Multipeak fat-corrected complex R2* relaxometry: theory, optimization, and clinical validation.
Multipeak fat-corrected complex R2* relaxometry: theory, optimization, and clinical validation.
复制标题
DOI：
10.1002/mrm.24593
复制
发表时间：
2013-11
影响因子：
3.3
通讯作者：
Reeder, Scott B.
中科院分区：
文献类型：
--
作者：
Hernando, Diego;Kramer, J. Harald;Reeder, Scott B.
开发R2*映射技术，纠正混杂因素并优化噪声性能。传统的R2*映射受到两个关键混杂因素的影响：噪声相关偏差和组织中脂肪的存在。噪声底效应会在基于震级的重建中引入偏置，特别是在高R2*值时。脂肪的存在，如果不加以纠正，会引入严重的方案依赖性偏倚。在这项工作中，使用Cramer-Rao界分析、模拟和体内数据表征了不同R2*映射重建（幅度和复杂拟合、脂肪未校正和脂肪校正）的偏置/噪声特性。提供了一个优化回声时间选择的框架。最后，在28名受试者中评估了存在脂肪的肝脏R2*作图的稳健性。脂肪校正的R2*映射消除了脂肪相关的偏差，没有噪声惩罚在R2*值的大范围内。复杂的非线性最小二乘拟合和脂肪校正的R2*重建，考虑到脂肪的光谱复杂性，在大范围的回波时间组合和R2*值下，提供了低偏差和优化噪声性能的鲁棒R2*估计。使用复杂的拟合和脂肪校正提高了R2*测量的鲁棒性、噪声性能和准确性，并且是建立R2*作为肝脏定量成像生物标志物所必需的。
To develop R2* mapping techniques corrected for confounding factors and optimized for noise performance. Conventional R2* mapping is affected by two key confounding factors: noise-related bias and the presence of fat in tissue. Noise floor effects introduce bias in magnitude-based reconstructions, particularly at high R2* values. The presence of fat, if uncorrected, introduces severe protocol-dependent bias. In this work, the bias/noise properties of different R2* mapping reconstructions (magnitude-and complex-fitting, fat-uncorrected, and fat-corrected) are characterized using Cramer-Rao Bound analysis, simulations, and in vivo data. A framework for optimizing the choice of echo times is provided. Finally, the robustness of liver R2* mapping in the presence of fat is evaluated in 28 subjects. Fat-corrected R2* mapping removes fat-related bias without noise penalty over a wide range of R2* values. Complex nonlinear least-squares fitted and fat-corrected R2* reconstructions that account for the spectral complexity of fat provide robust R2* estimates with low bias and optimized noise performance over a wide range of echo times combinations and R2* values. The use of complex fitting and fat-correction improves the robustness, noise performance, and accuracy of R2* measurements, and are necessary to establish R2* as quantitative imaging biomarker in the liver.
登录
查看更多内容
影响因子：
3.3
作者：
Hernando, Diego;Liang, Zhi-Pei;Kellman, Peter
通讯作者：
Kellman, Peter
影响因子：
3.3
作者：
Hernando, D.;Hines, C. D. G.;Yu, H.;Reeder, S. B.
通讯作者：
Reeder, S. B.
影响因子：
4.4
作者：
Hines, Catherine D. G.;Agni, Rashmi;Roen, Calista;Rowland, Ian;Hernando, Diego;Bultman, Eric;Horng, Debra;Yu, Huanzhou;Shimakawa, Ann;Brittain, Jean H.;Reeder, Scott B.
通讯作者：
Reeder, Scott B.
影响因子：
3.3
作者：
Kellman, Peter;Hernando, Diego;Shah, Saurabh;Zuehlsdorff, Sven;Jerecic, Renate;Mancini, Christine;Liang, Zhi-Pei.;Arai, Andrew E.
通讯作者：
Arai, Andrew E.
影响因子：
3.3
作者：
Dahnke, H;Schaeffter, T
通讯作者：
Schaeffter, T