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采用新鲜动物组织和成分优化的组织等效样本对基于双能 CT 的成分分析进行验证。

Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples.

机构信息

Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany.

Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America.

出版信息

Phys Med Biol. 2024 Aug 12;69(16):165033. doi: 10.1088/1361-6560/ad68bc.

DOI:10.1088/1361-6560/ad68bc
PMID:39074494
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11334240/
Abstract

Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton range. To fully utilize these methods, an estimate of the elemental composition of the patient's tissue is often needed.A published dual-energy computed tomography (DECT)-based composition-extraction algorithm was validated against reference compositions obtained with two independent methods. For this purpose, a set of phantoms containing either fresh porcine tissue or tissue-mimicking samples with known, realistic compositions were imaged with a CT scanner at two different energies. Then, the prompt gamma-ray (PG) signal during proton irradiation was measured with a PG detector prototype. The PG workflow used pre-calculated Monte Carlo simulations to obtain an optimized estimate of the sample's carbon and oxygen contents. The compositions were also assessed with chemical combustion analysis (CCA), and the stopping-power ratio (SPR) was measured with a multi-layer ionization chamber. The DECT images were used to calculate SPR-, density- and elemental composition maps, and to assign voxel-wise compositions from a selection of human tissues. For a more comprehensive set of reference compositions, the original selection was extended by 135 additional tissues, corresponding to spongiosa, high-density bones and low-density tissues.The root-mean-square error for the soft tissue carbon and oxygen content was 8.5 wt% and 9.5 wt% relative to the CCA result and 2.1 wt% and 10.3 wt% relative to the PG result. The phosphorous and calcium content were predicted within 0.4 wt% and 1.1 wt% of the CCA results, respectively. The largest discrepancies were encountered in samples whose composition deviated the most from tabulated compositions or that were more inhomogeneous.Overall, DECT-based composition estimations of relevant elements were in equal or better agreement with the ground truth than the established SECT-approach and could contribute todose verification measurements.

摘要

质子治疗允许高度适形的剂量沉积,但对射程不确定性敏感。目前正在开发的几种方法通过测量与组成相关的二次辐射来监测递送的质子射程。为了充分利用这些方法,通常需要估计患者组织的元素组成。

已发布的基于双能 CT(DECT)的组成提取算法已针对使用两种独立方法获得的参考组成进行了验证。为此,使用 CT 扫描仪在两种不同能量下对一组含有新鲜猪组织或具有已知真实组成的组织模拟样本的体模进行成像。然后,使用 PG 探测器原型测量质子辐照期间的瞬发伽马(PG)信号。PG 工作流程使用预计算的蒙特卡罗模拟来获得样品碳和氧含量的优化估计。还使用化学燃烧分析(CCA)评估了组成,并用多层电离室测量了阻止本领比(SPR)。DECT 图像用于计算 SPR、密度和元素组成图,并从选定的人体组织中分配体素组成。为了获得更全面的参考组成数据集,原始选择扩展了 135 种额外的组织,包括松质骨、高密度骨和低密度组织。

软组织中碳和氧含量的均方根误差相对于 CCA 结果分别为 8.5 wt%和 9.5 wt%,相对于 PG 结果分别为 2.1 wt%和 10.3 wt%。磷和钙含量相对于 CCA 结果的预测值分别在 0.4 wt%和 1.1 wt%以内。在组成与表格组成偏差最大或更不均匀的样本中遇到了最大的差异。

总体而言,基于 DECT 的相关元素组成估计与既定 SECT 方法相比,与真实值的一致性相等或更好,并且可以为剂量验证测量做出贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a0d/11334240/d475f39648cd/pmbad68bcf4_lr.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a0d/11334240/369648665041/pmbad68bcf1_lr.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a0d/11334240/02434dc63182/pmbad68bcf2_lr.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a0d/11334240/c336f05b1f64/pmbad68bcf3_lr.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a0d/11334240/d475f39648cd/pmbad68bcf4_lr.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a0d/11334240/369648665041/pmbad68bcf1_lr.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a0d/11334240/02434dc63182/pmbad68bcf2_lr.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a0d/11334240/c336f05b1f64/pmbad68bcf3_lr.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a0d/11334240/d475f39648cd/pmbad68bcf4_lr.jpg

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本文引用的文献

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2
Assessment of quantitative information for radiation therapy at a first-generation clinical photon-counting computed tomography scanner.在第一代临床光子计数计算机断层扫描扫描仪上对放射治疗的定量信息进行评估。
Front Oncol. 2022 Sep 14;12:970299. doi: 10.3389/fonc.2022.970299. eCollection 2022.
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Validation of prompt gamma-ray spectroscopy for proton range verification in tissue-mimicking and porcine samples.
用于组织模拟和猪样本中质子射程验证的瞬发伽马射线光谱学的验证。
Phys Med Biol. 2022 Oct 12;67(20). doi: 10.1088/1361-6560/ac950f.
4
Reduction of clinical safety margins in proton therapy enabled by the clinical implementation of dual-energy CT for direct stopping-power prediction.通过临床实施双能 CT 进行直接阻止本领预测,实现质子治疗的临床安全裕度降低。
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