{"title":"基于经验光谱校正的微ct光子计数探测器的迭代聚类材料分解。","authors":"J Carlos Rodriguez Luna, Mini Das","doi":"10.1117/1.JMI.11.S1.S12810","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>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.</p><p><strong>Approach: </strong>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.</p><p><strong>Results: </strong>Utilizing a (cadmium telluride 1-mm-thick sensor) Medipix detector with a <math><mrow><mn>55</mn> <mtext>-</mtext> <mi>μ</mi> <mi>m</mi></mrow> </math> 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.</p><p><strong>Conclusions: </strong>This ICMD allows for quantitative separation of multiple materials including mixtures and also effectively separates multi-contrast agents.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 Suppl 1","pages":"S12810"},"PeriodicalIF":1.9000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11676343/pdf/","citationCount":"0","resultStr":"{\"title\":\"Iterative clustering material decomposition aided by empirical spectral correction for photon counting detectors in micro-CT.\",\"authors\":\"J Carlos Rodriguez Luna, Mini Das\",\"doi\":\"10.1117/1.JMI.11.S1.S12810\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>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.</p><p><strong>Approach: </strong>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.</p><p><strong>Results: </strong>Utilizing a (cadmium telluride 1-mm-thick sensor) Medipix detector with a <math><mrow><mn>55</mn> <mtext>-</mtext> <mi>μ</mi> <mi>m</mi></mrow> </math> 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.</p><p><strong>Conclusions: </strong>This ICMD allows for quantitative separation of multiple materials including mixtures and also effectively separates multi-contrast agents.</p>\",\"PeriodicalId\":47707,\"journal\":{\"name\":\"Journal of Medical Imaging\",\"volume\":\"11 Suppl 1\",\"pages\":\"S12810\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11676343/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1117/1.JMI.11.S1.S12810\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/27 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Imaging","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1117/1.JMI.11.S1.S12810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/27 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0
摘要
目的:光子计数探测器在计算机断层扫描(CT)成像中提供了有希望的进步,通过同时从宽x射线光谱中进行多能投射,实现造影剂和组织类型的量化和三维成像。然而,这些分解方法的准确性取决于精确的复合光谱衰减值,必须从光谱微ct中重建。这种估计中的误差可能是由于光束硬化、物体散射或探测器传感器相关的光谱畸变(如荧光)等影响造成的。即使进行了精确的光谱校正,在一个体积内的多材料分离仍然是一个挑战。在材料分解问题中,增加能量箱的数量通常会带来显著的噪音惩罚,但分解效益却微乎其微。方法:我们从在层析域中执行的经验光谱校正方法开始,该方法考虑了每个体素估计的光谱衰减的扭曲。接下来是我们提出的迭代聚类材料分解(ICMD),其中使用体素聚类来减少每个聚类需要解析的基材料的数量。与美国国家标准与技术研究院(National Institute of Standards and Technology)的衰减值相比,在聚类步骤中使用更大数量的能量桶,对于具有准确聚类中心的更大数量的聚类具有明显的优势。分解步骤分别应用于每个簇,每个簇与整个体积相比具有更少的基材料。这可以减少对簇的每个分解步骤所需的能量箱数量的需求。这种方法显著增加了可以在体积内分解的材料总数,具有高精度和优异的噪声特性。结果:利用55 μ m间距的(1 mm厚的碲化镉传感器)Medipix探测器,我们在模拟研究中展示了几种材料的定量准确分解,其中样品包括软材料(如水和聚甲基丙烯酸甲酯)以及对比度增强材料的混合物。当使用所有五个能量桶将体素有效分类为多个准确的基本聚类时,我们显示出更高的准确性和更低的噪声,然后仅使用两个能量桶对每个聚类应用分解步骤。我们还展示了一个生物样本成像的例子,并在小鼠中分离了三种不同类型的组织:肌肉、脂肪和骨骼。实验结果表明,有效实用的光谱校正与高维数据聚类相结合,提高了微ct分解精度,降低了噪声。结论:该ICMD可以定量分离多种材料,包括混合物,也可以有效分离多种造影剂。
Iterative clustering material decomposition aided by empirical spectral correction for photon counting detectors in micro-CT.
Purpose: 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.
Approach: 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.
Results: 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.
Conclusions: This ICMD allows for quantitative separation of multiple materials including mixtures and also effectively separates multi-contrast agents.
期刊介绍:
JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.