Abstract: In a method to operate a magnetic resonance (MR) system to acquire MR data, an RF excitation pulse is radiated followed by repeated, chronologically sequential implementation of the following steps in order to respectively acquire the MR data of an echo train. A refocusing pulse is radiated, a phase coding gradient is activated, and an additional magnetic field gradient for spatial coding is activated in a direction that is orthogonal to the direction of the phase coding gradient in order to read out the MR data of a k-space line. A k-space line in the k-space center is acquired at a predetermined echo time. A first half of k-space is acquired by entering data into k-space lines of the respective echo train, the data being acquired before the echo time. A second half of k-space is acquired by entering data into k-space lines of the respective echo train, this data having been acquired after the echo time.

Abstract: In a method to associate k-space lines with echo trains of raw magnetic resonance data, parallel k-space lines orthogonally intersect a plane at respective intersection points. Each echo train has a trajectory length, and the k-space lines are associated with the echo trains such that a sum of trajectory lengths of all echo trains is minimal. The trajectory length TL of an echo train is defined by TL = ? i = 1 L - 1 ? ? P i ? P i + 1 _ wherein L is a sequence of k-space lines, Pi is an intersection point of the i-th k-space line of the echo train with the plane; and PiPi+1 is the length of the path from the i-th intersection point to the (i+1)-th intersection point.

Abstract: In a method to operate a magnetic resonance (MR) system to acquire MR data, an RF excitation pulse is radiated followed by repeated, chronologically sequential implementation of the following steps in order to respectively acquire the MR data of an echo train. A refocusing pulse is radiated, a phase coding gradient is activated, and an additional magnetic field gradient for spatial coding is activated in a direction that is orthogonal to the direction of the phase coding gradient in order to read out the MR data of a k-space line. A k-space line in the k-space center is acquired at a predetermined echo time. A first half of k-space is acquired by entering data into k-space lines of the respective echo train, the data being acquired before the echo time. A second half of k-space is acquired by entering data into k-space lines of the respective echo train, this data having been acquired after the echo time.

Abstract: A method is disclosed for reducing metal artifacts in CT image datasets. An embodiment of the method includes reconstructing a first CT image dataset with and a second CT image dataset without metal artifact correction, weighted summation of a high-pass-filtered first and a high-pass-filtered second CT image dataset plus a low-pass-filtered second CT image dataset, wherein the weightings are dependent on the proximity to metal in the CT image datasets. A computing unit, a CT system and a C-arm system designed to execute the method are also disclosed.

Abstract: In a method to associate k-space lines with echo trains of raw magnetic resonance data, parallel k-space lines orthogonally intersect a plane at respective intersection points. Each echo train has a trajectory length, and the k-space lines are associated with the echo trains such that a sum of trajectory lengths of all echo trains is minimal. The trajectory length TL of an echo train is defined by TL = ? i = 1 L - 1 ? ? P i ? P i + 1 _ wherein L is a sequence of k-space lines, Pi is an intersection point of the i-th k-space line of the echo train with the plane; and PiPi+1 is the length of the path from the i-th intersection point to the (i+1)-th intersection point.

Abstract: A method is disclosed for reducing metal artifacts in CT image datasets. An embodiment of the method includes reconstructing a first CT image dataset with and a second CT image dataset without metal artifact correction, weighted summation of a high-pass-filtered first and a high-pass-filtered second CT image dataset plus a low-pass-filtered second CT image dataset, wherein the weightings are dependent on the proximity to metal in the CT image datasets. A computing unit, a CT system and a C-arm system designed to execute the method are also disclosed.

Abstract: A method and a device for operating a wind farm with a plurality of wind turbines are provided. According to the method, operating parameters of the wind turbines of the wind farm are adjusted according to an optimization goal, the optimization goal being the maximum value of the total output of the wind farm produced from the sum of all individual outputs of the wind turbines. The optimization goal differs from conventional optimization goals where the respective individual outputs of the wind turbines are optimized without taking the overall output into consideration.

Abstract: A method and a device for operating a wind farm with a plurality of wind turbines are provided. According to the method, operating parameters of the wind turbines of the wind farm are adjusted according to an optimization goal, the optimization goal being the maximum value of the total output of the wind farm produced from the sum of all individual outputs of the wind turbines. The optimization goal differs from conventional optimization goals where the respective individual outputs of the wind turbines are optimized without taking the overall output into consideration.

Abstract: A method is disclosed for reconstructing image data of an examination object from measurement data, wherein the measurement data was captured as projection data during a relative rotational movement between a radiation source of a computed tomography system and the examination object. In at least one embodiment, a first image is determined from the measurement data and pixel values of the first image are modified, by classifying the pixel values in at least three classes, a class pixel value being assigned to each class, and the pixels of the first image being allocated the respective class pixel value. Projection data is calculated from the thus modified first image. The calculated projection data is used to normalize the measured projection data. Values are modified in the normalized projection data and the thus modified normalized projection data is subjected to a processing that reverses normalization. Finally a second image is determined from the thus processed projection data.