公开(公告)号：US20140071125A1

公开(公告)日：2014-03-13

申请号：US13609476

申请日：2012-09-11

申请人： Philippe M. Burlina ,  Ryan N. Mukherjee ,  Chad R. Sprouse

发明人： Philippe M. Burlina ,  Ryan N. Mukherjee ,  Chad R. Sprouse

IPC分类号： G06T17/00

CPC分类号： G06T7/00 ,  G06T7/246 ,  G06T17/00 ,  G06T2207/10076 ,  G06T2207/10136 ,  G06T2207/30048

摘要： Methods and systems to analyze and predict patient-specific physiological behavior of a human organ or anatomical entity such as the heart complex and the heart subcomponents from 3-dimensional volumetric ultrasound (3D) or time-sequential volumetric (4D) ultrasound image data, to assist physicians in performing diagnostics and cardiac preoperative planning. Also disclosed herein are methods and systems to segment patient-specific anatomical features from 3D/4D ultrasound. Also disclosed herein are methods and systems to compute patient-specific tissue motion and blood flow from 3D/4D ultrasound and contrast-enhanced 3D/4D ultrasound image data. Also disclosed herein are methods and systems to simulate the patient-specific mechanical behavior of the organ and anatomical entity using both 3D/4D ultrasound and mechanical models. Also disclosed herein are methods and systems to estimate tissue stress and strain and physiological parameters of the tissues from 3D/4D ultrasound.

摘要翻译： 用于分析和预测来自三维体积超声（3D）或时间顺序体积（4D）超声图像数据的人体器官或解剖实体（例如心脏复合体和心脏子部分）的患者特异性生理行为的方法和系统， 协助医生执行诊断和心脏手术前计划。 本文还公开了从3D / 4D超声分割患者特异性解剖特征的方法和系统。 本文还公开了从3D / 4D超声和对比度增强的3D / 4D超声图像数据计算患者特异性组织运动和血流的方法和系统。 本文还公开了使用3D / 4D超声和机械模型来模拟器官和解剖实体的患者特异性机械行为的方法和系统。 本文还公开了从3D / 4D超声估计组织的组织应力和应变和生理参数的方法和系统。

公开(公告)号：US20130114868A1

公开(公告)日：2013-05-09

申请号：US13626301

申请日：2012-09-25

申请人： Philippe M. Burlina ,  Amit Banerjee ,  Saurabh Vyas ,  Luis Garza

发明人： Philippe M. Burlina ,  Amit Banerjee ,  Saurabh Vyas ,  Luis Garza

IPC分类号： G06K9/78

CPC分类号： A61B5/0075 ,  A61B5/444 ,  G06K2009/4657 ,  G06T7/0012 ,  G06T2207/10048 ,  G06T2207/20081 ,  G06T2207/30088

摘要： A method of detecting a skin condition may include employing a multiband hyperspectral sensor to obtain multi-spectral data, employing the multi-spectral data to map constitutive skin parameters to corresponding spectral signatures via a forward model that enables generation of a set of samples including a plurality of parameters mapped to a plurality of spectral signatures, utilizing the set of samples to employ machine learning to generate an inverse model to enable mapping of a spectral signature of skin of a patient to corresponding skin parameters, estimatingconstitutive skin parameters of the skin of the patient based on the inverse model, and determining a distribution of the constitutive parameters for one or more skin locations.

摘要翻译： 检测皮肤状况的方法可以包括采用多频带高光谱传感器来获得多光谱数据，采用多光谱数据通过正向模型将组成​​型皮肤参数映射到相应的光谱特征，所述前向模型能够生成一组样本，包括 多个参数映射到多个光谱特征，利用该组样本来采用机器学习来生成逆模型，以使得能够将患者的皮肤的光谱特征映射到相应的皮肤参数，估计皮肤的皮肤组织的皮肤参数 基于逆模型的患者，以及确定一个或多个皮肤位置的本构参数的分布。