Abstract: Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and multiple neural networks. The multiple neural networks comprise a noise estimation neural network (122) and at least one image quantification neural network (124). The noise estimation neural network is configured to output a noise estimate (128) of a medical image (126, 126?) in response to receiving the medical image as input. The at least one image quantification neural network is configured to output an image attribute (130) of the medical image in response to receiving the medical image as input. The medical system further comprises a computational system (104).

Abstract: An imaging system (IS), comprising an image acquisition unit (AQ) for acquisition of image data (I1) of an object (OB). The image acquisition is based on an imaging signal imitable by the unit (AQ) to interact with the object. The image acquisition unit (AQ) is adjustable to operate at different acquisition parameters that determine a property of the imaging signal. A predictor component (PC) predicts, based at least on the acquired image data (I1), one or more properties of the object. An acquisition parameter adjuster (PA) adjusts, based on the predicted object properties, the acquisition parameter at which the image acquisition unit (AQ) is to acquire follow-up image data (I2).

Abstract: A method and apparatus for analyzing diagnostic image data are provided in which correspondence detection between a first diagnostic image and a second diagnostic image of a vessel of interest in a patients vasculature is performed on the basis of at least one functional parameter by matching the one or more values of said functional parameter at particular positions along the vessel of interest as shown in the first diagnostic image and the second diagnostic image to one another, thereby determining a correlation between the positions in the basis of said functional parameter values rather than solely the vessel geometry.

Abstract: In the technical field of the preparation of alicyclic compounds, a ring hydrogenation of aromatic compounds prepares alicyclic compounds in a process for preparing alicyclic compounds. The alicyclic compounds may preferably be alicyclic carboxylic acids and esters thereof. An apparatus may be provided for carrying out this process.

Abstract: In the technical field of the preparation of alicyclic compounds, a ring hydrogenation of aromatic compounds prepares alicyclic compounds in a process for preparing alicyclic compounds. The alicyclic compounds may preferably be alicyclic carboxylic acids and esters thereof. An apparatus for carrying out this process may further be provided.

Abstract: A two-stage process produces tetraalkyl 1,2,3,4-butanetetracarboxylates having alkyl groups with 1 to 6 carbon atoms starting from tetrahydrophthalic anhydride (THPA). The process starts with an oxidation of tetrahydrophthalic anhydride (THPA), followed by a subsequent esterification of the resulting 1,2,3,4-butanetetracarboxylic acid with an alcohol having 1 to 6 carbon atoms.

Abstract: An electrochemical process produces tetraalkyl 1,2,3,4-butanetetracarboxylates having alkyl groups with 1 to 6 carbon atoms. The process employs an electrohydrodimerization of dialkyl maleates having alkyl groups having 1 to 6 carbon atoms in a reactant solution with an alcohol and a conducting salt.

Abstract: In a conventional system for preparing reports on findings in medical images, the actual formulation of the report is not or only insufficiently supported by the computer-based system. Although there are efforts to improve such a system through automatic reporting, however, in previous systems, the error rate is too high and/or the operation of the system too complicated. This application proposes to provide text prediction to a user on the display device. The text prediction is based on prior analyzing the image content of a medical image at is displayed to the user at least when the user activates a text field shown on the display device via the input unit. The displayed text prediction is selected from a pre-defined set of text modules that are associated to the analysis result.

Abstract: The invention refers to an apparatus that allows to improve the image quality of nuclear images, e.g. PET images. The apparatus (110) comprises a providing unit (111) for providing nuclear image data of a region of interest, a providing unit (112) for providing a motion signal indicative of a motion of the region of interest, a determination unit (113) for determining different motion states of the region of interest based on the motion signal, a determination unit (114) for determining for each motion state nuclear image data corresponding to the motion state, a reconstruction unit (115) for reconstructing an absorption map for each motion state based on the corresponding nuclear image data of the respective motion state, and a reconstruction unit (116) for reconstructing one or more nuclear images of the region of interest based on the nuclear image data and the absorption maps reconstructed for each motion state.

Abstract: A computer-implemented method of calculating a value of a contrast agent attenuation gradient for a lumen in a vasculature, is provided. The method includes: analyzing spectral CT data to isolate from the spectral CT data, contrast agent attenuation data representing the distribution of the contrast agent along the lumen; and calculating, from the contrast agent attenuation data, a value of a gradient of the contrast agent along one or more portions (Pa-Pd) of the lumen, to provide the value of the contrast agent attenuation gradient.

Abstract: A method for performing motion compensation in spectral CT imaging, to compensate for motion of at least one structural feature over a time period for which the scan is executed. The method comprises receiving or generating reconstructed image data for a plurality of spectral or material basis components the spectral CT imaging data and generating for each spectral or material component at least one motion vector field corresponding to detected motion of the feature of interest over at least a portion of the imaging time period. Motion compensation is applied using a final motion vector field either selected or constructed from the plurality of motion vector fields.

Abstract: A ring hydrogenation of aromatic compounds prepares alicyclic compounds in a process for preparing alicyclic compounds using a static mixer, and also apparatuses for carrying out this process.

Abstract: : A computer-implemented method of providing normalised medical images (110?1 . . . n) representing a region of interest (120) in a subject, is provided. The method includes: warping medical images (1101 . . . n) in a temporal series to a 2D atlas image (130), to provide normalised medical images (110?1 . . . n) having a warped region of interest (120?1 . . . n) for comparison with the region of interest (120) in the atlas image (130). The method also includes outputting the normalised medical images (110?1 . . . n), and/or outputting a magnitude of a change in the warped region of interest (120?1 . . . n) between the normalised medical images (110?1 . . . n).

Abstract: A computer-implemented method of measuring a blood flow parameter in a vasculature, is provided. The method includes: analyzing spectral CT projection data to isolate from the spectral CT projection data, contrast agent projection data representing the flow of the injected contrast agent; sampling the contrast agent projection data at one or more regions of interest in the vasculature to provide temporal blood flow data at the one or more regions of interest; and calculating, from the temporal blood flow data, a value of one or more blood flow parameters at the one or more regions of interest.

Abstract: The present invention relates to an apparatus (10) for correcting computer tomography (“CT”) X-ray data acquired at high relative pitch, the apparatus comprising: an input unit (20); a processing unit (30); and an output unit (40). The input unit is configured to provide the processing unit with CT X-ray data of a body part of a person acquired at high relative pitch. The processing unit is configured to determine CT slice reconstruction data of the body part of the person with no or reduced high relative pitch operation reconstruction artefacts using a machine learning algorithm. The machine learning algorithm was trained on the basis of CT slice reconstruction data, and wherein the CT slice reconstruction data comprised first CT slice reconstruction data with high relative pitch reconstruction artefacts and comprised second CT slice reconstruction data with less, less severe, or no high relative pitch reconstruction artefacts.

Abstract: A mechanism for generating denoised basis images for a computed tomography scanner. An input dataset, comprising first and second basis image data, is processed using a machine-learning algorithm process to produce the denoised basis images. The first and second basis image data each comprise at least one basis image, wherein the type of image differs between the first and second basis image data.

Abstract: A method is provided for processing images comprising retrieving measured data for a first image. The method then generates partially filtered data by applying a first filter to the measured data. The first filter is a generic filter. The method then reconstructs the partially filtered data to generate a partially filtered image. The method then generates a partially processed image by applying a first processing routine to the partially filtered image. The method then generates a filtered image by applying a second filter to the partially processed image, where the second filter is a filter selected from a plurality of potential secondary filters. The method then outputs the filtered image. Systems are provided for implementing the claimed method and training methods for neural networks used in the method are provided as well.

Abstract: The present invention relates to a spectral X-ray CT imaging system (10), comprising: a spectral X-ray CT imaging unit (20); a processing unit (30); and an output unit (40). The spectral X-ray CT imaging unit comprises an X-ray tube (22) and a dual layer X-ray detector (24), and wherein a body portion of a subject to be examiner can be located between the X-ray tube and the dual layer X-ray detector. The spectral X-ray CT imaging unit is configured to acquire an overall scan of the body portion comprising a plurality of acquisitions at different projections angles. The processing unit is configured to utilize information on the body portion for each projection of the different projections angles. The processing unit is configured to determine a voltage for the X-ray tube for each acquisition of the plurality of acquisitions, wherein the determination for each acquisition comprises utilization of the corresponding information on the body portion for the projection associated with that acquisition.

Abstract: A mechanism for generating denoised basis projection data. Low-energy projection data and high-energy projection data is processed using a neural network that is trained to perform the dual task of decomposition and denoising. The neural network thereby directly outputs basis projection data, taking the place of existing decomposition and denoising techniques.

Abstract: All X-ray computerized tomography systems that are available or proposed base their reconstructions on measurements that integrate over energy. X-ray tubes produce a broad spectrum of photon energies and a great deal of information can be derived by measuring changes in the transmitted spectrum. We show that for any material, complete energy spectral information may be summarized by a few constants which are independent of energy. A technique is presented which uses simple, low-resolution, energy spectrum measurements and conventional computerized tomography techniques to calculate these constants at every point within a cross-section of an object. For comparable accuracy, patient dose is shown to be approximately the same as that produced by conventional systems. Possible uses of energy spectral information for diagnosis are presented.