Collins D L, Zijdenbos A P, Kollokian V, Sled J G, Kabani N J, Holmes C J, Evans A C
Montréal Neurological Institute, McGill University, McConnell Brain Imaging Centre, Canada.
IEEE Trans Med Imaging. 1998 Jun;17(3):463-8. doi: 10.1109/42.712135.
After conception and implementation of any new medical image processing algorithm, validation is an important step to ensure that the procedure fulfills all requirements set forth at the initial design stage. Although the algorithm must be evaluated on real data, a comprehensive validation requires the additional use of simulated data since it is impossible to establish ground truth with in vivo data. Experiments with simulated data permit controlled evaluation over a wide range of conditions (e.g., different levels of noise, contrast, intensity artefacts, or geometric distortion). Such considerations have become increasingly important with the rapid growth of neuroimaging, i.e., computational analysis of brain structure and function using brain scanning methods such as positron emission tomography and magnetic resonance imaging. Since simple objects such as ellipsoids or parallelepipedes do not reflect the complexity of natural brain anatomy, we present the design and creation of a realistic, high-resolution, digital, volumetric phantom of the human brain. This three-dimensional digital brain phantom is made up of ten volumetric data sets that define the spatial distribution for different tissues (e.g., grey matter, white matter, muscle, skin, etc.), where voxel intensity is proportional to the fraction of tissue within the voxel. The digital brain phantom can be used to simulate tomographic images of the head. Since the contribution of each tissue type to each voxel in the brain phantom is known, it can be used as the gold standard to test analysis algorithms such as classification procedures which seek to identify the tissue "type" of each image voxel. Furthermore, since the same anatomical phantom may be used to drive simulators for different modalities, it is the ideal tool to test intermodality registration algorithms. The brain phantom and simulated MR images have been made publicly available on the Internet (http://www.bic.mni.mcgill.ca/brainweb).
在任何新的医学图像处理算法构思并实施之后，验证是确保该程序满足初始设计阶段所设定的所有要求的重要步骤。尽管算法必须在真实数据上进行评估，但全面的验证还需要额外使用模拟数据，因为无法通过体内数据确定真实情况。使用模拟数据进行实验能够在广泛的条件下（例如不同水平的噪声、对比度、强度伪影或几何失真）进行可控评估。随着神经成像（即使用正电子发射断层扫描和磁共振成像等脑部扫描方法对脑结构和功能进行计算分析）的迅速发展，这些考量变得愈发重要。由于诸如椭球体或平行六面体等简单物体无法反映自然脑解剖结构的复杂性，我们展示了一种逼真、高分辨率、数字化的人脑体积模型的设计与创建。这个三维数字脑模型由十个体积数据集组成，这些数据集定义了不同组织（例如灰质、白质、肌肉、皮肤等）的空间分布，其中体素强度与体素内组织的比例成正比。该数字脑模型可用于模拟头部的断层图像。由于已知脑模型中每种组织类型对每个体素的贡献，它可以用作黄金标准来测试诸如分类程序等分析算法，这些算法旨在识别每个图像体素的组织“类型”。此外，由于相同的解剖模型可用于驱动不同模态的模拟器，它是测试模态间配准算法的理想工具。脑模型和模拟的磁共振图像已在互联网上公开提供（http://www.bic.mni.mcgill.ca/brainweb）。
