A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning.
A unifying probabilistic Bayesian approach to derive electron density from MRI for radiation therapy treatment planning.
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DOI：
10.1088/0031-9155/59/21/6595
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发表时间：
2014-11-07
影响因子：
3.5
通讯作者：
Li R
中科院分区：
文献类型：
--
作者：
Gudur MS;Hara W;Le QT;Wang L;Xing L;Li R
与 CT 相比，MRI 具有优越的软组织对比度，可显着提高某些肿瘤放射治疗中靶区描绘的准确性和可靠性。以 MRI 作为唯一成像方式的治疗计划流程将消除系统性 CT/MRI 联合配准错误，降低成本和辐射暴露，并简化临床工作流程。然而，MRI 缺乏精确剂量计算和生成患者设置参考图像所需的关键电子密度信息。这项工作的目的是开发一种统一的方法，从标准 T1 加权 MRI 中导出电子密度。我们建议将强度和几何信息结合到电子密度映射的统一概率贝叶斯框架中。对于每个体素，我们根据以下条件计算电子密度的两个条件概率密度函数 (PDF)：(1) T1 加权 MRI 强度，以及 (2) 参考解剖结构中的几何形状，通过图谱 MRI 和测试患者之间的可变形图像配准获得。将包含强度和几何信息的两个条件PDF组合成统一的后验PDF，其平均值对应于均方误差准则下的最佳电子密度值。我们使用另一名患者作为图集或模板，评估了该算法的电子密度图准确性及其检测 8 名患者头部骨骼的能力。计算估计 CT 与真实 CT 之间的平均绝对 HU 误差，以及骨检测的 ROC (HU>200)。该性能与基于 T1 且无密度校正（将整个水头设置为水）的全局强度方法进行了比较。所提出的技术显着减少了电子密度估计的误差，平均绝对 HU 误差为 126，而变形配准 (p=2×10-4) 的平均绝对 HU 误差为 139，强度方法 (p=2×10-6) 的 283，无密度校正的 282 (p=5×10-6)。对于 90% 的骨检测灵敏度，所提出的方法实现了 86% 的特异性，而使用变形配准、强度和无密度校正的特异性分别为 80%、11% 和 10%。值得注意的是，贝叶斯方法对于患者之间的解剖差异更加稳健，在最坏的情况（患者）中特异性为 62%，而基于配准的方法的特异性为 30%。总之，所提出的统一贝叶斯方法提供了高度异质解剖结构的头部 MRI 的精确电子密度估计和骨骼检测。
MRI significantly improves the accuracy and reliability of target delineation in radiation therapy for certain tumors due to its superior soft tissue contrast compared to CT. A treatment planning process with MRI as the sole imaging modality will eliminate systematic CT/MRI co-registration errors, reduce cost and radiation exposure, and simplify clinical workflow. However, MRI lacks the key electron density information necessary for accurate dose calculation and generating reference images for patient setup. The purpose of this work is to develop a unifying method to derive electron density from standard T1-weighted MRI. We propose to combine both intensity and geometry information into a unifying probabilistic Bayesian framework for electron density mapping. For each voxel, we compute two conditional probability density functions (PDFs) of electron density given its: (1) T1-weighted MRI intensity, and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of the atlas and test patient. The two conditional PDFs containing intensity and geometry information are combined into a unifying posterior PDF, whose mean value corresponds to the optimal electron density value under the mean-square error criterion. We evaluated the algorithm's accuracy of electron density mapping and its ability to detect bone in the head for 8 patients, using an additional patient as the atlas or template. Mean absolute HU error between the estimated and true CT, as well as ROC's for bone detection (HU>200) were calculated. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water). The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 126, compared with 139 for deformable registration (p=2×10-4), 283 for the intensity approach (p=2×10-6) and 282 without density correction (p=5×10-6). For 90% sensitivity in bone detection, the proposed method achieved a specificity of 86%, compared with 80%, 11% and 10% using deformable registration, intensity and without density correction, respectively. Notably, the Bayesian approach was more robust against anatomical differences between patients, with a specificity of 62% in the worst case (patient), compared to 30% specificity in registration-based approach. In conclusion, the proposed unifying Bayesian method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous anatomy.
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