Arizona State University

Mayo Foundation for Medical Education and Research

Viridity Energy, Inc.

Ms-technologies Gmbh

Arizona Board of Regents, a Body Corporate

Banner Health

Teresa Wu

Jing Li

Leland S Hu

Kristin R Swanson

J Ross Mitchell

Alain Steven

Nhan L Tran

Eunice B Hameyie

Jin Wen

Fei Gao

Yuan-Ming Fleming Lure

Kewei Chen

Min Zhang

David Weidman

Nathan Gaw

Hyunsoo Yoon

Andrea Hawkins-Daarud

Yanzhe Xu

Yi Su

Jay Shah

Ajay Sunder

Xiaonan Liu

A system for blob detection using deep learning is disclosed. The system may include a non-transitory computer-readable storage medium configured to store a plurality of instructions thereon which, when executed by a processor, cause the system to train a U-Net and generate a probability map including a plurality of centroids of a plurality of corresponding blobs, derive two distance maps with bounded probabilities, apply Difference of Gaussian (DoG) with an adaptive scale constrained by the two distance maps with the bounded probabilities, and apply Hessian analysis and perform a blob segmentation.

Deep learning based blob detection systems and methods

Using deep learning, imaging harmonization among images produced with differing PET tracers is achieved. A method may include providing an original PET image of the brain using an original PET tracer, providing a target PET tracer, and converting, using a deep learning neural network, the original PET image into a target PET image simulating the image that would be obtained had the target PET tracer been used.

Deep residual inception encoder-decoder network for amyloid PET harmonization

Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme ('GBM') are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor (“BAT”) regions.

Systems and methods for quantifying multiscale competitive landscapes of clonal diversity in glioblastoma

Described here are systems and methods for generating and implementing a hybrid machine learning and mechanistic model to produce biological feature maps, or other measurements of biological features, based on an input of multiparametric magnetic resonance or other images. The hybrid model can include a combination of a machine learning model and a mechanistic model that takes as an input multiparametric MRI, or other imaging, data to generate biological feature maps (e.g., tumor cell density maps), or other measures or predictions of biological features (e.g., tumor cell density). The hybrid models have capabilities of learning individual-specific relationships between imaging features and biological features.

Methods for using machine learning and mechanistic models for biological feature mapping with multiparametric MRI

A system and method for predicting mild cognitive impairment (MCI) related diagnosis and prognosis utilizing deep learning. More specifically, the system and method produce predictions of MCI conversions to Alzheimer's/dementia and prognosis related thereof. Using available medical imaging and non-imaging data a diagnosis and prognosis model is a deep learned model trained using transfer learning. An MCI-DAP server may then receive a request from a clinician to process predictions related to a target patient's diagnosis or prognosis. The target patient's medical data is retrieved and used to create a model for the target patient. Then details of the target patient's model and the diagnosis and prognosis model are compared, a prediction is generated, and the prediction is returned to the clinician. As new medical data becomes available it is fed into the respective model to improve accuracy and update predictions.

System and method for diagnostics and prognostics of mild cognitive impairment using deep learning

Methods that implement image-guided tissue analysis, MRI-based computational modeling, and imaging informatics to analyze the diversity and dynamics of molecularly-distinct subpopulations and the evolving competitive landscapes in human glioblastoma multiforme ('GBM') are provided. Machine learning models are constructed based on multiparametric MRI data and molecular data (e.g., CNV, exome, gene expression). Models can also be built based on specific biological factors, such as sex and age. Inputting MRI data into the trained predictive models generates maps that depict spatial patterns of molecular markers, which can be used to quantify and co-localize regions molecularly distinct subpopulations in tumors and other regions, such as the non-enhancing parenchyma, or brain around tumor ('BAT') regions.

A system and method for characterizing tissues of a subject using multi-parametric imaging are provided. In some aspects, the method includes receiving a set of multi-parametric magnetic resonance ('MR') images acquired from a subject using an MR imaging system, and selecting at least one region of interest ('ROI') in the subject using one or more images in the set of multi-parametric MR images. The method also includes performing a texture analysis on corresponding ROIs in the set of multi-parametric MR images to generate a set of texture features, and applying a classification scheme, using the set of texture features, to characterize tissues in the ROI. The method further includes generating a report indicative of characterized tissues in the ROI.

System and method for tissue characterization based on texture information using multi-parametric MRI

Methods and systems for identifying blobs, for example kidney glomeruli, are disclosed. A raw image may be smoothed via a difference of Gaussians filter, and a Hessian analysis may be conducted on the smoothed image to mark glomeruli candidates. Exemplary candidate features are identified, such as average intensity A, likelihood of blobness R, and flatness S. A clustering algorithm may be utilized to post prune the glomeruli candidates.

Kidney glomeruli measurement systems and methods

The disclosure facilitates generation of energy-related revenue for an energy customer of an electricity supplier. The disclosure herein can be implemented to generate suggested operating schedules for energy assets that include a controllable energy asset, using an objective function. The objective function is determined based on a dynamic simulation model of the energy profile of the energy assets. The dynamic simulation model is adaptive to physical changes in the energy assets based at least in part on a physical model of the thermodynamic property of the at least one energy asset and at least in part on data representative of an operation characteristic of the controllable energy asset, a thermodynamic property of the energy assets, and/or a projected environmental condition. Energy-related revenue available to the energy customer is based at least in part on a wholesale electricity market or on a regulation market.

Facilitating revenue generation from wholesale electricity markets using an engineering-based model

The apparatus, systems and methods herein facilitate generation of energy-related revenue for an energy customer of an electricity supplier. The apparatuses and methods herein can be used to generate suggested operating schedules for the energy assets that including a controllable energy asset, using an objective function. The objective function is determined based on a dynamic simulation model of the energy profile of the energy assets. The dynamic simulation model is adaptive to physical changes in the energy assets based on a parametric estimation using at least one model parameter. The model parameter is at least one of an operation characteristic of the controllable energy asset, a thermodynamic property of the energy assets, and a projected environmental condition. Energy-related revenue available to the energy customer is based at least in part on a wholesale electricity market or on a regulation market.

Growing community of inventors

Gilbert, AZ, United States of America

Teresa Wu