denoising algorithm is applied to the high-frequency dataset of the first imaging dataset to generate a final denoised high-frequency dataset.

Abstract: In a computer-implemented method for parameterizing an imaging system for mapping a clothed person, image data about the clothed person is obtained and body-shape information about the person is determined by applying a trained machine-learning model to the image data. At least one imaging parameter of the imaging system is determined as a function of the body-shape information.

Abstract: A fundamental machine learning model, fMLM, is provisioned in the untrained or in a partially trained state to provide a trained machine learning model for feature extraction, xMLM, from medical data, wherein the fMLM has an architecture that is trainable by way of unsupervised or self-supervised training. The fMLM has the xMLM and at least one downstream machine learning model, nMLM, for performing at least one corresponding downstream task. First medical data is obtained and the fMLM is trained in an unsupervised or a self-supervised manner based on the first medical data. The xMLM is taken from trained fMLM and stored.

Abstract: A method for denoising a tomography recording with a plurality of projection images includes: selecting an action projection image to be denoised; selecting a plurality of reference projection images having recording angles that lie in a range of the recording angle of the action projection image and/or an opposite recording angle; adapting a binning of the reference projection images to the action projection image so that the reference projection images correspond to the projection geometry of the action projection image; and denoising the action projection image based on a noise of the reference projection images.

Abstract: A method and a system for magnetic resonance imaging are provided. The method includes acquiring a magnetic resonance (MR) data set of an object by sampling only a portion of a k-space of the object. This sampled portion may be substantially triangle-shaped or be composed of multiple planes that extend through a point of origin of the k-space and are tangential to a common spherical cap in the k-space. An inverse Fourier transformation is then applied to the MR data set to transform the MR data set from the k-space to image space. A final MR image with a fan- or cone-beam geometry is then computed based on the transformed MR data set in image space.

Abstract: Methods for performing digital subtraction angiography of a region of interest of a patient are described herein. The methods include acquiring a filled image data set of the region of interest by x-ray imaging and creating an angiography image data set by subtracting a mask image data set from the filled image data set, wherein an x-ray imaging device for x-ray imaging and a further imaging device for at least one additional imaging modality are co-registered and operable to acquire image data in the same field of view, wherein the imaging devices are used to simultaneously acquire the filled image data set using the x-ray imaging device and an anatomy data set using the further imaging device and the mask image data set in derived from the anatomy data set in a conversion process, which converts additional imaging modality image data into virtual x-ray image data.

Abstract: A method and a system for magnetic resonance imaging are provided. The method includes acquiring a magnetic resonance (MR) data set of an object by sampling only a portion of a k-space of the object. This sampled portion may be substantially triangle-shaped or be composed of multiple planes that extend through a point of origin of the k-space and are tangential to a common spherical cap in the k-space. An inverse Fourier transformation is then applied to the MR data set to transform the MR data set from the k-space to image space. A final MR image with a fan- or cone-beam geometry is then computed based on the transformed MR data set in image space.

Abstract: Methods for performing digital subtraction angiography of a region of interest of a patient are described herein. The methods include acquiring a filled image data set of the region of interest by x-ray imaging and creating an angiography image data set by subtracting a mask image data set from the filled image data set, wherein an x-ray imaging device for x-ray imaging and a further imaging device for at least one additional imaging modality are co-registered and operable to acquire image data in the same field of view, wherein the imaging devices are used to simultaneously acquire the filled image data set using the x-ray imaging device and an anatomy data set using the further imaging device and the mask image data set in derived from the anatomy data set in a conversion process, which converts additional imaging modality image data into virtual x-ray image data.