Abstract: A trained deep learning network is for determining a cardiac phase in magnet resonance imaging. In an embodiment, the trained deep learning network includes an input layer; an output layer; and a number of hidden layers between input layer and output layer, the layers processing input data entered into the input layer. In an embodiment, the deep learning network is designed and trained to output a probability or some other label of a certain cardiac phase at a certain time from entered input data. A method for determining a cardiac phase in magnet resonance imaging; a related device; a training method for the deep learning network; a control device and a related magnetic resonance imaging system are also disclosed.

Abstract: In a method for automatic motion detection in medical image-series, a dataset of a series of images is provided. The images can be of a similar region of interest that are recorded at consecutive points of time. The method can further include localizing a target in the images of the dataset and calculating a position of the target in the images to calculate localization data of the target, and calculating movement data of a movement of the target of temporal adjacent images of the images based on the localization data.

Abstract: A trained deep learning network is for determining a cardiac phase in magnet resonance imaging. In an embodiment, the trained deep learning network includes an input layer; an output layer; and a number of hidden layers between input layer and output layer, the layers processing input data entered into the input layer. In an embodiment, the deep learning network is designed and trained to output a probability or some other label of a certain cardiac phase at a certain time from entered input data. A method for determining a cardiac phase in magnet resonance imaging; a related device; a training method for the deep learning network; a control device and a related magnetic resonance imaging system are also disclosed.

Abstract: In a method and device for fingerprinting magnetic resonance imaging, a first sequence of MR data is acquired within a region of interest using a fingerprinting magnetic resonance pulse sequence; the first sequence of MR data is input to a neural network; a second sequence of MR data from the neural network is output from the neural network, the second sequence of MR data having reduced undersampling/aliasing artifacts and/or noise compared to the first sequence of MR data; values of at least one quantitative parameter are determined for the region of interest based on the second sequence of MR data; and a quantitative parameter map of the at least one quantitative parameter for the region of interest is constructed based on the determined values.

Abstract: In a method for automatic motion detection in medical image-series, a dataset of a series of images is provided. The images can be of a similar region of interest that are recorded at consecutive points of time. The method can further include localizing a target in the images of the dataset and calculating a position of the target in the images to calculate localization data of the target, and calculating movement data of a movement of the target of temporal adjacent images of the images based on the localization data.

Abstract: In a method for determining magnetic resonance (MR) parameters, an MR fingerprint of a voxel is acquired by execution of a pulse sequence, the MR fingerprint is provided as an input into the input layer of a trained neural network, and at least one MR parameter relating to the MR fingerprint is provided at the output layer of the neural network.

Abstract: In a method for determining magnetic resonance (MR) parameters, an MR fingerprint of a voxel is acquired by execution of a pulse sequence, the MR fingerprint is provided as an input into the input layer of a trained neural network, and at least one MR parameter relating to the MR fingerprint is provided at the output layer of the neural network.