Data Streaming Analysis Framework for Through-time 3D Free-breathing Liver DCE-MRI

The Magnetic Resonance Imaging (MRI) clinical applications historically have generated large amounts of data, driven by record keeping, data analysis, and regulatory requirements. However, most raw data is not necessarily stored in hard copy form after analysis (like image reconstruction) is completed. In addition, such image analysis, including reconstruction, registration, and perfusion quantification that performed “offline” are computationally-intensive and time-consuming. These limitations make some MRI analysis applications inappropriate for clinical timescale. Driven by the potential to improve the efficiency of MRI analysis and delivery meanwhile reducing the costs, we develop a data streaming analysis framework specifically for Dynamic Contrast-Enhanced (DCE) Liver MRI application in this paper. The proposed framework has two main features. First, the framework transforms the whole image processing from “offline” to “online” to extensively reduce the data saving and transfer time through data streaming architecture. Second, with the design of optimized reconstruction and registration algorithms, as well as the integration of external computing resources, including Graphics Processing Units (GPUs) parallel computing techniques, the streaming framework achieved 180 times speed-up compared with the original protocol. Our in-vivo experiments showed significantly increased speed (Average 7.72 minutes total analysis time compared to 21.6 hours by original protocol) with minor differences in both image quality and perfusion quantification results. This framework allows easy and direct deployment of clinical studies.