Siemens Medical Solutions Usa, Inc.

Ludovic Sibille

Bruce S Spottiswoode

Matthew David Kelly

Sven Zuehlsdorff

Jicun Hu

Rachid Fahmi

David Schottlander

Vilim Simcic

A computer-implemented method for classifying a reconstruction includes receiving an uncategorized reconstruction and applying a trained classification function configured to classify the uncategorized reconstruction into one of a plurality of categories. The plurality of categories are based on a labeled data-set including a plurality of labeled reconstructions. The trained classification function uses reconstruction-invariant features for classification. The method further includes storing a label indicating a selected one of the plurality of categories for the uncategorized reconstruction.

Learning parameter invariant image reconstruction embedding for AI systems

Systems and methods for detecting and classifying clinical features in medical images are disclosed. Natural language processes are applied to speech received from a dictation system to determine clinical and anatomical information for a medical image being viewed. In some examples, gaze location information identifying an eye position is received, as well as an image position for the medical image being viewed. Features of the medical image are detected and classified based on machine learning models. Anatomical associations are generated based on one or more of the classifications, the anatomical information, the gaze information, and the image position. The machine learning models can be trained based on the anatomical associations. In some examples, reports are generated based on the anatomical associations.

Methods and apparatus for artificial intelligence informed radiological reporting and model refinement

A method for parametric image reconstruction and motion correction using whole-body motion fields includes receiving a nuclear imaging data set including a set of dynamic frames and generating at least one of a whole-body forward motion field and/or a whole-body inverse motion field for at least one frame in the set of frames. An iterative loop is applied to update at least one parameter used in a direct parametric reconstruction and at least one parametric image is generated based on the at least one parameter updated by the iterative loop. The iterative loop includes calculating a frame emission image for the at least one frame, generating a motion-corrected frame emission image based on the at least one whole-body forward motion field or a whole-body inverse motion field, and updating at least one parameter by applying a fit to the motion-corrected frame emission image.

Inter-frame motion correction in whole-body direct parametric image reconstruction

A method for configuring a neural network comprises: accessing a plurality of three-dimensional (3D) emission image data sets collected by an emission scanner from respective brains of respective subjects; transforming each of the plurality of 3D emission image data sets to a respective two-dimensional (2D) image; cropping portions of each respective 2D image to remove image data corresponding to tissue outside of a striatum of each of the respective brains, to form respective cropped 2D striatum images; and training a neural network to detect a presence of a Parkinsonian syndrome using the cropped 2D striatum images.

Neural network for neurodegenerative disease classification

In a method for retrieval of similar findings from a hybrid image dataset, a database of hotspots is prepared, wherein the hotspots are identified by binary strings encoding descriptors, and identify binary strings stored in the database are identified that resemble a new binary string.

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Knoxville, TN, United States of America

Ludovic Sibille