Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Francisco Pereira

Bin Lou

Dorin Comaniciu

Hasan Ertan Cetingul

Bogdan Georgescu

Tommaso Mansi

Benjamin L Odry

Saikiran Rapaka

Christophe Chefd'hotel

Wen Hong Liu

James G Reisman

Ahmet Tuysuzoglu

Alma Gregory Sorensen

Angeliki Lazaridou

Ashutosh Modi

Helene Chopinet

A computer-implemented method for decoding brain imaging data of individual subjects by using additional imaging data from other subjects includes receiving a plurality of functional Magnetic Resonance Imaging (fMRI) datasets corresponding to a plurality of subjects. Each fMRI dataset corresponds to a distinct subject and comprises brain activation patterns resulting from presentation of a plurality of stimuli to the distinct subject. A group dimensionality reduction (GDR) technique is applied to the example fMRI datasets to yield a low-dimensional space of response variables shared by the plurality of subjects. A model is trained to predict a set of target variables based on the low-dimensional space of response variables shared by all subjects, wherein the set of target variables comprise one or more characteristics of the plurality of stimuli.

Decoding from brain imaging data of individual subjects by using additional imaging data from other subjects

By way of introduction, the present embodiments described below include apparatuses and methods for generating natural language representations of mental content from functional brain images. Given functional imaging data acquired while a subject reads a text passage, a reconstruction of the text passage is produced. Linguistic semantic vector representations are assigned () to words, phrases or sentences to be used as training stimuli. Basis learning is performed (), using brain imaging data acquired () when a subject is exposed to the training stimuli and the corresponding semantic vectors for training stimuli, to learn an image basis directly. Semantic vector decoding () is performed with functional brain imaging data for test stimuli and using the image basis to generate a semantic vector representing the test imaging stimuli. Text generation () is then performed using the decoded semantic vector representing the test imaging stimuli.

Generating natural language representations of mental content from functional brain images

A computer-implemented method of monitoring dynamics of patient brain state during neurosurgical procedures includes receiving a plurality of brain response images acquired using a functional MRI scan of a patient over a plurality of time points and selecting a plurality of image features from the brain response images. One or more recurrent neural network (RNN) model are used to directly estimate one or more brain state measurements at each time point based on the image features. Once estimated, the brain state measurements are presented on a display.

Monitoring dynamics of patient brain state during neurosurgical procedures

A method for subject-specific assessment of neurological disorders, the method includes receiving 3D image data representative of a subject's brain and identifying subject-specific anatomical structures in the 3D image data. A subject-specific model for electrical dynamics is created based on the 3D image data and the subject-specific anatomical structures and one or more functional indicators of neurological disorder are computed using the subject-specific model for electrical dynamics.

Subject-specific assessment of neurological disorders with biochemical propagation mechanisms

A computer-implemented method for predicting answers to questions concerning medical image analytics reports includes splitting a medical image analytics report into a plurality of sentences and generating a plurality of sentence embedding vectors by applying a natural language processing framework to the plurality of sentences. A question related to subject matter included in the medical image analytics report is received and a question embedding vector is generated by applying the natural language processing framework to the question. A subset of the sentence embedding vectors most similar to the question embedding vector is identified by applying a similarity matching process to the sentence embedding vectors and the question embedding vector. A trained recurrent neural network (RNN) is used to determine a predicted answer to the question based on the subset of the sentence embedding vectors.

Image analytics question answering

Traumatic brain injury (TBI) in a patient is assessed. A TBI diagnosis for the patient is determined based on features from MRI data, such as anatomical features, functional features, diffusion features, connectivity features from functional MRI, connectivity features from diffusion MRI, and/or network features from the connectivity features. The TBI diagnosis is determined using a trained classifier. The classifier synthesizes the features into a single number (e.g., a confidence in the prediction of the diagnosis) and indicates the features most responsible for the diagnosis. The disease trajectory for a given patient may be predicted using the trained classifier.

Assessment of traumatic brain injury

A method () that generates attenuation correction maps for the reconstruction of PET data using MR images, such as, MR ultra-fast TE (UTE) images, Dixon MR images, as well as MR images obtained using other MR imaging methods.

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Jersey City, NJ, United States of America

Francisco Pereira