Abstract: A method for localizing structural connectivity biomarkers in neurological diseases, includes dividing a diffusion magnetic resonance imaging brain volume into a set of connected brain regions; extracting three-dimensional voxels along fiber connections which structurally connect the connected brain regions, wherein the brain regions comprise bundles of neurons; applying a deep neural network to diffusion magnetic resonance imaging features extracted from the three-dimensional voxels for each set of fiber connections which structurally connect brain regions; outputting a disease classification based on applying the deep neural network; and applying multi-instance learning to predict whether each fiber connection is indicative of a healthy brain or a diseased brain.

Abstract: The present invention relates to an apparatus (10) to analyse diffusion magnetic resonance imaging data. The apparatus comprises an input unit (20), a processing unit (30), and an output unit (40). The input unit is configured to provide the processing unit with at least one diffusion magnetic resonance imaging “dMRI” image of a patient's brain. The processing unit is configured to determine an estimate of an orientation of neurons at each voxel in the dMRI image, the determination comprising utilization of the at least one dMRI image. The processing unit is configured to determine a plurality of fiber tracts in the at least one dMRI image, the determination comprising utilization of the estimated orientation of neurons at each voxel in the at least one dMRI image. The processing unit is configured to select a plurality of voxels along at least one fiber tract of the plurality of fiber tracts.

Abstract: The invention provides for a medical imaging system (100, 400), comprising: The execution of the machine executable instructions (112) causes a processor (104) to: receive (200) a set of input white matter fiber tracts (118): receive (202) the label from a discriminator neural network (116) in response to inputting the set of input w hue matter fiber tracts, generate (204) an optimized feature vector (122) using the set of input white matter fiber tracts and a generator neural network ((114) if the label indicates anatomically incorrect; receive (206) the set of generated white matter fiber tracts from the generator neural network in response to inputting the optimized feature vector, and construct (208) a false positive subset (126) of the set of input white matter fiber tracts using the generated set of white matter fiber tracts.

Abstract: The invention provides for a medical imaging system (100, 300). The medical imaging system comprises a memory (110) for storing machine executable instructions (120). The memory further contains an implementation of a trained convolutional neural network (122, 122?, 122?, 122??, 122??). The trained convolutional neural network comprises more than one spherical convolutional neural network portions (502, 502?). The trained convolutional neural network is configured for receiving diffusion magnetic resonance imaging data (124). The diffusion magnetic resonance imaging data comprises a spherical diffusion portion (500, 500?). The more than one spherical convolutional neural network portions are configured for receiving the spherical diffusion portion. The trained convolutional neural network comprises an output layer (508) configured for generating a neural network output (126) in response to inputting the diffusion magnetic resonance imaging data into the trained convolutional neural network.

Abstract: This application proposes an improved medical imaging device enabling a timely communication of critical findings. The medical imaging device comprises an image acquisition unit, adapted to acquire image data of a subject to be imaged. The medical imaging device further comprises a local data processing device having an artificial-intelligence-module, Al-module, adapted to automatically detect a finding on basis of the acquired image data and to determine a priority status of the detected finding. Further, the medical imaging device comprises a notification module, adapted to provide, if the determined priority status reaches or exceeds a notification threshold, a notification data containing the detected finding. The application further proposes a medical imaging system, a method of operating a medical imaging device, a computer program element and a computer-readable medium having stored the computer program element.

Abstract: A computer-implemented method for sparse recovery of fiber orientations using a multidimensional Prony method for use in tractography applications includes performing magnetic resonance imaging to acquire a plurality of sparse signal measurements using a q-space sampling scheme which enforces a lattice structure with a predetermined number of collinear samples. Next, for each voxel included in the plurality of sparse signal measurements, a computer system is used to perform a parameter estimation process. This process includes translating a portion of the sparse signal measurements corresponding to the voxel into a plurality of Sparse Approximate Prony Method (SAPM) input parameters, and applying a SAPM process to the SAPM input parameters to recover a number of fiber populations, a plurality of orientation vectors, and a plurality of amplitude scalars.

Abstract: A computer-implemented method for sparse recovery of fiber orientations using a multidimensional Prony method for use in tractography applications includes performing magnetic resonance imaging to acquire a plurality of sparse signal measurements using a q-space sampling scheme which enforces a lattice structure with a predetermined number of collinear samples. Next, for each voxel included in the plurality of sparse signal measurements, a computer system is used to perform a parameter estimation process. This process includes translating a portion of the sparse signal measurements corresponding to the voxel into a plurality of Sparse Approximate Prony Method (SAPM) input parameters, and applying a SAPM process to the SAPM input parameters to recover a number of fiber populations, a plurality of orientation vectors, and a plurality of amplitude scalars.