A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images
A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images
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DOI：
10.1109/tmi.2013.2281719
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发表时间：
2014-01-01
影响因子：
10.6
通讯作者：
Sahani, Dushyant V.
中科院分区：
文献类型：
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作者：
Mendonca, Paulo R. S.;Lamb, Peter;Sahani, Dushyant V.
双能量计算机断层扫描(CT)系统能够确定混合物中组成物质的浓度，即物质分解，这是双能量CT许多临床应用的基础。然而，人体组织和器官的复杂组成对许多材料分解方法提出了挑战，这些方法假设混合物中只存在两种或至多三种材料。我们开发了一种灵活的、基于模型的方法，扩展了双能CT的核心材料分解能力，以处理更复杂的情况，在这种情况下，必须消除歧义并量化大量材料的浓度。该方法被称为多材料分解(MMD)，用来开发两种图像分析算法。第一种是虚拟增强(VUE)，它从对比度增强的双能量CT检查中以数字方式消除造影剂的影响。VUE具有减少患者剂量和改善临床工作流程的能力，可用于CT尿路成像和CT血管成像等临床应用。开发的第二个算法是肝脏脂肪量化(LFQ)，它通过双能量CT检查精确地量化肝脏中的脂肪浓度。LFQ可以形成针对脂肪肝疾病的诊断和治疗的临床应用的基础。使用从由50名患者和模体组成的队列收集的图像数据，将MMD应用于VUE和LFQ，与金标准相比，产生了定量准确的结果。此外，在成像的所有阶段(无对比度和增强对比度)都获得了一致的结果。这一点特别重要，因为大多数腹部CT成像的临床方案都要求进行多时相成像。我们的结论是，MMD可以成功地形成一些双能量CT图像分析算法的基础，并具有提高双能量CT在疾病管理中的临床实用价值的潜力。
The ability of dual-energy computed-tomographic (CT) systems to determine the concentration of constituent materials in a mixture, known as material decomposition, is the basis for many of dual-energy CT's clinical applications. However, the complex composition of tissues and organs in the human body poses a challenge for many material decomposition methods, which assume the presence of only two, or at most three, materials in the mixture. We developed a flexible, model-based method that extends dual-energy CT's core material decomposition capability to handle more complex situations, in which it is necessary to disambiguate among and quantify the concentration of a larger number of materials. The proposed method, named multi-material decomposition (MMD), was used to develop two image analysis algorithms. The first was virtual unenhancement (VUE), which digitally removes the effect of contrast agents from contrast-enhanced dual-energy CT exams. VUE has the ability to reduce patient dose and improve clinical workflow, and can be used in a number of clinical applications such as CT urography and CT angiography. The second algorithm developed was liver-fat quantification (LFQ), which accurately quantifies the fat concentration in the liver from dual-energy CT exams. LFQ can form the basis of a clinical application targeting the diagnosis and treatment of fatty liver disease. Using image data collected from a cohort consisting of 50 patients and from phantoms, the application of MMD to VUE and LFQ yielded quantitatively accurate results when compared against gold standards. Furthermore, consistent results were obtained across all phases of imaging (contrast-free and contrast-enhanced). This is of particular importance since most clinical protocols for abdominal imaging with CT call for multi-phase imaging. We conclude that MMD can successfully form the basis of a number of dual-energy CT image analysis algorithms, and has the potential to improve the clinical utility of dual-energy CT in disease management.