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Pharmacokinetic modeling of dynamic MR images using a simulated annealing-based optimization

机译:使用模拟退火的优化的动态MR图像的药代动力学建模

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The aim of this work was to use dynamic contrast enhanced MR image (DEMRI) data to generate 'parameter images' which provide functional information about contrast agent access, in bone sarcoma. A simulated annealing based technique was applied to optimize the parameters of a pharmacokinetic model used to describe the kinetics of the tissue response during and after intravenous infusion of a paramagnetic contrast medium, Gd-DTPA. Optimization was performed on a pixel by pixel basis so as to minimize the sum of square deviations of the calculated values from the values obtained experimentally during dynamic contrast enhanced MR imaging. A cost function based on a priori information was introduced during the annealing procedure to ensure that the values obtained were within the expected ranges. The optimized parameters were used in the model to generate parameter images, which reveal functional information that is normally not visible in conventional Gd-DTPA enhanced MR images. This functional information, during and upon completion of pre-operative chemotherapy, is useful in predicting the probability of disease free survival.
机译:这项工作的目的是使用动态对比增强的MR图像(DEMRI)数据来生成“参数图像”,它提供有关骨骼肉瘤中的造影剂访问的功能信息。应用了基于模拟的基于退火的技术来优化用于描述静脉注射顺磁造影剂的组织响应动力学的药代动力学模型的参数,GD-DTPA。通过像素的基础上对像素进行优化,以便在动态对比度增强MR成像期间通过实验获得的值来最小化计算值的平方偏差之和。在退火过程中引入了基于先验信息的成本函数,以确保获得的值在预期的范围内。在模型中使用优化的参数来生成参数图像,该参数图像显示通常在传统GD-DTPA增强型MR图像中通常不可见的功能信息。这种功能信息,期间和完成前术前化疗,可用于预测无疾病的缺陷存活率。

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