Abstract: The disclosed technology relates generally to medical imaging, and more particularly, some embodiments relate to systems and methods for creating and using a covariate modulated or “dynamic” atlas. Some embodiments of the disclosure provide a method for predicting an alas using General Additive Model (GAM) parameters, wherein the GAM parameters are derived by registering (and optionally segmenting) a plurality of image data sets from a plurality of different subjects to an initial atlas estimate (e.g., a seed atlas), and analyzing the resulting registration, segmentation, and intensity parameters as correlated with input covariates.

Abstract: The present disclosure is directed to a system and method to integrate genotypic information and phenotypic measurements for predicting health related risks. While the genetic information is extracted through efficient training with genotypic data and biological priors, the phenotypic measurements are further integrated into the risk assessing model through updating. The flexibility of this approach enables not just personalized risk assessment in near future, but also a framework to evaluate the value of specific medical tests, clinical decision support, and life actuarial calculations.

Abstract: The disclosed technology relates generally to medical imaging, and more particularly, some embodiments relate to systems and methods for creating and using a covariate modulated or “dynamic” atlas. Some embodiments of the disclosure provide a method for predicting an alas using General Additive Model (GAM) parameters, wherein the GAM parameters are derived by registering (and optionally segmenting) a plurality of image data sets from a plurality of different subjects to an initial atlas estimate (e.g., a seed atlas), and analyzing the resulting registration, segmentation, and intensity parameters as correlated with input covariates.

Abstract: The disclosed technology relates generally to medical imaging, and more particularly, some embodiments relate to systems and methods for creating and using a covariate modulated or “dynamic” atlas. Some embodiments of the disclosure provide a method for predicting an alas using General Additive Model (GAM) parameters, wherein the GAM parameters are derived by registering (and optionally segmenting) a plurality of image data sets from a plurality of different subjects to an initial atlas estimate (e.g., a seed atlas), and analyzing the resulting registration, segmentation, and intensity parameters as correlated with input covariates.

Abstract: The disclosed technology relates generally to medical imaging, and more particularly, some embodiments relate to systems and methods for creating and using a covariate modulated or “dynamic” atlas. Some embodiments of the disclosure provide a method for predicting an alas using General Additive Model (GAM) parameters, wherein the GAM parameters are derived by registering (and optionally segmenting) a plurality of image data sets from a plurality of different subjects to an initial atlas estimate (e.g., a seed atlas), and analyzing the resulting registration, segmentation, and intensity parameters as correlated with input covariates.

Abstract: The disclosed technology relates generally to medical imaging, and more particularly, some embodiments relate to systems and methods for creating and using a covariate modulated or “dynamic” atlas. Some embodiments of the disclosure provide a method for predicting an alas using General Additive Model (GAM) parameters, wherein the GAM parameters are derived by registering (and optionally segmenting) a plurality of image data sets from a plurality of different subjects to an initial atlas estimate (e.g., a seed atlas), and analyzing the resulting registration, segmentation, and intensity parameters as correlated with input covariates.

Abstract: The disclosure herein provides methods, systems, and devices for automated reorientation and/or analysis of medical scans and/or images. The methods, systems, and devices for automated analysis of medical scans can be configured to mark, score, grade, and/other otherwise classify medical scans that are more time-sensitive, severe, and/or the like to allow a medical professional reviewing and/or analyzing medical scans to view and/or analyze such scans more efficiently by using a common image orientation and/or taking into account knowledge of the risk of severity, time-sensitiveness, and/or other priority.

Abstract: The disclosed technology relates generally to medical imaging, and more particularly, some embodiments relate to systems and methods for creating and using a covariate modulated or “dynamic” atlas. Some embodiments of the disclosure provide a method for predicting an alas using General Additive Model (GAM) parameters, wherein the GAM parameters are derived by registering (and optionally segmenting) a plurality of image data sets from a plurality of different subjects to an initial atlas estimate (e.g., a seed atlas), and analyzing the resulting registration, segmentation, and intensity parameters as correlated with input covariates.

Abstract: Methods, devices and systems are disclosed for measuring biological tissue parameters using restriction spectrum magnetic resonance imaging. In one aspect, a method of characterizing a biological structure includes determining individual diffusion signals from magnetic resonance imaging (MRI) data in a set of MRI images that include diffusion weighting conditions (e.g.

Abstract: The disclosure herein provides methods, systems, and devices for automated reorientation and/or analysis of medical scans and/or images. The methods, systems, and devices for automated analysis of medical scans can be configured to mark, score, grade, and/other otherwise classify medical scans that are more time-sensitive, severe, and/or the like to allow a medical professional reviewing and/or analyzing medical scans to view and/or analyze such scans more efficiently by using a common image orientation and/or taking into account knowledge of the risk of severity, time-sensitiveness, and/or other priority.

Abstract: Methods, devices and systems are disclosed for measuring biological tissue parameters using restriction spectrum magnetic resonance imaging. In one aspect, a method of characterizing a biological structure includes determining individual diffusion signals from magnetic resonance imaging (MRI) data in a set of MRI images that include diffusion weighting conditions (e.g.