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一种用于确定两种常用探测器间接测量α粒子辐射活性的响应关系的蒙特卡罗方法。

A Monte Carlo Method for Determining the Response Relationship between Two Commonly Used Detectors to Indirectly Measure Alpha Particle Radiation Activity.

机构信息

Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.

Department of Cancer Physiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA.

出版信息

Molecules. 2019 Sep 19;24(18):3397. doi: 10.3390/molecules24183397.

DOI:10.3390/molecules24183397
PMID:31546752
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6767018/
Abstract

Using targeted ligands to deliver alpha-emitting radionuclides directly to tumor cells has become a promising therapeutic strategy. To calculate the radiation dose to patients, activities of parent and daughter radionuclides must be measured. Scintillation detectors can be used to quantify these activities; however, activities found in pre-clinical and clinical studies can exceed their optimal performance range. Therefore, a method of correcting scintillation detector measurements at higher activities was developed using Monte Carlo modeling. Because there are currently no National Institute of Standards and Technology traceable Actinium-225 (Ac) standards available, a well-type ionization chamber was used to measure 70.3 ± 7.0, 144.3 ± 14.4, 222.0 ± 22.2, 299.7 ± 30.0, 370.0 ± 37.0, and 447.7 ± 44.7 kBq samples of Ac obtained from Oak Ridge National Lab. Samples were then placed in a well-type NaI(Tl) scintillation detector and spectra were obtained. Alpha particle activity for each species was calculated using gamma abundance per alpha decay. MCNP6 Monte Carlo software was used to simulate the 4π-geometry of the NaI(Tl) detector. Using the ionization chamber reading as activity input to the Monte Carlo model, spectra were obtained and compared to NaI(Tl) spectra. Successive simulations of different activities were run until a spectrum minimizing the mean percent difference between the two was identified. This was repeated for each sample activity. Ionization chamber calibration measurements showed increase in error from 3% to 10% as activities decreased, resulting from decreasing detection efficiency. Measurements of Ac using both detector types agreed within 7% of Oak Ridge stated activities. Simulated Monte Carlo spectra of Ac were successfully generated. Activities obtained from these spectra differed with ionization chamber readings up to 156% at 147.7 kBq. Simulated spectra were then adjusted to correct NaI(Tl) measurements to be within 1%. These were compared to ionization chamber readings and a response relationship was determined between the two instruments. Measurements of Ac and daughter activity were conducted using a NaI(Tl) scintillation detector calibrated for energy and efficiency and an ionization chamber calibrated for efficiency using a surrogate calibration reference. Corrections provided by Monte Carlo modeling improve the accuracy of activity quantification for alpha-particle emitting radiopharmaceuticals in pre-clinical and clinical studies.

摘要

使用靶向配体将α发射放射性核素直接递送到肿瘤细胞已成为一种有前途的治疗策略。为了计算患者的辐射剂量,必须测量母体和子体放射性核素的活度。闪烁探测器可用于定量这些活度;然而,在临床前和临床研究中发现的活度可能超过其最佳性能范围。因此,开发了一种使用蒙特卡罗建模校正闪烁探测器在更高活度下的测量方法。由于目前没有可溯源到锕-225(Ac)的国家标准与技术研究所标准,因此使用井型电离室测量了从橡树岭国家实验室获得的 70.3±7.0、144.3±14.4、222.0±22.2、299.7±30.0、370.0±37.0 和 447.7±44.7 kBq 的 Ac 样品。然后将样品放置在井型碘化钠(Tl)闪烁探测器中并获得光谱。使用每个物种的γ衰变丰度计算每个物种的α粒子活度。MCNP6 蒙特卡罗软件用于模拟碘化钠(Tl)探测器的 4π 几何形状。使用电离室读数作为蒙特卡罗模型的活度输入,获得光谱并与碘化钠(Tl)光谱进行比较。运行不同活度的连续模拟,直到确定最小化两个之间的平均百分比差异的光谱。对每个样品的活度重复进行此操作。随着活度的降低,电离室校准测量的误差从 3%增加到 10%,这是由于检测效率降低所致。使用两种探测器类型测量的 Ac 与橡树岭规定的活度一致,误差在 7%以内。成功生成了 Ac 的模拟蒙特卡罗光谱。从这些光谱中获得的活度与电离室读数相差高达 156%,在 147.7 kBq 时。然后调整模拟光谱以将碘化钠(Tl)测量值校正为 1%以内。将这些与电离室读数进行比较,并确定两个仪器之间的响应关系。使用能量和效率经过校准的碘化钠(Tl)闪烁探测器和使用替代校准参考进行效率校准的电离室对 Ac 和子体活性进行测量。蒙特卡罗建模提供的校正可提高临床前和临床研究中发射α粒子的放射性药物活度定量的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/b376fac220b5/molecules-24-03397-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/b7298a803187/molecules-24-03397-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/19120ca5ca2c/molecules-24-03397-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/a72a34d90481/molecules-24-03397-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/bea9898d9357/molecules-24-03397-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/cc4342c678ee/molecules-24-03397-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/c801d0464bf2/molecules-24-03397-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/1811cc17ac71/molecules-24-03397-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/d7a2d79aa4c6/molecules-24-03397-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/182e5fe1428a/molecules-24-03397-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/b376fac220b5/molecules-24-03397-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/b7298a803187/molecules-24-03397-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/19120ca5ca2c/molecules-24-03397-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/a72a34d90481/molecules-24-03397-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/bea9898d9357/molecules-24-03397-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/cc4342c678ee/molecules-24-03397-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/c801d0464bf2/molecules-24-03397-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/1811cc17ac71/molecules-24-03397-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/d7a2d79aa4c6/molecules-24-03397-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fce/6767018/b376fac220b5/molecules-24-03397-g010.jpg

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