Luna J Carlos Rodriguez, Das Mini
University of Houston, Department of Physics, Houston, Texas, United States.
J Med Imaging (Bellingham). 2024 Dec;11(Suppl 1):S12810. doi: 10.1117/1.JMI.11.S1.S12810. Epub 2024 Dec 27.
Photon counting detectors offer promising advancements in computed tomography (CT) imaging by enabling the quantification and three-dimensional imaging of contrast agents and tissue types through simultaneous multi-energy projections from broad X-ray spectra. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectral micro-CT. Errors in such estimations could be due to effects such as beam hardening, object scatter, or detector sensor-related spectral distortions such as fluorescence. Even if accurate spectral correction is done, multi-material separation within a volume remains a challenge. Increasing the number of energy bins in material decomposition problems often comes with a significant noise penalty but with minimal decomposition benefits.
We begin with an empirical spectral correction method executed in the tomographic domain that accounts for distortions in estimated spectral attenuation for each voxel. This is followed by our proposed iterative clustering material decomposition (ICMD) where clustering of voxels is used to reduce the number of basis materials to be resolved for each cluster. Using a larger number of energy bins for the clustering step shows distinct advantages in excellent classification to a larger number of clusters with accurate cluster centers when compared with the National Institute of Standards and Technology attenuation values. The decomposition step is applied to each cluster separately where each cluster has fewer basis materials compared with the entire volume. This is shown to reduce the need for the number of energy bins required in each decomposition step for the clusters. This approach significantly increases the total number of materials that can be decomposed within the volume with high accuracy and with excellent noise properties.
Utilizing a (cadmium telluride 1-mm-thick sensor) Medipix detector with a pitch, we demonstrate the quantitatively accurate decomposition of several materials in a phantom study, where the sample includes mixtures of soft materials such as water and poly-methyl methacrylate along with contrast-enhancing materials. We show improved accuracy and lower noise when all five energy bins were used to yield effective classification of voxels into multiple accurate fundamental clusters which was followed by the decomposition step applied to each cluster using just two energy bins. We also show an example of biological sample imaging and separating three distinct types of tissue in mice: muscle, fat, and bone. Our experimental results show that the combination of effective and practical spectral correction and high-dimensional data clustering enhances decomposition accuracy and reduces noise in micro-CT.
This ICMD allows for quantitative separation of multiple materials including mixtures and also effectively separates multi-contrast agents.
光子计数探测器通过从宽X射线光谱进行同步多能量投影，实现造影剂和组织类型的定量及三维成像，为计算机断层扫描（CT）成像带来了有前景的进展。然而，这些分解方法的准确性取决于必须从光谱显微CT重建的精确复合光谱衰减值。此类估计中的误差可能源于束硬化、物体散射或探测器传感器相关的光谱失真（如荧光）等效应。即使进行了精确的光谱校正，在一个体积内进行多材料分离仍然是一项挑战。在材料分解问题中增加能量 bins 的数量通常会带来显著的噪声惩罚，但分解效果却微乎其微。
我们首先采用一种在断层扫描域中执行的经验光谱校正方法，该方法考虑了每个体素估计光谱衰减中的失真。接下来是我们提出的迭代聚类材料分解（ICMD）方法，其中体素聚类用于减少每个聚类中要解析的基础材料数量。与美国国家标准与技术研究院的衰减值相比，在聚类步骤中使用更多数量的能量 bins 显示出明显优势，能够以准确的聚类中心对更多聚类进行出色分类。分解步骤分别应用于每个聚类，与整个体积相比，每个聚类中的基础材料数量更少。这表明减少了每个聚类分解步骤所需的能量 bins 数量。这种方法显著提高了能够在体积内高精度且具有出色噪声特性分解的材料总数。
使用具有特定间距的（碲化镉1毫米厚传感器）Medipix探测器，我们在体模研究中展示了几种材料的定量准确分解，其中样品包括水和聚甲基丙烯酸甲酯等软材料的混合物以及造影增强材料。当使用所有五个能量 bins 对体素进行有效分类，形成多个准确的基本聚类，然后仅使用两个能量 bins 对每个聚类应用分解步骤时，我们展示了更高的准确性和更低的噪声。我们还展示了一个生物样品成像的例子，并在小鼠中分离出三种不同类型的组织：肌肉、脂肪和骨骼。我们的实验结果表明，有效且实用的光谱校正与高维数据聚类相结合可提高显微CT中的分解准确性并降低噪声。
这种ICMD方法能够对包括混合物在内的多种材料进行定量分离，并且还能有效分离多种造影剂。
