Abstract: In order to reduce the time and effort required to generate high-quality image reconstructions, a machine-trained neural network may assign a quality score to an image at each iteration of a reconstruction. The neural network may confirm that the iterative reconstruction process increases image quality as each iteration converges to the solution of an optimization problem. The image quality score generated by the neural network may drive the reconstruction toward better image quality by contributing to a regularization term of a cost function minimized by the optimization problem. The neural network may allow for multiple reconstruction of image data to be performed rapidly and for the highest image quality reconstruction to be identified. Additionally, the neural network may provide exit criteria of the iterative reconstruction or may contribute to the optimization problem.

Abstract: A method is used to carry out a magnetic resonance measurement with at least one echo train with n spin echoes and prospective movement correction. Movement correction data for each echo train is updated at the start of the echo train and is then updated again at most partially for the spin echoes.

Abstract: The disclosure includes a method for generating quantitative magnetic resonance (MR) images of an object under investigation. A first MR data set of the object under investigation is captured in an undersampled raw data space, wherein the object under investigation is captured in a plurality of 2D slices, in which the resolution in a slice plane of the slices is in each case higher than perpendicular to the slice plane, wherein the plurality of 2D slices are in each case shifted relative to one another by a distance which is smaller than the resolution perpendicular to the slice plane. Further MR raw data points of the first MR data set are reconstructed with the assistance of a model using a cost function which is minimized. The cost function takes account of the shift of the plurality of 2D slices perpendicular to the slice plane.

Abstract: A system and method generate a synthetic image with switchable image contrast components for a biological object. The method includes: a) using first and second quantitative MRI acquisition techniques for measuring a value of first or second quantitative parameters Q1, Q2 for the biological object and generating first and second quantitative maps, the first and second quantitative MRI acquisition techniques generate first and second contrast-weighted images; b) using the first and second quantitative maps, and the first and second contrast weighted images as inputs in a model configured for generating a synthetic image M with arbitrary sequence parameters P1, P2, P3, according to: M=|Cif(Q1,Q2,P1,P2,P3)| wherein Ci with i=1,2, are contrast components for the generation of the synthetic image M coming from respectively the first (i=1) and second (i=2) contrast-weighted images (i=1) and f is a function of Q1, Q2, P1, P2 and P3; and c) displaying the synthetic image M.

Abstract: A method and a system create an age-specific quantitative atlas for a biological object. The method includes obtaining a quantitative map of the biological object for each subject of a healthy subject population, generating an age-specific initial map for the biological object using a weighted mean, and spatially registering each of the quantitative maps on the age-specific initial map. The generating and registering steps are repeated iteratively until reaching a first predefined alignment threshold between all spatially registered quantitative maps. The new age-specific initial map obtained is stored at the end of the iterative process of the repeating step as the age-specific quantitative atlas for a biological object characterized by the specific age.

Abstract: A system and a method monitor a biological process. The method includes obtaining an abnormal tissue mask from an abnormal tissue segmentation of an image of an object containing tissue to be analyzed, the image being acquired at a time t0 being a reference time point. Other images of the object are registered onto the abnormal tissue mask, the other images being acquired at other time points. Image contrasts of the other images are normalized with respect to the contrasts of the image acquired at the reference time point. The normalized images are subtracted for each available contrast in order to obtain difference images. A joint difference image is created by summing the previously obtained difference images. A biological process progression map is created by overlapping the abnormal tissue mask obtained and the joint difference image after applying a pre-defined threshold.

Abstract: A system and method automatically predicts an evolution of a cognitive score for a subject by classifying a cognitive data set for the subject into a first or second class by determining the cognitive set of data for each subject of a group, acquiring for each subject a neuropsychological score used for classifying each subject in the first or second class, and training a two-class machine learning classification algorithm on the cognitive data sets of all subjects. For each subject, the cognitive data set is used as input of the algorithm and the obtained classification of the subject as output target of the algorithm. The algorithm classifies each cognitive data set in the first or second class. The evolution of the cognitive score of a subject is predicted by the trained algorithm for automatically classifying a new cognitive dataset for the subject into the first or second class.

Abstract: In a magnetic resonance (MR) apparatus, and model-based method, for identifying a nuclear spin-dependent attribute of a subject, MR signals are acquired in multiple repetitions of an MR data acquisition sequence that is changed from repetition-to-repetition so as to deliberately encode effects of magnetization transfer between nuclear spins into the acquired MR signals. A model is generated, composed of at least two molecule pools, in which a single magnetization transfer parameter is used that is derived from the MR signals in which the magnetization transfer is encoded. A nuclear spin-dependent attribute of the subject is then identified, by comparing at least one MR signal evolution from the subject to at least one signal evolution produced by the model.

Abstract: Organ tissue properties of a patient are automatically compared with organ tissue properties of a healthy subject group. A population norm for the organ tissue properties is determined by: selecting at least two different tissue properties of the organ; determining for each tissue property previously selected and for each subject of said group a quantitative tissue property map; for each subject of the group, calculating a joint histogram from all the quantitative tissue property maps obtained for said subject; and determining an averaged joint histogram from all subjects of the healthy group, thus defining the population norm. A comparison is automatically performed of the averaged joint histogram with a patient joint histogram obtained for the organ tissue properties of the patient, by calculating a statistical deviation of values of a patient joint histogram relative to values of the averaged joint histogram, and mapping the statistical deviation to the patient organ.

Abstract: In order to reduce the time and effort required to generate high-quality image reconstructions, a machine-trained neural network may assign a quality score to an image at each iteration of a reconstruction. The neural network may confirm that the iterative reconstruction process increases image quality as each iteration converges to the solution of an optimization problem. The image quality score generated by the neural network may drive the reconstruction toward better image quality by contributing to a regularization term of a cost function minimized by the optimization problem. The neural network may allow for multiple reconstruction of image data to be performed rapidly and for the highest image quality reconstruction to be identified. Additionally, the neural network may provide exit criteria of the iterative reconstruction or may contribute to the optimization problem.

Abstract: System and method for automatically predicting an evolution of a cognitive score for a subject by classifying a cognitive data set for said subject into either a first class or a second class, the method comprising: determining the cognitive set of data for each subject of a group of subjects; acquiring for each subject a CDRSoB neuropsychological score, wherein said CDRSoB neuropsychological score is used for classifying each subject either in the first class or in the second class; training a two-class machine learning classification algorithm on the cognitive data sets of all subjects, wherein for each subject, the cognitive data set is used as input of the algorithm and the obtained classification of the subject as output target of the algorithm, wherein the two-class machine learning classification algorithm is configured for classifying each cognitive data set either in said first class or in said second class; predicting the evolution of the cognitive score of a subject by using the trained two-clas

Abstract: A system and a method determine a value for a parameter. Reference values for the parameter are determined from a group of objects. A first technique is used by the system for determining for each object the reference value from a first set of data. A learning dataset is created by associating for each object of the group of objects a second set of data and the reference value. The second set of data is acquired by the system according to a second technique for determining values of the parameter and is configured for enabling a determination of the parameter. A machine learning technique trained on the learning dataset is used for determining a value of the parameter. The second set of data obtained for each of the objects is used as input in a machine learning algorithm and its associated reference value is used as output target.

Abstract: Organ tissue properties of a patient are automatically compared with organ tissue properties of a healthy subject group. A population norm for the organ tissue properties is determined by: selecting at least two different tissue properties of the organ; determining for each tissue property previously selected and for each subject of said group a quantitative tissue property map; for each subject of the group, calculating a joint histogram from all the quantitative tissue property maps obtained for said subject; and determining an averaged joint histogram from all subjects of the healthy group, thus defining the population norm. A comparison is automatically performed of the averaged joint histogram with a patient joint histogram obtained for the organ tissue properties of the patient, by calculating a statistical deviation of values of a patient joint histogram relative to values of the averaged joint histogram, and mapping the statistical deviation to the patient organ.

Abstract: In a magnetic resonance (MR) apparatus, and model-based method, for identifying a nuclear spin-dependent attribute of a subject, MR signals are acquired in multiple repetitions of an MR data acquisition sequence that is changed from repetition-to-repetition so as to deliberately encode effects of magnetization transfer between nuclear spins into the acquired MR signals. A model is generated, composed of at least two molecule pools, in which a single magnetization transfer parameter is used that is derived from the MR signals in which the magnetization transfer is encoded. A nuclear spin-dependent attribute of the subject is then identified, by comparing at least one MR signal evolution from the subject to at least one signal evolution produced by the model.

Abstract: A method improves a detection of a brain tissue pathology in magnetic resonance (MR) images of a patient. The method includes acquiring multiple MR imaging data for creating four different contrast maps of a patient brain. From the multiple MR imaging data, performing an estimation of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) concentration for each voxel of a part of the patient brain. From the multiple MR imaging data, segmenting the part of the patient brain in different regions-of-interest (ROIs) according to a chosen atlas. For each voxel of each of the contrast maps of the patient brain, computing, for the part of the patient brain, a deviation score. The method further includes creating from the deviation score and for each of the quantitative contrast maps, a deviation map representing the part of the brain in dependence on the deviation score calculated for each voxel.

Abstract: A method for automatically and dynamically optimizing image acquisition parameters/commands of an imaging procedure performed by a medical imaging apparatus in order to mitigate or cancel dynamic effects perturbing the image acquisition process of an object to be imaged by the medical imaging apparatus. The method includes connecting a dynamic correction module (DCM) to the medical imaging apparatus, automatically acquiring by the DCM image acquisition parameters/commands and data about dynamic changes or effects, and automatically determining in real time, by the DCM, at least one new image acquisition parameter/command from the image acquisition parameters/commands defined in the imaging control system and the dynamic change data, while the image acquisition parameter/command defined in the imaging control system remains unchanged. The method further includes automatically providing, by the DCM, the new image acquisition parameter/command to the hardware control system.

Abstract: A method is disclosed for recording a parameter map of a target region via a magnetic resonance device. In at least one embodiment, an optimization method is used for the iterative reconstruction of the parameter map. In the optimization method, the deviation of undersampled magnetic resonance data of the target region present in the k-space for different echo times, magnetic resonance data of a portion of the k-space being present in each case for each echo time, is assessed from hypothesis data of a current hypothesis for the parameter map obtained as a function of the parameter from a model for the magnetization. To determine the magnetic resonance data of a portion of the k-space, undersampled raw data is initially acquired within the portions by way of the magnetic resonance device embodied for parallel imaging, and missing magnetic resonance data within the portions is completed by way of interpolation.

Abstract: A folding top for a cabriolet vehicle, and which includes a top linkage and a top cover. The top linkage may be adjusted for movement between a closed position and an open position via a roof kinematic system. The roof kinematic system is configured as a four-bar linkage kinematic system having a first link, a second link, and a drive member to drive the first link.

Abstract: A method for automatically updating planning of measurement volumes as a function of object motion during MRI examination of an object in which the planning images a specific subject/object anatomy by MRI, includes defining an object reference pose and a reference coordinate system for the reference pose at a time during MRI examination, with the reference coordinate system being used for planning the measurement volumes. During examination, information is obtained about an object pose change between a current object pose at the time and the reference pose. For each subsequent scan or single acquisition of imaging data during examination, pose change information is used for calculating a new coordinate system for the current pose and updating the planning of measurement volumes as a function of the new coordinate system so the imaged object anatomy remains the same throughout the scans or acquisitions of imaging data irrespective of object motion.

Abstract: A method for improving image homogeneity of image data acquired from balanced Steady-State Free Precision (bSSFP) sequences in magnetic resonance imaging. Multiple bSSFP sequences are performed with different radio frequency phase increments to create multiple bSSFP image volumes with different phase offsets ?. Each image has voxels whose intensity M is a function of a nuclear resonance signal (or magnetization) measured by the MR imaging apparatus. Per-voxel fitting of a mathematical signal model onto the measured magnetization of the field of view in function of the phase offsets ?. Then the spin density M0, the relaxation time ratio ? and the local phase offset ?? are determined from the fit for each voxel. A homogeneous image of the object is generated by calculating the signal intensity in each voxel, using the spin density M0 and the relaxation time ratio ?, wherein ?? is chosen such that ????=0°.