Abstract: Disclosed herein are compound or pharmaceutically acceptable salts or stereoisomers thereof of formula I: X1 is linear or branched C1-6 alkyl, C3-8 cycloalkyl, C6-10 aryl, 5-10 membered heteroaryl, 4-8 membered heterocycloalkyl, wherein X1 is unsubstituted or substituted with one or more of halogen, linear or branched C1-6 alkyl, linear or branched C1-6 heteroalkyl, CF3, CHF2, CMeF2, —O—CHF2, —O—(CH2)2—OMe, OCF3, C1-6 alkylamino, —CN, —N(H)C(O)—C1-6alkyl, —OC(O)—C1-6alkyl, —OC(O)—C1-4alkylamino, —C(O)O—C1-6alkyl, —COOH, —C1-6alkylC(O)OH, —C1-6alkylC(O)O—C1-6alkyl, NH2, C1-4 alkoxy, C1-4 alkylhydroxy, —CH2F, —N(H)C(O)—O—C1-6 alkyl, and C(OH)(CF3); or X1 together with the N atom of the carbamate forms a 4-8 membered heterocycloalkyl, which is unsubstituted or substituted with one or more of halogen, linear or branched —C1-6 alkyl, CF3, CHF2, CMeF2, —O—(CH2)2—OMe, OCF3, OCHF2, C1-6 alkylamino, —CN, —N(H)C(O)—C1-6alkyl, —OC(O)—C1-6alkyl, —C(O)O—C1-6alkyl, —COOH, —C1-6alkylC(O)OH, —C1-6alkylC(O)O—C1-6alkyl, NH

Abstract: In one embodiment, a method is for calculating an examination parameter for a computed tomography examination of an area of interest of a patient. The method includes receiving a topogram of the area of interest of the patient; determining fat distribution information by applying a trained machine learning algorithm onto the topogram; and calculating the examination parameter for the computed tomography examination of the area of interest of the patient based on the fat distribution information.

Abstract: Disclosed herein are compound or pharmaceutically acceptable salts or stereoisomers thereof of formula I: X1 is linear or branched C1-6 alkyl, C3-8 cycloalkyl, C6-10 aryl, 5-10 membered heteroaryl, 4-8 membered heterocycloalkyl, wherein X1 is unsubstituted or substituted with one or more of halogen, linear or branched C1-6 alkyl, linear or branched C1-6 heteroalkyl, CF3, CHF2, CMeF2, —O—CHF2, —O—(CH2)2—OMe, OCF3, C1-6 alkylamino, —CN, —N(H)C(O)—C1-6alkyl, —OC(O)—C1-6alkyl, —OC(O)—C1-4alkylamino, —C(O)O—C1-6alkyl, —COOH, —C1-6alkylC(O)OH, —C1-6alkylC(O)O—C1-6alkyl, NH2, C1-4 alkoxy, C1-4 alkylhydroxy, —CH2F, —N(H)C(O)—O—C1-6 alkyl, and C(OH)(CF3); or X1 together with the N atom of the carbamate forms a 4-8 membered heterocycloalkyl, which is unsubstituted or substituted with one or more of halogen, linear or branched —C1-6 alkyl, CF3, CHF2, CMeF2, —O—(CH2)2—OMe, OCF3, OCHF2, C1-6 alkylamino, —CN, —N(H)C(O)—C1-6alkyl, —OC(O)—C1-6alkyl, —C(O)O—C1-6alkyl, —COOH, —C1-6alkylC(O)OH, —C1-6alkylC(O)O—C1-6alkyl, NH

Abstract: A method is disclosed for generating an image. An embodiment of the method includes detecting a first projection data set via a first group of detector units, the first group including a first plurality of first detector units, each having more than a given number of detector elements; detecting a second projection data set via a second group of detector units, the second group including a second plurality of second detector units, each including, at most, the given number of detector elements; reconstructing first image data based on the first projection data set; reconstructing second image data based on the second projection data set; and combining the first image data and the second image data. A non-transitory computer readable medium, a data processing unit, and an imaging device including the data processing unit are also disclosed.

Abstract: An embodiment relates to a computer-implemented method for generating a combined tissue-vessel representation. The method includes receiving imaging data of a tissue; receiving imaging data of a vessel; generating a tissue representation based on the imaging data of the tissue; generating a vessel representation based on the imaging data of the vessel; and generating a combined tissue-vessel representation based on the vessel representation and the tissue representation, the vessel representation being overlaid over the tissue representation.

Abstract: A first image data set and at least one second image data set are captured, and a contrast-enhanced image data set and a non-contrast image data set are determined based on the first and the at least one second image data set. The perfusion image data set is calculated based on a ratio of image values of the contrast-enhanced image data set and locally corresponding image values of the non-contrast image data set, and the perfusion image data set is provided via a second interface.

Abstract: Embodiments of the present invention relates to an X-ray contrast agent. The X-ray contrast agent has an X-ray absorption the change of which between at least two different X-ray photon energy levels differs from the change in X-ray absorption of calcium between the at least two different X-ray photon energy level. Embodiments of the present invention also relates to an X-ray imaging method. Embodiments of the present invention additionally relates to an image reconstruction device. Embodiments of the present invention further relates to an X-ray imaging system.

Abstract: An ensemble of at least two X-ray contrast agents includes X-ray contrast agent and a second X-ray contrast agent. The second X-ray contrast agent has an X-ray absorption whose change between at least two different X-ray photon energies differs significantly from the change of the X-ray absorption of the first X-ray contrast agent between the at least two different X-ray photon energies. An X-ray imaging method, an image reconstruction device, an X-ray imaging system are also disclosed.

Abstract: Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.

Abstract: Systems and methods for assessing a disease are provided. Medical imaging data of lungs of a patient is received. The lungs are segmented from the medical imaging data and abnormality regions associated with a disease are segmented from the medical imaging data. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality regions. The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia.

Abstract: A method for the automatic determination of a contrast agent protocol for a contrast agent-enhanced medical imaging method is described. The method includes an overview recording of a region of interest of an object under examination. In addition, an image recording protocol including a plurality of recording parameters is defined on the basis of the overview recording. Furthermore, recording time points are determined for a plurality of z-positions of the region of interest on the basis of the image recording protocol. In addition, the structures acquired with the overview recording are assigned to the recording time points determined. Finally, a contrast agent protocol including the temporal course of a contrast agent injection curve is defined as a function of the acquired structures and the assigned recording time points. A contrast agent protocol determination device is also described, as well as an imaging medicinal device.

Abstract: A method is for actuating a medical imaging device including a radiation source, embodied during a rotational movement around an axis of rotation to irradiate an irradiation region from a plurality of angular positions. In an embodiment, the method includes capturing an object position of a moving object relative to the irradiation region. The Method further includes actuating the medical imaging device based upon the captured object position of the moving object, such that the intensity of the radiation emitted by the radiation source for a first partial number of the plurality of angular positions in a first angular range round the axis of rotation is reduced relative to a second partial number of the plurality of angular positions in a second angular range around the axis of rotation, he captured object position being included in the first angular range.

Abstract: A computer-implemented method of an embodiment is for automatically estimating and/or correcting an error due to artifacts in a multi-energy computed tomography result dataset relating to at least one target material. In an embodiment, the method includes determining at least one first subregion of the imaging region, which is free from the target material and contains at least one, in particular exactly one, second material with known material-specific energy dependence of x-ray attenuation; for each first subregion, comparing the image values of the energy dataset for each voxel, taking into account the known energy dependence, to determine deviation values indicative of artifacts; and for at least a part of the at least one remaining second subregion of the imaging region, calculating estimated deviation values by interpolating and/or extrapolating from the determined deviation values in the first subregion, the estimated deviation values being used as estimated error due to artifacts.

Abstract: A method is for the characterization of plaque in a region of interest inside an examination subject by way of a plurality of image data sets. The image data sets have been reconstructed from a plurality of projection data sets, which have been acquired via a CT device using different X-ray energy spectra. The method includes: acquiring the image data sets, which include a plurality of pixels. Spectral parameter values are acquired on a pixel by pixel basis using at least two image data sets. Character parameter values are then acquired on a pixel by pixel basis to characterize plaques on the basis of the spectral parameter values. An analysis unit and a computed tomography system are also disclosed.

Abstract: A method is disclosed for generating an image. An embodiment of the method includes detecting a first projection data set via a first group of detector units, the first group including a first plurality of first detector units, each having more than a given number of detector elements; detecting a second projection data set via a second group of detector units, the second group including a second plurality of second detector units, each including, at most, the given number of detector elements; reconstructing first image data based on the first projection data set; reconstructing second image data based on the second projection data set; and combining the first image data and the second image data. A non-transitory computer readable medium, a data processing unit, and an imaging device including the data processing unit are also disclosed.

Abstract: A method is for producing a CT image data set via a detection unit with a photon-counting X-ray detector and configured to convert detected X-rays into measurement signals resolved at least into a first and a second energy range and, via an X-ray source unit, configured to emit X-rays having a first energy spectrum and having a different second energy spectrum. The method includes adapting the first and the second energy ranges, at least one limiting energy of each energy range being adapted; emitting X-rays of the first and second energy spectrum; detecting the emitted X-rays, at least one measurement signal being generated as a function of each respective energy range and the respective energy spectrum; producing the spectral CT image data set based upon the generated first and second measurement signals with the assistance of a spectral image processing technique; and outputting the spectral CT image data set.

Abstract: A method is disclosed for generating an image. An embodiment of the method includes detecting a first projection data set via a first group of detector units, the first group including a first plurality of first detector units, each having more than a given number of detector elements; detecting a second projection data set via a second group of detector units, the second group including a second plurality of second detector units, each including, at most, the given number of detector elements; reconstructing first image data based on the first projection data set; reconstructing second image data based on the second projection data set; and combining the first image data and the second image data. A non-transitory computer readable medium, a data processing unit, and an imaging device including the data processing unit are also disclosed.

Abstract: Systems and methods for automatically detecting a disease in medical images are provided. Input medical images are received. A plurality of metrics for a disease is computed for each of the input medical images. The input medical images are clustered into a plurality of clusters based on one or more of the plurality of metrics to classify the input medical images. The plurality of clusters comprise a cluster of one or more of the input medical images associated with the disease and one or more clusters of one or more of the input medical images not associated with the disease. In one embodiment, the disease is COVID-19 (coronavirus disease 2019).

Abstract: Systems and methods for assessing a disease are provided. Medical imaging data of lungs of a patient is received. The lungs are segmented from the medical imaging data and abnormality regions associated with a disease are segmented from the medical imaging data. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality regions. The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia.

Abstract: A method is for controlling an x-ray imaging device, in particular a computed tomography device. The x-ray imaging device includes an x-ray source and a photon counting detector as an x-ray detector. The methods includes, for an image acquisition process of a patient: determining at least one input parameter relating to at least one of an attenuation property of the patient and a purpose of the image acquisition; at least one of determining or adapting at least one operation parameter of the x-ray detector, dependent upon the at least one input parameter determined; and performing the image acquisition using the at least one operation parameter determined or adapted.