Zarei Mojtaba, Abadi Ehsan, Vancoillie Liesbeth, Samei Ehsan
Duke University, Center for Virtual Imaging Trials, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina, United States.
Duke University, Pratt School of Engineering, Department of Electrical and Computer Engineering, Durham, North Carolina, United States.
J Med Imaging (Bellingham). 2024 Mar;11(2):025501. doi: 10.1117/1.JMI.11.2.025501. Epub 2024 Apr 26.
The accuracy of morphological radiomic features (MRFs) can be affected by various acquisition settings and imaging conditions. To ensure that clinically irrelevant changes do not reduce sensitivity to capture the radiomics changes between successive acquisitions, it is essential to determine the optimal imaging systems and protocols to use.
The main goal of our study was to optimize CT protocols and minimize the minimum detectable difference (MDD) in successive acquisitions of MRFs.
MDDs were derived based on the previous research involving 15 realizations of nodule models at two different sizes. Our study involved simulations of two consecutive acquisitions using 297 different imaging conditions, representing variations in scanners' reconstruction kernels, dose levels, and slice thicknesses. Parametric polynomial models were developed to establish correlations between imaging system characteristics, lesion size, and MDDs. Additionally, polynomial models were used to model the correlation of the imaging system parameters. Optimization problems were formulated for each MRF to minimize the approximated function. Feature importance was determined for each MRF through permutation feature analysis. The proposed method was compared to the recommended guidelines by the quantitative imaging biomarkers alliance (QIBA).
The feature importance analysis showed that lesion size is the most influential parameter to estimate the MDDs in most of the MRFs. Our study revealed that thinner slices and higher doses had a measurable impact on reducing the MDDs. Higher spatial resolution and lower noise magnitude were identified as the most suitable or noninferior acquisition settings. Compared to QIBA, the proposed protocol selection guideline demonstrated a reduced coefficient of variation, with values decreasing from 1.49 to 1.11 for large lesions and from 1.68 to 1.12 for small lesions.
The protocol optimization framework provides means to assess and optimize protocols to minimize the MDD to increase the sensitivity of the measurements in lung cancer screening.
形态学放射组学特征（MRF）的准确性可能会受到各种采集设置和成像条件的影响。为确保临床无关的变化不会降低捕捉连续采集之间放射组学变化的敏感性，确定最佳的成像系统和使用方案至关重要。
我们研究的主要目标是优化CT方案，并在MRF的连续采集中将最小可检测差异（MDD）降至最低。
基于之前涉及两种不同大小的结节模型的15次实现的研究得出MDD。我们的研究涉及使用297种不同成像条件对两次连续采集进行模拟，这些条件代表了扫描仪重建内核、剂量水平和切片厚度的变化。开发了参数多项式模型，以建立成像系统特征、病变大小和MDD之间的相关性。此外，多项式模型用于对成像系统参数的相关性进行建模。针对每个MRF制定优化问题，以最小化近似函数。通过排列特征分析确定每个MRF的特征重要性。将所提出的方法与定量成像生物标志物联盟（QIBA）的推荐指南进行比较。
特征重要性分析表明，在大多数MRF中，病变大小是估计MDD的最有影响力的参数。我们的研究表明，更薄的切片和更高的剂量对降低MDD有可测量的影响。更高的空间分辨率和更低的噪声幅度被确定为最合适或非劣效的采集设置。与QIBA相比，所提出的方案选择指南显示变异系数降低，大病变的值从1.49降至1.11，小病变的值从1.68降至1.12。
方案优化框架提供了评估和优化方案的方法，以最小化MDD，提高肺癌筛查测量的敏感性。
