Nonlinear performance analysis and prediction for robust low dose lung CT

鲁棒低剂量肺部 CT 的非线性性能分析和预测

基本信息

批准号：
10684375
负责人：
Jianan Grace Gang
金额：
$ 28.59万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10684375
关键词：
3D Print Address Adoption Algorithms Anatomy Appearance Beauty Behavior Biological Models Clinic Clinical Complex Data Databases Dependence Derivation procedure Development Diagnostic Dictionary Digital Libraries Dimensions Dose Ensure Evaluation Genes Image Image Analysis Imaging Techniques Lead Lesion Libraries Lung Lung CAT Scan Lung nodule Machine Learning Measures Medical Imaging Methods Modeling Nodule Noise Non-linear Models Outcome Output Patients Performance Play Predictive Analytics Property Protocols documentation Radiation Dose Unit Research Role Sampling Scanning Scheme Shapes Signal Transduction Source Statistical Models System Techniques Technology Texture Training Transcend Work X-Ray Computed Tomography base clinical application clinical translation clinically relevant data-driven model deep learning deep learning algorithm deep neural network design exhaustion flexibility imaging system improved insight interest low dose computed tomography lung cancer screening lung lesion machine learning method neural network novel predicting response public database quantitative imaging radiomics reconstruction response screening shape analysis simulation success targeted imaging

项目摘要

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

1 项目概要/摘要 2 基于模型的重建（MBR）和深度学习（DL）重建等非线性算法 3 近年来引起了人们极大的研究兴趣。与传统的线性方法相比，非线性方法 4 这些算法的数量超越了传统的信噪比要求，并提供了获取信息的灵活性 5 来自各种来源（例如统计模型、先前图像、字典、训练数据）。 MBR 已启用数字 6 项进步，包括低剂量 CT 和先进的扫描协议。深度学习算法是rap- 7 慢慢出现，并在研究环境中展示了优越的剂量与图像质量权衡。然而， 8 非线性算法在临床上的广泛应用因缺乏系统性、定量性而受到阻碍。 9种性能分析的滴定方法。非线性方法对图像有许多依赖性 10 技术、成像目标、先验信息以及数据本身。这些之间的关系 11 依赖性和图像质量通常是不透明的。此外，算法参数选择不当也会导致 12 导致重建中的错误特征（例如较小的病灶、纹理）。因此，量化方法 13、预测绩效允许高效且可量化的绩效评估，以提供稳健的控制 14 并了解可靠的临床应用和监管监督所需的成像输出。 15 我们建议建立一个稳健的、预测性的绩效评估和优化框架， 16可以推广到任何重建方法。我们依次量化扰动响应和 17 协方差是成像技术、系统配置、患者解剖结构以及重要的是， 18 扰动本身。扰动响应量化外观（例如偏差、模糊、扭曲），并且， 19 与协方差一起，允许计算更复杂的指标，例如基于任务的性能 20 和放射组学测量，包括尺寸、形状和纹理信息。我们举例说明该方法在肺部的实用性 21 成像具有以下具体目标：目标 1：开发病变库并生成扰动 22 项通过临床相关特征。我们将从公共数据库中提取病变并开发病变方法 23 仿真通过 3D 打印技术实现真实的 CT 模拟和物理数据。目标 2：开发一个通用的 24 扰动响应和协方差的化预测框架。使用分析和神经网络 25 建模，我们将建立一个框架来预测跨成像的扰动响应和协方差数据依赖性不断增加的算法类别的 26 个场景，包括具有 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 推动稳健、低剂量肺部筛查 CT 方法的策略。我们将优化和适应非线性 29 种肺癌筛查算法和协议，可忠实再现临床特征。这 30 项工作有潜力推动急需的与图像质量直接相关的定量评估标准 31 诊断性能和最佳策略，以实现稳健、可靠的非线性算法临床部署。 32