Abstract: A method uses an artificial neural network (ANN) to automatically produce a magnetic resonance (MR) pulse sequence. A first MR signal corresponding to a first tissue and a second MR signal corresponding to a second tissue are identified. An RF pulse to be applied to the first and second tissues is selected. Based on at least the first MR signal, the second MR signal, and the RF pulse, an updated first MR signal and an updated second MR signal are determined. A difference is computed between the updated first MR signal and the updated second MR signal. The difference is added to an accumulated difference. The RF pulse selecting, updated first and second MR signal determination, difference computation and adding are repeated. The ANN is controlled to use reinforcement learning to select the MR imaging pulse sequence based, at least in part, on the accumulated difference.

Abstract: Systems and methods are provided for automatically designing RF pulses using a reinforcement machine-learnt classifier. Data representing an object and a selected outcome is accessed. A reinforcement learnt method identifies the RF pulse sequence that generates a result within a predefined value of the selected outcome. An MRI scanner images the object using the RF pulse sequence.

Abstract: Systems and methods are provided for synthesizing protocol independent magnetic resonance images. A patient is scanned by a magnetic resonance imaging system to acquire magnetic resonance data. The magnetic resonance data is input to a machine learnt generator network trained to extract features from input magnetic resonance data and synthesize protocol independent images using the extracted features. The machine learnt generator network generates a protocol independent segmented magnetic resonance image from the input magnetic resonance data. The protocol independent magnetic resonance image is displayed.

Abstract: Systems and methods are provided for generating a protocol independent image. A deep learning generative framework learns to recognize the boundaries and classification of tissues in an MRI image. The deep learning generative framework includes an encoder, a decoder, and a discriminator network. The encoder is trained using the discriminator network to generate a latent space that is invariant to protocol and the decoder is trained to generate the best output possible for brain and/or tissue extraction.

Abstract: A method uses an artificial neural network (ANN) to automatically produce a magnetic resonance (MR) pulse sequence. A first MR signal corresponding to a first tissue and a second MR signal corresponding to a second tissue are identified. An RF pulse to be applied to the first and second tissues is selected. Based on at least the first MR signal, the second MR signal, and the RF pulse, an updated first MR signal and an updated second MR signal are determined. A difference is computed between the updated first MR signal and the updated second MR signal. The difference is added to an accumulated difference. The RF pulse selecting, updated first and second MR signal determination, difference computation and adding are repeated. The ANN is controlled to use reinforcement learning to select the MR imaging pulse sequence based, at least in part, on the accumulated difference.

Abstract: In white matter tractography from magnetic resonance imaging, a mathematical representation of diffusion (e.g., fiber orientation distributions) is first estimated from the diffusion MR data. Fiber tracing is performed via deterministic or probabilistic tractography where the tract maps and brain regions from multiple atlases and/or templates can be used for seeding and/or as spatial constraints. Field map correction and/or denoising may improve the diffusion weighted imaging data used in tractography.

Abstract: A system and method includes generation of one or more motion-corrupted images based on each of a plurality of reference images, and training of a regression network to determine a motion score, where training of the regression network includes input of a generated motion-corrupted image to the regression network, reception of a first motion score output by the regression network in response to the input image, and determination of a loss by comparison of the first motion score to a target motion score, the target motion score calculated based on the input motion-corrupted image and a reference image based on which the motion-corrupted image was generated.

Abstract: For image quality scoring of an image from a medical scanner, a generative model of an expected good quality image may be created using deep machine-learning. The deviation of an input image from the generative model is used as an input feature vector for a discriminative model. The discriminative model may also operate on another input feature vector derived from the input image. Based on these input feature vectors, the discriminative model outputs an image quality score.

Abstract: A single level machine-learnt classifier is used in medical imaging. A gross or large structure is located using any approach, including non-ML approaches such as region growing or level-sets. Smaller portions of the structure are located using ML applied to relatively small patches (small relative to the organ or overall structure of interest). The classification of small patches allows for a simple ML approach specific to a single scale or at a voxel/pixel level. The use of small patches may allow for providing classification as a service (e.g., cloud-based classification) since partial image data is to be transmitted. The use of small patches may allow for feedback on classification and updates to the ML. The use of small patches may allow for the creation of a labeled library of classification partially based on ML. Given a near complete labeled library, a simple matching of patches or a lookup can replace ML classification for faster throughput.

Abstract: Tissue is characterized using machine-learnt classification. The prognosis, diagnosis or evidence in the form of a similar case is found by machine-learnt classification from features extracted from frames of medical scan data. The texture features for tissue characterization may be learned using deep learning. Using the features, therapy response is predicted from magnetic resonance functional measures before and after treatment in one example. Using the machine-learnt classification, the number of measures after treatment may be reduced as compared to RECIST for predicting the outcome of the treatment, allowing earlier termination or alteration of the therapy.

Abstract: For image quality scoring of an image from a medical scanner, a generative model of an expected good quality image may be created using deep machine-learning. The deviation of an input image from the generative model is used as an input feature vector for a discriminative model. The discriminative model may also operate on another input feature vector derived from the input image. Based on these input feature vectors, the discriminative model outputs an image quality score.

Abstract: For correction of an image from an imaging system, a deep-learnt generative model is used as a regularlizer in an inverse solution with a physics model of the degradation behavior of the imaging system. The prior model is based on the generative model, allowing for correction of an image without application specific balancing. The generative model is trained from good images, so difficulty gathering problem-specific training data may be avoided or reduced.

Abstract: A system and method including receiving magnetic resonance (MR) imaging data from a first MR scanner device, the MR imaging data including data for a plurality of MR scans of different structural or anatomical regions; generating, based on the MR imaging data, normalized reference data including statistical information for each MR scan; learning a transformation, based on the normalized reference data, to correlate a set of input MR imaging data to the normalized reference data; and storing a record of the transformed imaging data.

Abstract: A learning-based magnetic resonance fingerprinting (MRF) reconstruction method for reconstructing an MR image of a tissue space in an MR scan subject for a particular MR sequence is disclosed. The method involves using a machine-learning algorithm that has been trained to generate a set of tissue parameters from acquired MR signal evolution without using a dictionary or dictionary matching.

Abstract: A method for depicting an airway tree of a patient includes: (a) generating an iodine map of the airway tree from dual energy computed tomography (DECT) imaging data acquired from the patient; (b) defining a region of interest of the airway tree from the DECT imaging data; (c) rendering at least a portion of the airway tree based on information derived from the iodine map and the defined region of interest; and (d) displaying a graphical image of at least a portion of the airway tree on a user interface. Systems for depicting an airway tree of a patient are described.

Abstract: A computer-implemented method for identifying abnormalities in Magnetic Resonance (MR) brain image data includes a computer receiving multi-contrast MR image data of a subject's brain and identifying, within the multi-contrast MR image data, (i) an abnormality region comprising one or more suspected abnormalities and (ii) a healthy region comprising healthy tissue. The computer creates a model of the healthy region, computes a novelty score for each voxel in the multi-contrast MR image data based on the abnormality region and the model, and creates an abnormality map of the subject's brain based on the novelty score computed for each voxel in the multi-contrast MR image data.

Abstract: A method of evaluating airway wall density and inflammation including: segmenting a bronchial tree to create an airway wall map; for each branch, taking a set of locations that form the wall of each branch from the map and sampling the value in a virtual non-contrast image of the bronchial tree and, given a set of samples of pre-contrast densities, computing a value to yield a bronchial wall density for each branch to yield density measures; for each branch, taking the set of locations that form the wall of each branch from the map and sampling the value in a contrast agent map of the bronchial tree and, given the set of samples of contrast agent intake, computing a value to yield a bronchial wall uptake for each branch to yield inflammation measures; and using the density and inflammation measures to determine treatment or predict outcome for a patient.

Abstract: In white matter tractography from magnetic resonance imaging, a mathematical representation of diffusion (e.g., fiber orientation distributions) is first estimated from the diffusion MR data. Fiber tracing is performed via deterministic or probabilistic tractography where the tract maps and brain regions from multiple atlases and/or templates can be used for seeding and/or as spatial constraints. Field map correction and/or denoising may improve the diffusion weighted imaging data used in tractography.

Abstract: Dynamic systems and methods for depicting context among different views of an imaging visualization application used by a medical workstation are provided.

Abstract: A computer-implemented method for generating an assessment of traumatic brain injury (TBI) includes a TBI assessment computer receiving structural imaging data acquired by performing a structural imaging scan on an individual and generating a structural imaging score based on the structural imaging data. The TBI assessment computer receives functional imaging data acquired by performing a functional imaging scan on the individual and generates a functional imaging score based on the functional imaging data. The TBI assessment computer also receives diffusion imaging data acquired by performing a diffusion imaging scan on the individual and generates a diffusion imaging score based on the diffusion imaging data. Based on the structural imaging score, the functional imaging score, and the diffusion imaging score, the TBI assessment computer generates a TBI assessment score.