Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universidad Politécnica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain.
Neuroimage. 2010 Nov 1;53(2):480-90. doi: 10.1016/j.neuroimage.2010.06.046. Epub 2010 Jun 25.
This paper addresses the problem of accurate voxel-level estimation of tissue proportions in the human brain magnetic resonance imaging (MRI). Due to the finite resolution of acquisition systems, MRI voxels can contain contributions from more than a single tissue type. The voxel-level estimation of this fractional content is known as partial volume coefficient estimation. In the present work, two new methods to calculate the partial volume coefficients under noisy conditions are introduced and compared with current similar methods. Concretely, a novel Markov Random Field model allowing sharp transitions between partial volume coefficients of neighbouring voxels and an advanced non-local means filtering technique are proposed to reduce the errors due to random noise in the partial volume coefficient estimation. In addition, a comparison was made to find out how the different methodologies affect the measurement of the brain tissue type volumes. Based on the obtained results, the main conclusions are that (1) both Markov Random Field modelling and non-local means filtering improved the partial volume coefficient estimation results, and (2) non-local means filtering was the better of the two strategies for partial volume coefficient estimation.
本文针对人脑磁共振成像（MRI）中组织比例的精确体素级估计问题。由于采集系统的分辨率有限，MRI 体素可能包含来自多种组织类型的贡献。这种分数含量的体素级估计称为部分体积系数估计。在本工作中，引入了两种新的方法来计算噪声条件下的部分体积系数，并与当前类似的方法进行了比较。具体来说，提出了一种新的马尔可夫随机场模型，允许相邻体素的部分体积系数之间有明显的转变，以及一种先进的非局部均值滤波技术，以减少部分体积系数估计中随机噪声引起的误差。此外，还进行了比较，以了解不同方法学如何影响脑组织结构体积的测量。基于获得的结果，得出的主要结论是：(1)马尔可夫随机场建模和非局部均值滤波都改善了部分体积系数估计结果，(2)非局部均值滤波是两种策略中用于部分体积系数估计的更好策略。
