...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Medical Physics >Impact of joint statistical dual‐energy CT reconstruction of proton stopping power images: Comparison to image‐ and sinogram‐domain material decomposition approaches
【24h】

Impact of joint statistical dual‐energy CT reconstruction of proton stopping power images: Comparison to image‐ and sinogram‐domain material decomposition approaches

机译：聚环统计双能CT重建质子停止电源的影响：与型材和叠层域材料分解方法的比较

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Purpose The purpose of this study was to assess the performance of a novel dual‐energy CT (DECT) approach for proton stopping power ratio (SPR) mapping that integrates image reconstruction and material characterization using a joint statistical image reconstruction (JSIR) method based on a linear basis vector model (BVM). A systematic comparison between the JSIR‐BVM method and previously described DECT image‐ and sinogram‐domain decomposition approaches is also carried out on synthetic data. Methods The JSIR‐BVM method was implemented to estimate the electron densities and mean excitation energies ( I ‐values) required by the Bethe equation for SPR mapping. In addition, image‐ and sinogram‐domain DECT methods based on three available SPR models including BVM were implemented for comparison. The intrinsic SPR modeling accuracy of the three models was first validated. Synthetic DECT transmission sinograms of two 330 mm diameter phantoms each containing 17 soft and bony tissues (for a total of 34) of known composition were then generated with spectra of 90 and 140 kVp. The estimation accuracy of the reconstructed SPR images were evaluated for the seven investigated methods. The impact of phantom size and insert location on SPR estimation accuracy was also investigated. Results All three selected DECT‐SPR models predict the SPR of all tissue types with less than 0.2% RMS errors under idealized conditions with no reconstruction uncertainties. When applied to synthetic sinograms, the JSIR‐BVM method achieves the best performance with mean and RMS‐average errors of less than 0.05% and 0.3%, respectively, for all noise levels, while the image‐ and sinogram‐domain decomposition methods show increasing mean and RMS‐average errors with increasing noise level. The JSIR‐BVM method also reduces statistical SPR variation by sixfold compared to other methods. A 25% phantom diameter change causes up to 4% SPR differences for the image‐domain decomposition approach, while the JSIR‐BVM method and sinogram‐domain decomposition methods are insensitive to size change. Conclusion Among all the investigated methods, the JSIR‐BVM method achieves the best performance for SPR estimation in our simulation phantom study. This novel method is robust with respect to sinogram noise and residual beam‐hardening effects, yielding SPR estimation errors comparable to intrinsic BVM modeling error. In contrast, the achievable SPR estimation accuracy of the image‐ and sinogram‐domain decomposition methods is dominated by the CT image intensity uncertainties introduced by the reconstruction and decomposition processes.
机译：目的本研究的目的是评估用于质子停止功率比（SPR）的映射的新的双能CT（DECT）的方法的，使用一个联合统计图像重建集成图像重建和材料表征（JSIR）的基础上的方法的性能线性基础矢量模型（BVM）。所述JSIR-BVM方法和前面描述的DECT图像 - 和窦腔X线照相域分解之间的系统的比较办法上合成的数据也被进行。方法的JSIR-BVM方法被实施以估计电子密度和平均激发能（I - 值）由贝特方程SPR映射所需。此外，以进行比较实现了基于三个可用SPR模型，包括BVM图像 - 和正弦图域DECT方法。这三种模式的内在SPR建模精度首先进行了验证。 2米330mm直径的合成DECT传输窦腔X线照相体模已知组合物各自含有17个柔软和骨组织（总共34）然后用90和140kVp的光谱产生。重建的SPR图像的估计精度为七点调查的方法进行评价。在SPR估计精度幻像大小和插入地点的影响也进行了研究。结果所有三个选定的DECT-SPR模型预测所有组织类型的SPR小于0.2％均方根误差没有重建的不确定性理想化的条件下。当施加到合成窦腔X线照相中，JSIR-BVM方法实现与分别低于0.05％和0.3％，平均值和RMS-平均误差，对于所有的噪音水平的最好的性能，而所述图像和窦腔X线照相域分解方法显示增加平均和随噪声电平RMS的平均误差。所述JSIR-BVM方法还通过六倍与其它方法相比降低了统计SPR变化。将25％的幻影直径改变导致高达用于图像域分解方法4％SPR差异，而JSIR-BVM方法和窦腔X线照相域分解方法是不敏感的尺寸变化。结论在所有被调查的方法，对JSIR-BVM方法实现在我们的模拟影像学SPR估计的最佳性能。这种新颖的方法是相对于正弦图噪声和剩余束硬化效应健壮，得到相当于固有BVM建模误差SPR估计误差。与此相反，所述图像和窦腔X线照相域分解方法中的实现的SPR估计精度被由重建和分解过程引入的CT图像强度的不确定性支配。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号