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    • 5. 发明授权
    • Method for determining a property map of an object, particularly of a living being, based on at least a first image, particularly a magnetic resonance image
    • 基于至少第一图像,特别是磁共振图像来确定对象,特别是生物的属性图的方法
    • US08290568B2
    • 2012-10-16
    • US12351429
    • 2009-01-09
    • Bernd PichlerMatthias HofmannBernhard SchölkopfFlorian Steinke
    • Bernd PichlerMatthias HofmannBernhard SchölkopfFlorian Steinke
    • A61B5/05
    • G01R33/481A61B6/037G06T7/33G06T2207/10088G06T2207/30004G06T2207/30196
    • It is disclosed a system and method (12) for determining a property map (82) of an object, particularly a human being, based on at least a first image (84), particularly an magnetic resonance (MR) image, of the object. In the method (12), a structure of reference pairs is defined in a first step (96), wherein each reference pair (16-26) comprises at least two entries (62). The first entry represents a property value, particularly an attenuation value. The second entry (62) preferably represents a group of image points (67) belonging together, which is extracted particularly from MR images (28) and comprises an interesting image point corresponding to the property value. In another step (98) of the method (12) a plurality of training pairs (16-26) is provided. A structure of the training pairs (16-26) corresponds to the structure of reference pairs, and the entries of respective training pairs (16-26) are known. In another step (100) of the method (12), an assignment between the first entries and the other entries (62-66) of the training pairs (16-26) is determined by machine learning, thus allowing prediction of a property value (88) corresponding to an arbitrary point (90) of the first image (84).
    • 公开了一种用于基于至少第一图像(84),特别是磁共振(MR)图像来确定对象,特别是人类的属性图(82)的系统和方法(12) 。 在方法(12)中,在第一步骤(96)中定义参考对的结构,其中每个参考对(16-26)包括至少两个条目(62)。 第一项表示属性值,特别是衰减值。 第二条目(62)优选地表示属于一组的一组图像点(67),其特别是从MR图像(28)提取并包括与该属性值对应的有趣图像点。 在方法(12)的另一步骤(98)中,提供了多个训练对(16-26)。 训练对(16-26)的结构对应于参考对的结构,并且各训练对(16-26)的条目是已知的。 在方法(12)的另一步骤(100)中,训练对(16-26)的第一条目和其他条目(62-66)之间的分配由机器学习确定,从而允许预测属性值 (88)对应于第一图像(84)的任意点(90)。