DL-based CT image formation with characterization and control of resolution and noise

基于深度学习（DL）的CT图像形成方法在过去几年中激增。的 现有的方法大多遵循计算机视觉中建立的范式，并建立一个深度神经网络。 DNN网络（DNN）具有捕获对计算机视觉任务有用的显著图像特征的标准模块。 这些标准模块也非常适合CT图像，将基于DL的CT图像形成方法置于 研究和创新的前沿。然而，目前的DNN忽略了CT图像， 与自然图像不同，必须由放射科医师解释以做出诊断。CT图像解释是 受图像分辨率和噪声方差-协方差等图像特征的影响，这些特征尚未得到充分利用 通过计算机视觉的标准模块。因此，当前基于DL的CT图像形成不具有 直接表征，更不用说前瞻性控制，图像分辨率和噪声方差-协方差。这些 属性只能在生成图像后进行评估，但分辨率/噪声没有直接影响 在图像形成过程中。在本提案中，我们挑战这一既定模式，并提出一个 一个名为GradDNN的创新DL框架，用于（1）表征DNN的分辨率和噪声特性 在网络训练或参数微调期间输出，以及（2）指导图像形成过程， 输出具有期望的分辨率/噪声特性。GradDNN（表示梯度+ DNN）适用 网络线性化，即，梯度计算，对母DNN进行局部分辨率和噪声提取 母亲生成的图像的属性，并使这些属性在网络训练期间可用 和参数微调。用于分析非线性系统（如DNN）的线性化方法从未 以前尝试过。从概念上讲，GradDNN将子模块关联到任何母DNN， 噪声/分辨率表征，从而使所得到的网络CT特定。我们将开发GradDNN 并在两个重要的临床任务的背景下证明其能力：（1）减轻钙开花 在冠状动脉CT血管造影中，和（2）腹部CT中的低对比度病变检测，两者都具有较高的 分辨率和噪声要求。用于DL网络训练的数据将使用数字增强的 在两个研究中心前瞻性收集的患者数据。单独使用母DNN和 使用许多有效的母DNN架构的母+子二人组将被执行， 演示联合分辨率/噪声学习的DL额外增益。比较将使用两个数字 增强的患者数据和真实的患者数据，以进一步建立鲁棒性和可推广性。在这 探索性的建议，我们专注于DL网络，执行图像到图像的转换。但 使用GradDNN的学习框架是通用的，并且可以应用于执行直接学习的DL网络。 投影到图像变换。成功完成此提案将产生强大的初步 数据的后续R 01，扩展我们的结果，这样的DL网络。

Deep learning (DL) based CT image formation methods have proliferated over the past few years. The existing approaches mostly follow the paradigm established in computer vision, and build a deep neural network (DNN) with standard modules that capture salient image features useful for computer vision tasks. These standard modules also work very well for CT images, placing DL-based CT image formation methods at the forefront of research and innovation. However, current DNNs are oblivious to the fact that CT images, unlike natural images, must be interpretable by a radiologist to make a diagnosis. CT image interpretation is affected by image features such as image resolution and noise variance-covariance, which are under exploited by the standard modules from computer vision. Consequently, current DL-based CT image formation has no direct characterization, let alone prospective control, of image resolution and noise variance-covariance. These properties can only be assessed after an image is generated, but resolution/noise has no direct influence during the image formation process. In this proposal, we challenge this established paradigm and propose an innovative DL framework named GradDNN to (1) characterize the resolution and noise properties of a DNN’s output during network training or parameter fine-tuning, and to (2) guide the image formation process so that the output has the desired resolution/noise properties. GradDNN (which stands for gradient + DNN) applies network linearization, i.e., gradient computation, to a mother DNN to extract local resolution and noise properties of the images generated by the mother, and make these properties available during network training and parameter fine tuning. The linearization method for analyzing nonlinear systems such as a DNN has never been attempted before. Conceptually, GradDNN associates a daughter module to any mother DNN for noise/resolution characterization, thereby making the resulting network CT-specific. We will develop GradDNN and demonstrate its capability in the context of two important clinical tasks: (1) mitigation of calcium blooming in coronary CT angiography, and (2) low contrast lesion detection in abdominal CT, both of which have high requirements on resolution and noise. Data for DL network training will be generated using digitally augmented patient data prospectively collected at two sites. Image quality comparison between the mother DNN alone and the mother+daughter duo, using a number of effective mother DNN architectures, will be carried out to demonstrate the additional gain of DL with joint resolution/noise learning. The comparison will use both digitally augmented patient data and real patient data to further establish robustness and generalizability. In this exploratory proposal, we focus on DL networks that perform image-to-image transformation. However, the learning framework using GradDNN is general and can be applied to DL networks that perform direct projection-to-image transformation. Successful completion of this proposal will generate strong preliminary data for a follow-up R01 that extends our results to such DL networks.