Abstract: To improve execution of image-based tasks, such as obtaining an x-ray image with a desired contrast while using a minimum x-ray dosage, a method in which one or more acquisition parameters of an image acquisition apparatus are automatically set as a function of the task is provided. An image of a biological tissue is acquired using the image acquisition apparatus for which one or more acquisition parameters have been automatically set. The task is executed based on the image of the biological tissue.

Abstract: A computer-implemented method for provision of a correction algorithm for an x-ray image that was recorded with an x-ray source emitting an x-ray radiation field, a filter facility spatially modulating an x-ray radiation dose, and an x-ray detector is provided. The correction algorithm includes a trained first processing function that, from first input data that includes at least one first physical parameter describing the x-ray radiation field and/or the measurement and at least one second physical parameter describing the spatial modulation of the filter facility, determines first output data. The first output data includes a mask for brightness compensation with regard to the spatial modulation of the filter facility in the x-ray image. The method includes providing first training data, providing an autoencoder for masks, and training of the autoencoder using the first training data. The method also includes determining an assignment rule, and providing the trained first processing function.

Abstract: Some embodiments relate to solutions to providing a result image data set. At least one embodiment is based on an input image data set of a first examination volume being received. A result image parameter is received or determined. A result image data set of the first examination volume is determined by application of a trained generator function to input data. Input data is based on the input image data set and the result image parameter, and the result image parameter relates to a property of the result image data set. A parameter of the trained generator function is based on a GA algorithm (acronym for the English technical term “generative adversarial”). Finally, the result image data set is provided. Some embodiments relate to solutions for providing a trained generator function and/or a trained classifier function, in particular for use in solutions for providing a result image data set.

Abstract: A computer-implemented method for provision of a correction algorithm for an x-ray image that was recorded with an x-ray source emitting an x-ray radiation field, a filter facility spatially modulating an x-ray radiation dose, and an x-ray detector is provided. The correction algorithm includes a trained first processing function that, from first input data that includes at least one first physical parameter describing the x-ray radiation field and/or the measurement and at least one second physical parameter describing the spatial modulation of the filter facility, determines first output data. The first output data includes a mask for brightness compensation with regard to the spatial modulation of the filter facility in the x-ray image. The method includes providing first training data, providing an autoencoder for masks, and training of the autoencoder using the first training data. The method also includes determining an assignment rule, and providing the trained first processing function.

Abstract: A method is for providing a resultant image dataset for a volume of interest. In an embodiment, an X-ray image dataset for the volume of interest is received, having a first noise level. A trained generator function is received, a parameter of which is based on a first and a second training image dataset for a training volume of interest. The first and the second training image dataset include a first training noise level. In addition, a resultant image dataset for the volume of interest is determined by applying the trained generator function to input data comprising the X-ray image dataset, the resultant image dataset has a second noise level less than the first noise level. In addition, the resultant image dataset is provided. As such, a higher noise level can be accepted and/or a lower X-ray dose can be used in the acquisition of the X-ray image dataset.

Abstract: A method for noise reduction in a low-dose X-ray image includes preprocessing for determining input data, at least one trained function for determining noise-reduced output data from the input data, and postprocessing for determining a result image from the output data. At least one result parameter specifying at least one desired result attribute of the result image is received or determined. The at least one result attribute is obtained by modifying the preprocessing to set a noise value of at least one first noise parameter. The noise value is determined from the result parameter. The noise value may be selected to differ from a reference value of the first noise parameter. Alternatively or additionally, the at least one result attribute is obtained by setting, according to the result parameter, the at least one trained function to one of a plurality of predefined noise values of at least one second noise parameter.

Abstract: An object of this disclosure is to allow dose-specific alteration of the noise suppression in X-ray images, wherein the image quality may change only below the threshold of perceptibility. To achieve this, a method is provided for adjusting a parameter for noise suppression in X-ray imaging, in which quality functions are determined in relation to a variation of a noise suppression parameter. Maximum values of the variation are set for each quality function based on a threshold value. The variability of the noise suppression parameter may be limited according to the X-ray dose up to the set maximum value.

Abstract: In an embodiment, a first real image dataset of an examination volume is received. The examination volume includes a vessel here, and the first real image dataset maps the examination volume includes contrast medium. Furthermore a differential image dataset of the examination volume is determined by application of a first trained generator function to input data. Here the input data includes the first real image dataset and a parameter of the trained generator function based on a GA algorithm. Furthermore the differential image dataset is provided.

Abstract: A method is for providing a resultant image dataset for a volume of interest. In an embodiment, an X-ray image dataset for the volume of interest is received, having a first noise level. A trained generator function is received, a parameter of which is based on a first and a second training image dataset for a training volume of interest. The first and the second training image dataset include a first training noise level. In addition, a resultant image dataset for the volume of interest is determined by applying the trained generator function to input data comprising the X-ray image dataset, the resultant image dataset has a second noise level less than the first noise level. In addition, the resultant image dataset is provided. As such, a higher noise level can be accepted and/or a lower X-ray dose can be used in the acquisition of the X-ray image dataset.

Abstract: In an embodiment, a first real image dataset of an examination volume is received. The examination volume includes a vessel here, and the first real image dataset maps the examination volume includes contrast medium. Furthermore a differential image dataset of the examination volume is determined by application of a first trained generator function to input data. Here the input data includes the first real image dataset and a parameter of the trained generator function based on a GA algorithm. Furthermore the differential image dataset is provided.

Abstract: Some embodiments relate to solutions to providing a result image data set. At least one embodiment is based on an input image data set of a first examination volume being received. A result image parameter is received or determined. A result image data set of the first examination volume is determined by application of a trained generator function to input data. Input data is based on the input image data set and the result image parameter, and the result image parameter relates to a property of the result image data set. A parameter of the trained generator function is based on a GA algorithm (acronym for the English technical term “generative adversarial”). Finally, the result image data set is provided. Some embodiments relate to solutions for providing a trained generator function and/or a trained classifier function, in particular for use in solutions for providing a result image data set.

Abstract: A method for processing at least one X-ray image is provided. A variance of noise is signal dependent. The method includes applying a variance-stabilizing transformation to image data of the at least X-ray image to generate variance-stabilized data. At least one transform parameter of the variance-stabilizing transformation is dependent on a property of the at least one X-ray image that depends on an X-ray imaging device and/or a measurement parameter used to record the at least one X-ray image. A noise reduction algorithm is applied to the variance-stabilized data to generate noise-reduced data, and an inverse transformation of the variance stabilizing transformation is applied to the noise-reduced data to generate a denoised X-ray image.

Abstract: A method for processing at least one X-ray image is provided. A variance of noise is signal dependent. The method includes applying a variance-stabilizing transformation to image data of the at least X-ray image to generate variance-stabilized data. At least one transform parameter of the variance-stabilizing transformation is dependent on a property of the at least one X-ray image that depends on an X-ray imaging device and/or a measurement parameter used to record the at least one X-ray image. A noise reduction algorithm is applied to the variance-stabilized data to generate noise-reduced data, and an inverse transformation of the variance stabilizing transformation is applied to the noise-reduced data to generate a denoised X-ray image.