EXTEND SCOPE & LEVEL OF DETAIL OF ANATOMICAL MODELS FROM IN VIVO DATA

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目及 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者的研究机构。 多回波闪光（MEF）成像序列的一个重要优点是，它为各种大脑结构提供了更好的对比度信息，从而允许对这些结构进行更详细的分析。 作为利用丰富的对比度信息的第一种方法，我们采用子空间投影方法从初始MEF数据中生成最佳加权图像，以区分不同的结构。由此产生的图像可以用来更好地描绘解剖结构的细微边界，从而帮助建立更详细的大脑解剖模型。 为此，我们采用了线性判别分析（LDA）方法，该方法将原始高维数据线性投影到低维子空间中，同时优化减少的数据空间中的对比度噪声比。 初步结果表明，该技术有助于区分结构，包括外部（GPe）和内部苍白球（GPi），丘脑，以及硬脑膜和脂肪的大脑。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. One important advantage of the multi-echo flash (MEF) imaging sequence is that it provides better contrast information for various brain structures, and thus allows more detailed analysis of these structures. As the first approach to make use of the rich contrast information, we worked on a subspace projection method to generate optimally weighted images from the initial MEF data for differentiating different structures. The resulting images can be used to get better delineation of subtle boundaries of anatomical structures, and thus help build more detailed models of brain anatomy. To this end, we have adopted a linear discriminant analysis (LDA) method, which finds a linear projection of the original high-dimensional data into a lower-dimensional subspace while optimizing the contrast-to-noise ratio in the reduced data-space. Preliminary results, in which we used manually labeled data to compute the optimal projections for differentiating different brain structures, demonstrate that the technique helps to differentiate structures including the external (GPe) and internal pallidum (GPi), thalamus, and the brain from dura and fat.