Nonlinear performance analysis and prediction for robust low dose lung CT

1 项目总结/摘要 2非线性算法，如基于模型的重建（MBR）和深度学习（DL）重建， 3近年来引发了巨大的研究兴趣。与传统的线性方法相比，非线性- 这些算法的特点是超越了传统的信噪比要求，提供了提取信息的灵活性 5、各种来源（如：统计模型、先验图像、字典、训练数据）。MBR已启用编号- 包括低剂量CT和先进的扫描方案在内的6项进步。深度学习算法是RAP- 在研究环境中，已经证明了上级剂量与图像质量的权衡。然而，在这方面， 非线性算法的广泛临床应用受到缺乏系统性、全方位、 9种性能分析的评价方法。非线性方法对图像有许多依赖性， 成像技术、成像目标、先验信息和数据本身。的相关性 11依赖性和图像质量往往是不透明的。此外，算法参数的不适当选择可 12导致错误的特征（例如，较小的病变、纹理）。因此，量化方法 13和预测性能允许有效和可量化的性能评估，以提供鲁棒控制 了解可靠的临床应用和监管监督所需的成像输出。 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， 16可以推广到任何重建方法。我们量化的扰动响应， 17协方差作为成像技术、系统配置、患者解剖结构的函数，重要的是， 18自己的烦恼扰动响应量化外观（例如，偏差、模糊、失真），以及， 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和放射性测量，包括大小、形状和纹理信息。我们举例说明实用的方法在肺 目标1：开发病变库并生成扰动编码， 22个通过临床相关特征。我们将从公共数据库中提取病变，并开发方法 23仿真通过3D打印技术获得逼真的CT仿真和物理数据。目标2：开发一个通用的 扰动响应和协方差的24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，预测扰动响应和协方差跨成像 26种数据依赖性增加的算法类场景，包括带有Huber惩罚的MBR，MBR 27与字典正则化，和深度学习重建器。目标3：开展评估和优化- 28项策略，以推动稳健的低剂量肺部筛查CT方法。我们将优化和适应非线性 肺癌筛查的29种算法和协议，以实现临床特征的忠实表示。这 30工作有可能推动急需的定量评估标准，直接关系到图像质量 31的诊断性能和最佳策略，用于非线性算法的稳健、可靠的临床部署。 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