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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >Statistical modeling and MAP estimation for body fat quantification with MRI ratio imaging
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Statistical modeling and MAP estimation for body fat quantification with MRI ratio imaging

机译：MRI比率成像对脂肪定量的统计建模和MAP估计

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We are developing small animal imaging techniques to characterize the kinetics of lipid accumulation/reduction of fat depots in response to genetic/dietary factors associated with obesity and metabolic syndromes. Recently, we developed an MR ratio imaging technique that approximately yields lipid/ {lipid + water}. In this work, we develop a statistical model for the ratio distribution that explicitly includes a partial volume (PV) fraction of fat and a mixture of a Rician and multiple Gaussians. Monte Carlo hypothesis testing showed that our model was valid over a wide range of coefficient of variation of the denominator distribution (c.v.: 0 - 0.20) and correlation coefficient among the numerator and denominator (ρ: 0 - 0.95), which cover the typical values that we found in MRI data sets (c.v.: 0.027 -0.063, ρ: 0.50-0.75). Then a maximum a posteriori (MAP) estimate for the fat percentage per voxel is proposed. Using a digital phantom with many PV voxels, we found that ratio values were not linearly related to PV fat content and that our method accurately described the histogram. In addition, the new method estimated the ground truth within +1.6% vs. +43% for an approach using an uncorrected ratio image, when we simply threshold the ratio image. On the six genetically obese rat data sets, the MAP estimate gave total fat volumes of 279 ±45mL, values ≈ 21% smaller than those from the uncorrected ratio images, principally due to the non-linear PV effect. We conclude that our algorithm can increase the accuracy of fat volume quantification even in regions having many PV voxels, e.g. ectopic fat depots.
机译：我们正在开发小型动物成像技术，以表征与肥胖症和代谢综合征相关的遗传/饮食因素引起的脂质蓄积/减少脂肪库的动力学。最近，我们开发了一种MR比成像技术，可大致产生脂质/ {脂质+水}。在这项工作中，我们为比率分布开发了一个统计模型，该模型明确包括脂肪的部分体积（PV）分数以及Rician和多个高斯的混合物。蒙特卡洛假设检验表明，我们的模型在分母分布的变异系数（cv：0-0.20）和分子与分母之间的相关系数（ρ：0-0.95）的大范围内均有效，涵盖了典型值我们在MRI数据集中发现（cv：0.027 -0.063，ρ：0.50-0.75）。然后，提出了每个体素的脂肪百分比的最大后验（MAP）估计。使用带有许多PV体素的数字体模，我们发现比率值与PV脂肪含量不是线性相关的，并且我们的方法可以准确地描述直方图。此外，当我们简单地对比率图像进行阈值处理时，对于使用未校正比率图像的方法，新方法估计的地面真理在+ 1.6％和+ 43％之间。在六个遗传性肥胖大鼠数据集上，MAP估计得出的总脂肪量为279±45mL，其值比未校正比例图像小≈21％，这主要归因于非线性PV效应。我们得出的结论是，即使在具有许多PV体素的区域，例如我们的算法，我们的算法也可以提高脂肪量定量的准确性。异位脂肪库。

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