Nonlinear performance analysis and prediction for robust low dose lung CT

1 项目摘要/摘要 基于模型的重建(MBR)和深度学习(DL)重建等2种非线性算法 近年来，3引起了人们极大的研究兴趣。与传统的线性方法相比，NOnline- 这些算法的特点超越了传统的信噪比要求，为信息提取提供了灵活性 来自各种来源(例如，统计模型、先前图像、词典、训练数据)。MBR已启用数字- 6 OUS的改进，包括低剂量CT和先进的扫描方案。深度学习算法是RAP- 7轻微出现，并在研究环境中证明了优越的剂量与图像质量之间的权衡。然而， 8由于缺乏系统性、全面性和全局性，阻碍了非线性算法在临床上的广泛应用。 9绩效分析的定性方法。非线性方法伴随着对图像的大量依赖。 10成像技术、成像目标、先验信息和数据本身。这两者之间的关系 11依赖项和图像质量通常是不透明的。此外，算法参数的不正确选择可能 12导致重建中的错误特征(例如，较小的病变、纹理)。因此，量化的方法 13和预测性能允许进行有效和可量化的性能评估，以提供稳健的控制 14并了解可靠的临床应用和监管所需的成像输出。 15我们建议建立一个强大的、可预测的绩效评估和优化框架，以 16可以推广到任何重建方法。我们按扰动响应和扰动响应的顺序量化性能 17作为成像技术、系统配置、患者解剖以及重要的 18微扰本身。扰动响应量化外观(例如，偏差、模糊、失真)，并且， 19与协方差一起，允许计算更复杂的指标，如基于任务的性能 20和放射测量，包括大小、形状和纹理信息。我们举例说明了该方法在肺部的应用。 21具有以下具体目标的成像：目标1：建立病损资料库和产生扰动环境- 22例通过临床相关特征。我们将从公共数据库中提取病变并开发病变方法 23通过3D打印技术进行逼真的CT仿真和物理数据的仿真。目标2：开发一代人- 24扰动响应和协方差的标准化预测框架。使用分析和神经网络 25建模，我们将建立一个框架来预测成像中的扰动响应和协方差 26种对数据依赖性越来越强的算法类别，包括带有Huber惩罚的MBR，MBR 27具有字典正则化和深度学习重建器。目标3：开展评估和优化-- 28项战略，以推动稳健的、低剂量的肺部筛查CT方法。我们将优化和适应非线性 29用于肺癌筛查的算法和协议，以实现对临床特征的真实描述。这 30项工作有可能推动与图像质量直接相关的亟需的量化评估标准 31到诊断性能和最优策略，以实现稳健、可靠的非线性算法临床部署。 32

1 PROJECT SUMMARY / ABSTRACT 2 Nonlinear algorithms such as model-based reconstruction (MBR) and deep learning (DL) reconstruction have 3 sparked tremendous research interest in recent years. Compared to traditional linear approaches, the nonline- 4 arity of these algorithm transcends traditional signal-to-noise requirement and offer flexibility to draw information 5 from a variety of sources (e.g., statistical model, prior image, dictionary, training data). MBR has enabled numer- 6 ous advancements including low-dose CT and advanced scanning protocols. Deep learning algorithms are rap- 7 idly emerging and have demonstrated superior dose vs. image quality tradeoffs in research settings. However, 8 widespread clinical adoption of nonlinear algorithms has been impeded by the lack of a lack of systematic, quan- 9 titative methods for performance analysis. Nonlinear methods come with numerous dependencies on the imag- 10 ing techniques, the imaging target, and the prior information, and the data itself. The relationship between these 11 dependencies and image quality is often opaque. Furthermore, improper selection of algorithmic parameters can 12 lead to erroneous features (e.g., smaller lesions, texture) in the reconstruction. Therefore, methods to quantify 13 and predict performance permit efficient and quantifiable performance evaluation to provide the robust control 14 and understanding of imaging output necessary for reliable clinical application and regulatory oversight. 15 We propose to establish a robust, predictive framework for performance assessment and optimization that can 16 be generalized to any reconstruction method. We quantify performance in turns of the perturbation response and 17 covariance as a function of imaging techniques, system configurations, patient anatomy, and, importantly, the 18 perturbation itself. The perturbation response quantifies the appearance (e.g., biases, blurs, distortions), and, 19 together with the covariance, allows the computation of more complex metrics such as task-based performance 20 and radiomic measures including size, shape, and texture information. We illustrate utility of the approach in lung 21 imaging with the following specific aims: Aim 1: Develop a lesion library and generate perturbations encom- 22 passing clinically relevant features. We will extract lesions from public databases and develop methods lesion 23 emulation in for realistic CT simulation and physical data via 3D printing technology. Aim 2: Develop a gener- 24 alized prediction framework for perturbation response and covariance. Using analytical and neural network 25 modeling, we will establish a framework that predicts perturbation response and covariance across imaging 26 scenarios for classes of algorithms with increasing data-dependence including MBR with a Huber penalty, MBR 27 with dictionary regularization, and a deep learning reconstructor. Aim 3: Develop assessment and optimiza- 28 tion strategies to drive robust, low dose lung screening CT methods. We will optimize and adapt nonlinear 29 algorithms and protocols for lung cancer screening to achieve faithful representations of clinical features. This 30 work has the potential to drive much-needed quantitative assessment standards that directly relate image quality 31 to diagnostic performance and optimal strategies for robust, reliable clinical deployment of nonlinear algorithms. 32