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设计并构建一个模拟新生儿磁共振成像的脑模型。

Design and construction of a brain phantom to simulate neonatal MR images.

机构信息

Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran.

出版信息

Comput Med Imaging Graph. 2011 Apr;35(3):237-50. doi: 10.1016/j.compmedimag.2010.11.007. Epub 2010 Dec 13.

Abstract

This paper presents the design and construction of a 3D digital neonatal neurocranial phantom and its application for the simulation of brain magnetic resonance (MR) images. Commonly used digital brain phantoms (e.g. BrainWeb) are based on the adult brain. With the growing interest in computer-aided methods for neonatal MR image processing, there is a growing demand a digital phantom and brain MR image simulator especially for the neonatal brains. This is due to the pronounced differences between adult and neonatal brains not only in terms of size but also, more importantly, in terms of geometrical proportions and the need to subdivide white matter into two different tissue types in neonates. Therefore the neonatal brain phantom created in the here presented work consists of 9 different tissue types: skin, fat, muscle, skull, dura mater, gray matter, myelinated white matter, nonmyelinated white matter and cerebrospinal fluid. Each voxel has a vector consisting of 9 components, one for each of these nine tissue types. This digital phantom can be used to map simulated magnetic resonance signal intensities resulting in simulated MR images of the newborns head. These images with controlled degradation of the image data present a representative, reproducible data set ideal for development and evaluation of neonatal MRI analysis methods, e.g. segmentation and registration algorithms.

摘要

本文提出了一种 3D 数字新生儿神经颅像的设计和构建及其在脑磁共振(MR)图像模拟中的应用。常用的数字脑模型(例如 BrainWeb)基于成人脑。随着对计算机辅助新生儿 MR 图像处理方法的兴趣日益增加,对专门用于新生儿脑的数字模型和脑磁共振图像模拟器的需求也在不断增长。这是由于成人和新生儿脑之间不仅在大小方面,而且在几何比例方面存在明显差异,并且需要将白质细分为两种不同的组织类型。因此,本研究中创建的新生儿脑模型由 9 种不同的组织类型组成:皮肤、脂肪、肌肉、颅骨、硬脑膜、灰质、髓鞘化白质、非髓鞘化白质和脑脊液。每个体素都有一个由 9 个分量组成的向量,分别对应于这 9 种组织类型。这个数字模型可以用于模拟磁共振信号强度,从而生成新生儿头部的模拟磁共振图像。这些具有图像数据可控退化的图像提供了一个具有代表性的、可重复的数据集,非常适合开发和评估新生儿 MRI 分析方法,例如分割和配准算法。

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