Abstract: Described herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an upsampling neural network (122). The upsampling neural network is configured to output an upsampled magnetic resonance image (130) with a second resolution in response to receiving a preliminary magnetic resonance image (126) with a first resolution which is lower than the second resolution. The execution of the machine executable instructions causes a computational system (104) to: receive (200) preliminary k-space data (124); reconstruct (202) the preliminary magnetic resonance image from the preliminary k-space data; receive (204) clinical k-space data (204); receive (206) the upsampled magnetic resonance image in response to inputting the preliminary magnetic resonance image into the upsampling neural network; and provide (208) a motion corrected magnetic resonance image (132) using the upsampled magnetic resonance image and the clinical k-space data.

Abstract: The invention provides for a method of training a neural network (322) configured for providing a further processing location (326). The method comprises providing (200) a labeled medical image (100), wherein the labeled medical image comprises multiple labels each indicating a truth processing location (102, 104, 106). The method further comprises inputting (202) the labeled medical image into the neural network to obtain one trial processing location. The one trial processing location comprises a most likely trial processing location (108). The method further comprises determine (204) the closest truth processing location (106) for the most likely trial processing location. The method further comprises calculating (206) an error vector (110) using the closest truth processing location and the most likely trial processing location. The method further comprises training (208) the neural network using the error vector.

Abstract: The invention provides means for determining 3D position data in an MRI system.

Abstract: Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an image generating neural network (122). The image generating neural network is configured for outputting synthetic magnetic resonance image data (128) in response to receiving reference magnetic resonance image data (126) as input. The synthetic magnetic resonance image data is a simulation of magnetic resonance image data acquired according to a first configuration of a magnetic resonance imaging system when the reference magnetic resonance image data is acquired according to a second configuration of the magnetic resonance imaging system.

Abstract: A computer-implemented method for preparing a subject in medical imaging, comprising: obtaining a series of images of a region of interest comprising at least a part of the subject, wherein the series of images comprises at least a first image and at least a subsequent, second image (S10); determining a position of at least one landmark from the series of images, wherein the at least one landmark is anatomically related to a target anatomy (S20); determining a confidence level assigned to the position of the at least one landmark (S30); determining the position of the target anatomy based on the position of the at least one landmark, and the confidence level (S40); providing the position of the target anatomy for preparing the subject in medical imaging (S50).

Abstract: Disclosed herein is a medical system (100, 300, 500) comprising a memory (110) storing machine executable instructions (120) and a convolutional neural network (122). The convolutional neural network is configured for receiving an initial Dixon magnetic resonance image (124, 126) as input. The convolutional neural network is configured for identifying one or more water-fat swap regions (128) in the initial Dixon magnetic resonance image. The medical system further comprises a processor (104) for controlling the medical system. Execution of the machine executable instructions causes the processor to: receive (200) the initial Dixon magnetic resonance image; and receive (204) the one or more water-fat swap regions from the convolutional neural network in response to inputting the initial Dixon magnetic resonance image into the convolutional neural network.

Abstract: The invention relates to a method of MR imaging of an object (10) positioned in an examination volume of a MR device (1). It is an object of the invention to enable efficient and high-quality non-Cartesian MR imaging, even in situations of strong B0 inhomogeneity. In accordance with the invention, the method comprises: —subjecting the object to an imaging sequence comprising at least one RF excitation pulse and modulated magnetic field gradients, —acquiring MR signals along at least one non-Cartesian k-space trajectory, —reconstructing an MR image from the acquired MR signals, and —detecting one or more mal-sampling artefacts caused by B0 inhomogeneity induced insufficient k-space sampling in the MR image using a deep learning network. Moreover, the invention relates to a MR device (1) and to a computer program.

Abstract: A computer-implemented method for preparing a subject in medical imaging, comprising: obtaining a series of images of a region of interest comprising at least a part of the subject, wherein the series of images comprises at least a first image and at least a subsequent, second image (S10); determining a position of at least one landmark from the series of images, wherein the at least one landmark is anatomically related to a target anatomy (S20); determining a confidence level assigned to the position of the at least one landmark (S30); determining the position of the target anatomy based on the position of the at least one landmark, and the confidence level (S40); providing the position of the target anatomy for preparing the subject in medical imaging (S50).

Abstract: Disclosed herein is a medical system (100, 300, 400) comprising a memory (110) storing a trainable machine learning module (122) trained using training data descriptive of a training data distribution (600) to output a reconstructed medical image (136) in response to receiving measured medical image data (128) as input. The medical system comprises a computational system (104). The execution of machine executable instructions (120) causes the computational system to: receive (200) the measured medical image data and determine (202) the out-of-distribution score and the in-distribution accuracy score consecutively in an order determined a sequence, detect (204) a rejection of the measured medical image data using the out-of-distribution score and/or the in-distribution accuracy score during execution of the sequence, provide (206) a warning signal (134) if the rejection of the measured medical image data is detected.

Abstract: Disclosed herein is a medical system comprising a memory storing machine executable instructions and at least one trained neural network. Each of the at least one neural network is configured for receiving a medical image as input. Each of the at least one trained neural network has been modified to provide hidden layer output. Execution of the machine executable instructions causes the computational system to: receive the medical image; receive the hidden layer output in response to inputting the medical image into each of the at least one trained neural network; provide an anonymized image fingerprint comprising the hidden layer output from each of the at least one trained neural network; and receive an image assessment of the medical image in response to querying a historical image database using the anonymized image fingerprint.

Abstract: Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an image generating neural network (122). The image generating neural network is configured for outputting synthetic magnetic resonance image data (128) in response to receiving reference magnetic resonance image data (126) as input. The synthetic magnetic resonance image data is a simulation of magnetic resonance image data acquired according to a first configuration of a magnetic resonance imaging system when the reference magnetic resonance image data is acquired according to a second configuration of the magnetic resonance imaging system.

Abstract: A system (100) and computer-implemented method are provided for data collection for distributed machine learning of a machine learnable model. A privacy policy data (050) is provided defining computer-readable criteria for limiting a selection of medical image data (030) to a subset of the medical image data to obfuscate an identity of the at least one patient. The medical image data is selected based on the computer-readable criteria to obtain privacy policy-compliant training data (060) for transmission to another entity. The system and method enable medical data collection at clinical sites without requiring manual oversight, and enables such selections to be made automatically, e.g., based on a request for medical image data which may be received from outside of the clinical site.

Abstract: The invention relates to a system for assessing a pulmonary image which allows for an improved assessment with respect to lung nodules detectability. The pulmonary image is smoothed for providing different pulmonary images (20, 21, 22) with different degrees of smoothing, wherein signal values and noise values, which are indicative of the lung vessel detectability and the noise in these images, are determined and used for determining an image quality being indicative of the usability of the pulmonary image to be assessed for detecting lung nodules. Since a pulmonary image shows lung vessels with many different vessel sizes and with many different image values, which cover the respective ranges of potential lung nodules generally very well, the image quality determination based on the different pulmonary images with different degrees of smoothing allows for a reliable assessment of the pulmonary image's usability for detecting lung nodules.

Abstract: A magnetic resonance imaging system including a memory configured to store machine executable instructions, pulse sequence commands, and a first machine learning model including a first deep learning network. The pulse sequence commands are configured for controlling the magnetic resonance imaging system to acquire a set of magnetic resonance imaging data.

Abstract: The invention provides for a magnetic resonance imaging system (100) for acquiring magnetic resonance data (142) from a subject (118) within a measurement zone (108). The magnetic resonance imaging system (100) comprises: a processor (130) for controlling the magnetic resonance imaging system (100) and a memory (136) storing machine executable instructions (150, 152, 154), pulse sequence commands (140) and a dictionary (144). The pulse sequence commands (140) are configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of multiple steady state free precession (SSFP) states per repetition time. The pulse sequence commands (140) are further configured for controlling the magnetic resonance imaging system (100) to acquire the magnetic resonance data (142) of the multiple steady state free precession (SSFP) states according to a magnetic resonance fingerprinting protocol. The dictionary (144) comprises a plurality of tissue parameter sets.

Abstract: The invention relates to a method of MR imaging of an object (10) positioned in an examination volume of a MR device (1). It is an object of the invention to enable efficient and high-quality non-Cartesian MR imaging, even in situations of strong B0 inhomogeneity. In accordance with the invention, the method comprises: —subjecting the object to an imaging sequence comprising at least one RF excitation pulse and modulated magnetic field gradients, —acquiring MR signals along at least one non-Cartesian k-space trajectory, —reconstructing an MR image from the acquired MR signals, and —detecting one or more mal-sampling artefacts caused inhomogeneity induced insufficient k-space sampling in the MR image using a deep learning network. Moreover, the invention relates to a MR device (1) and to a computer program.

Abstract: The present disclosure relates to a computer implemented medical analysis method for predicting metastases (300) in a test tissue sample, the method comprising: providing a first machine learning model (154) having an input and an output, receiving a description (401) of a tumor (304) and first image data (148) of a test tissue sample of an anatomy region (306), the test tissue sample being free of metastases (300), providing the first image data (148) and the tumor description (401) to the input of the first machine learning model (154), in response to the providing, receiving from the output of the first machine learning model (154) a prediction of occurrence of metastases (300) originating from the tumor (304) in the test tissue sample, and providing the prediction.

Abstract: Disclosed herein is a medical system (100, 300, 500) comprising a memory (110) storing machine executable instructions (120) and a convolutional neural network (122). The convolutional neural network is configured for receiving an initial Dixon magnetic resonance image (124, 126) as input. The convolutional neural network is configured for identifying one or more water-fat swap regions (128) in the initial Dixon magnetic resonance image. The medical system further comprises a processor (104) for controlling the medical system. Execution of the machine executable instructions causes the processor to: receive (200) the initial Dixon magnetic resonance image; and receive (204) the one or more water-fat swap regions from the convolutional neural network in response to inputting the initial Dixon magnetic resonance image into the convolutional neural network.

Abstract: The invention provides for a magnetic resonance imaging system (100, 300). The execution of machine executable instructions causes a processor (130) controlling the magnetic resonance imaging system to control (200) the magnetic resonance imaging system to acquire the magnetic resonance imaging data (144) using pulse sequence commands (142) and reconstruct (202) a magnetic resonance image (148). Execution of the machine executable instructions causes the processor to receive (204) a list of suggested pulse sequence command changes (152) by inputting the magnetic resonance image and image metadata (150) into an MRI artifact detection module (146, 146?, 146?).

Abstract: The invention relates to a magnetic resonance imaging data processing system (126) for processing motion artifacts in magnetic resonance imaging data sets using a deep learning network (146, 502, 702) trained for the processing of motion artifacts in magnetic resonance imaging data sets. The magnetic resonance imaging data processing system (126) comprises a memory (134, 136) storing machine executable instructions (161, 164) and the trained deep learning network (146, 502, 702). Furthermore, the magnetic resonance imaging data processing system (126) comprises a processor (130) for controlling the magnetic resonance imaging data processing system.