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现代 PET 系统中的图像质量评估:新重建方法和放射组学方法的影响。

Image quality evaluation in a modern PET system: impact of new reconstructions methods and a radiomics approach.

机构信息

Medical Physics Department, Institut Català d'Oncologia, L'Hospitalet de Llobregat, Barcelona, Spain.

PET Unit. Nuclear Medicine Dept, IDI. Hospital U. de Bellvitge-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain.

出版信息

Sci Rep. 2019 Jul 23;9(1):10640. doi: 10.1038/s41598-019-46937-8.

Abstract

The present work investigates the influence of different biological and physical parameters on image quality (IQ) perception of the abdominal area in a modern PET scanner, using new reconstruction algorithms and testing the utility of a radiomics approach. Scans of 112 patients were retrospectively included. Images were reconstructed using both OSEM + PSF and BSRM methods, and IQ of the abdominal region was subjectively evaluated. First, 22 IQ related parameters were obtained (including count rate and biological or mixed parameters) and compared to the subjective IQ scores by means of correlations and logistic regression. Second, an additional set of radiomics features was extracted, and a model was constructed by means of an elastic-net regression. For the OSEM + PSF and especially for the BSRM reconstructions, IQ parameters presented only at best moderated correlations with the subjective IQ. None of the studied parameters presented a good predictive power for IQ, while a simple radiomics model increased the performance of the IQ prediction. These results suggest the necessity of changing the standard parameters to evaluate IQ, particularly when a BSRM algorithm is involved. Furthermore, it seems that a simple radiomics model can outperform the use of any single parameter to assess IQ.

摘要

本研究旨在利用新的重建算法,探讨不同生物学和物理学参数对现代 PET 扫描仪腹部区域图像质量(IQ)感知的影响,并测试放射组学方法的实用性。回顾性纳入了 112 名患者的扫描数据。使用 OSEM+PSF 和 BSRM 方法对图像进行重建,并对腹部区域的 IQ 进行主观评估。首先,获得了 22 个与 IQ 相关的参数(包括计数率和生物学或混合参数),并通过相关性和逻辑回归分析将这些参数与主观 IQ 评分进行比较。其次,提取了一组额外的放射组学特征,并通过弹性网络回归构建了一个模型。对于 OSEM+PSF 和 BSRM 重建,IQ 参数与主观 IQ 仅具有最佳中等相关性。研究中的参数均没有很好的预测 IQ 的能力,而简单的放射组学模型提高了 IQ 预测的性能。这些结果表明,有必要改变评估 IQ 的标准参数,特别是当使用 BSRM 算法时。此外,似乎简单的放射组学模型可以优于使用任何单一参数来评估 IQ。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33e2/6650602/16b682eff795/41598_2019_46937_Fig1_HTML.jpg

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