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Bao S, Boyd BD, Kanakaraj P, Ramadass K, Meyer FAC, Liu Y, Duett WE, Huo Y, Lyu I, Zald DH, Smith SA, Rogers BP, Landman BA. Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis. J Digit Imaging 2022; 35:1576-1589. [PMID: 35922700 PMCID: PMC9712842 DOI: 10.1007/s10278-022-00679-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 06/21/2022] [Accepted: 07/08/2022] [Indexed: 01/01/2023] Open
Abstract
A robust medical image computing infrastructure must host massive multimodal archives, perform extensive analysis pipelines, and execute scalable job management. An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces complexities for interfacing with XNAT archives. Moreover, workflow integration is combinatorically problematic when matching large amount of processing to large datasets. Historically, workflow engines have been focused on refining workflows themselves instead of actual job generation. However, such an approach is incompatible with data centric architecture that hosts heterogeneous medical image computing. Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool. Herein, we describe developments for DAX that allows for integration of XNAT and BIDS standards. We also improve DAX's efficiencies of diverse containerized workflows in a high-performance computing (HPC) environment. Briefly, we integrate YAML configuration processor scripts to abstract workflow data inputs, data outputs, commands, and job attributes. Finally, we propose an online database-driven mechanism for DAX to efficiently identify the most recent updated sessions, thereby improving job building efficiency on large projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX version 1). To validate the effectiveness of the new features, we verified (1) the efficiency of converting XNAT data to BIDS format and the correctness of the conversion using a collection of BIDS standard containerized neuroimaging workflows, (2) how YAML-based processor simplified configuration setup via a sequence of application pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing jobs compared with earlier DAX baseline method. The empirical results show that (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users, and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified sessions. Herein, we present approaches for efficiently integrating XNAT and modern image formats with a scalable workflow engine for the large-scale dataset access and processing.
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Affiliation(s)
- Shunxing Bao
- Computer Science, Vanderbilt University, Nashville, TN USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN USA
| | - Brian D. Boyd
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | | | | | | | - Yuqian Liu
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
| | - William E. Duett
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
| | - Yuankai Huo
- Computer Science, Vanderbilt University, Nashville, TN USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN USA
- Data Science Institute, Vanderbilt University, Nashville, TN USA
| | - Ilwoo Lyu
- Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - David H. Zald
- Department of Psychology, Vanderbilt University, Nashville, TN USA
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ USA
| | - Seth A. Smith
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Baxter P. Rogers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Bennett A. Landman
- Computer Science, Vanderbilt University, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA
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Wang X, Liu BJ, Martinez C, Zhang X, Winstein CJ. Development of a novel imaging informatics-based system with an intelligent workflow engine (IWEIS) to support imaging-based clinical trials. Comput Biol Med 2016; 69:261-9. [PMID: 25870169 DOI: 10.1016/j.compbiomed.2015.03.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Revised: 03/13/2015] [Accepted: 03/22/2015] [Indexed: 11/20/2022]
Abstract
Imaging based clinical trials can benefit from a solution to efficiently collect, analyze, and distribute multimedia data at various stages within the workflow. Currently, the data management needs of these trials are typically addressed with custom-built systems. However, software development of the custom-built systems for versatile workflows can be resource-consuming. To address these challenges, we present a system with a workflow engine for imaging based clinical trials. The system enables a project coordinator to build a data collection and management system specifically related to study protocol workflow without programming. Web Access to DICOM Objects (WADO) module with novel features is integrated to further facilitate imaging related study. The system was initially evaluated by an imaging based rehabilitation clinical trial. The evaluation shows that the cost of the development of system can be much reduced compared to the custom-built system. By providing a solution to customize a system and automate the workflow, the system will save on development time and reduce errors especially for imaging clinical trials.
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