Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90024, USA.
Physics and Biology in Medicine Graduate Program, David Geffen School of Medicine at UCLA, Los Angeles, CA, 90024, USA.
Med Phys. 2019 May;46(5):2310-2322. doi: 10.1002/mp.13401. Epub 2019 Apr 3.
With recent substantial improvements in modern computing, interest in quantitative imaging with CT has seen a dramatic increase. As a result, the need to both create and analyze large, high-quality datasets of clinical studies has increased as well. At present, no efficient, widely available method exists to accomplish this. The purpose of this technical note is to describe an open-source high-throughput computational pipeline framework for the reconstruction and analysis of diagnostic CT imaging data to conduct large-scale quantitative imaging studies and to accelerate and improve quantitative imaging research.
The pipeline consists of two, primary "blocks": reconstruction and analysis. Reconstruction is carried out via a graphics processing unit (GPU) queuing framework developed specifically for the pipeline that allows a dataset to be reconstructed using a variety of different parameter configurations such as slice thickness, reconstruction kernel, and simulated acquisition dose. The analysis portion then automatically analyzes the output of the reconstruction using "modules" that can be combined in various ways to conduct different experiments. Acceleration of analysis is achieved using cluster processing. Efficiency and performance of the pipeline are demonstrated using an example 142 subject lung screening cohort reconstructed 36 different ways and analyzed using quantitative emphysema scoring techniques.
The pipeline reconstructed and analyzed the 5112 reconstructed datasets in approximately 10 days, a roughly 72× speedup over previous efforts using the scanner for reconstructions. Tightly coupled pipeline quality assurance software ensured proper performance of analysis modules with regard to segmentation and emphysema scoring.
The pipeline greatly reduced the time from experiment conception to quantitative results. The modular design of the pipeline allows the high-throughput framework to be utilized for other future experiments into different quantitative imaging techniques. Future applications of the pipeline being explored are robustness testing of quantitative imaging metrics, data generation for deep learning, and use as a test platform for image-processing techniques to improve clinical quantitative imaging.
随着现代计算技术的显著进步，对 CT 定量成像的兴趣大幅增加。因此，对创建和分析大量高质量临床研究数据集的需求也在增加。目前，还没有有效的、广泛可用的方法来实现这一点。本技术说明的目的是描述一种用于诊断 CT 成像数据的重建和分析的开源高通量计算管道框架，以进行大规模定量成像研究，并加速和改进定量成像研究。
该管道由两个主要“块”组成：重建和分析。重建是通过专门为该管道开发的图形处理单元 (GPU) 队列框架来进行的，该框架允许使用各种不同的参数配置（如切片厚度、重建核和模拟采集剂量）来重建数据集。然后，分析部分使用“模块”自动分析重建的输出，这些模块可以以各种方式组合，以进行不同的实验。通过集群处理来加速分析。使用一个包含 142 名受试者的肺部筛查队列的示例，该队列以 36 种不同的方式进行重建，并使用定量肺气肿评分技术进行分析，证明了该管道的效率和性能。
该管道在大约 10 天内重建和分析了 5112 个重建数据集，与使用扫描仪进行重建相比，速度提高了约 72 倍。紧密耦合的管道质量保证软件确保了分析模块在分割和肺气肿评分方面的正确性能。
该管道大大缩短了从实验构思到定量结果的时间。管道的模块化设计允许高通量框架用于其他未来的定量成像技术实验。正在探索的该管道的未来应用包括定量成像指标的稳健性测试、深度学习的数据生成以及作为图像处理技术的测试平台，以改善临床定量成像。
