Mason Jonathan H, Perelli Alessandro, Nailon William H, Davies Mike E
School of Engineering, Institute for Digital Communications, The University of Edinburgh, Edinburgh, EH9 3JL, United Kingdom.
Phys Med Biol. 2017 Nov 2;62(22):8739-8762. doi: 10.1088/1361-6560/aa9162.
Quantifying material mass and electron density from computed tomography (CT) reconstructions can be highly valuable in certain medical practices, such as radiation therapy planning. However, uniquely parameterising the x-ray attenuation in terms of mass or electron density is an ill-posed problem when a single polyenergetic source is used with a spectrally indiscriminate detector. Existing approaches to single source polyenergetic modelling often impose consistency with a physical model, such as water-bone or photoelectric-Compton decompositions, which will either require detailed prior segmentation or restrictive energy dependencies, and may require further calibration to the quantity of interest. In this work, we introduce a data centric approach to fitting the attenuation with piecewise-linear functions directly to mass or electron density, and present a segmentation-free statistical reconstruction algorithm for exploiting it, with the same order of complexity as other iterative methods. We show how this allows both higher accuracy in attenuation modelling, and demonstrate its superior quantitative imaging, with numerical chest and metal implant data, and validate it with real cone-beam CT measurements.
从计算机断层扫描（CT）重建中量化物质质量和电子密度在某些医学实践中具有很高的价值，例如放射治疗计划。然而，当使用单一多能源与光谱无差别探测器时，根据质量或电子密度对X射线衰减进行唯一参数化是一个不适定问题。现有的单源多能建模方法通常与物理模型保持一致，如水骨或光电-康普顿分解，这要么需要详细的先验分割，要么需要严格的能量依赖性，并且可能需要进一步校准感兴趣的量。在这项工作中，我们引入了一种以数据为中心的方法，直接用分段线性函数将衰减拟合到质量或电子密度，并提出了一种无分割的统计重建算法来利用它，其复杂度与其他迭代方法相同。我们展示了这如何在衰减建模中实现更高的精度，并通过数值胸部和金属植入物数据展示其卓越的定量成像，并通过实际锥束CT测量对其进行验证。
