Département de physique, Université de Montréal, 2900 boulevard Édouard-Montpetit, Montréal, QC, H3T 1J4, Canada.
Département de radio-oncologie, Centre hospitalier de l'Université de Montréal (CHUM), 1000 rue Saint-Denis, Montréal, Québec, H2X 0C1, Canada.
Med Phys. 2018 Jul;45(7):3086-3096. doi: 10.1002/mp.12934. Epub 2018 May 20.
The purpose of this study was to develop and validate accurate methods for determining iodine content and virtual noncontrast maps of physical parameters, such as electron density, in the context of radiotherapy.
A simulation environment is developed to compare three methods allowing extracting iodine content and virtual noncontrast composition: (a) two-material decomposition, (b) three-material decomposition with the conservation of volume constraint, and (c) eigentissue decomposition. The simulation allows comparing the performance of the methods using iodine-based contrast agent contents in tissues from a reference dataset with variable density and elemental composition. The comparison is performed in two ways: (a) with a priori knowledge on the composition of the targeted tissue, and (b) without a priori knowledge on the base tissue. The three methods are tested with patient images scanned with dual-energy CT and iodine-based contrast agent. An experimental calibration adapted to the presence of iodine is performed by imaging tissue equivalent materials and diluted contrast agent solutions with known atomic composition.
Results show that in the case of known a priori on the composition of the targeted tissue, the two-material decomposition is robust to variable densities and atomic compositions without biasing the results. In the absence of a priori knowledge on the target tissue composition, the eigentissue decomposition method yields minimal bias and higher robustness to variations. Results from the experimental calibration and the images of two patients show that the extracted quantities are accurate and the bias is negligible for both methods with respect to clinical applications in their respective scope of use. For the patient imaged with a contrast agent, virtual noncontrast electron densities are found in good agreement with values extracted from the scan without contrast agent.
This study identifies two accurate methods to quantify iodine-based contrast agents and virtual noncontrast composition images with dual-energy CT. One is the two-material decomposition with a priori knowledge of the constituent components focused on organ-specific applications, such as kidney or lung function assessment. The other method is the eigentissue decomposition and is useful for general radiotherapy applications, such as treatment planning where accurate dose calculations are needed in the absence of contrast agent.
本研究旨在开发和验证准确的方法，以确定放射治疗中碘含量和电子密度等物理参数的虚拟非对比图。
开发了一个模拟环境，以比较三种提取碘含量和虚拟非对比成分的方法：（a）两物质分解，（b）具有体积约束的三物质分解，和（c）特征组织分解。该模拟允许使用参考数据集（具有可变密度和元素组成的组织）中基于碘的对比剂含量来比较这些方法的性能。比较通过以下两种方式进行：（a）具有目标组织组成的先验知识，和（b）没有基组织的先验知识。使用双能 CT 和碘基对比剂扫描的患者图像对这三种方法进行了测试。通过对具有已知原子组成的组织等效材料和稀释对比剂溶液进行成像，进行了适应碘存在的实验校准。
结果表明，在已知目标组织组成的先验知识的情况下，两物质分解对可变密度和原子组成具有鲁棒性，而不会产生偏差。在没有目标组织组成的先验知识的情况下，特征组织分解方法产生的偏差最小，对变化的鲁棒性更高。实验校准和两名患者的图像结果表明，对于这两种方法，提取的数量都是准确的，对于其各自使用范围内的临床应用，偏差可以忽略不计。对于用造影剂成像的患者，虚拟非对比电子密度与无造影剂扫描提取的值吻合良好。
本研究确定了两种准确的方法，可使用双能 CT 定量碘基对比剂和虚拟非对比成分图像。一种是具有器官特异性应用（如肾功能或肺功能评估）的先验知识的两物质分解。另一种方法是特征组织分解，对于需要在没有对比剂的情况下进行准确剂量计算的一般放射治疗应用（如治疗计划）非常有用。
