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A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images.

Pradeeban Kathiravelu1, Puneet Sharma2, Ashish Sharma2

  • 1Emory University, GA, 30306, Atlanta, USA. Pradeeban.kathiravelu@emory.edu.

Journal of Digital Imaging
|August 18, 2021
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Summary
This summary is machine-generated.

Niffler, a new framework, enables real-time machine learning (ML) on radiology images by efficiently transferring data from Picture Archiving and Communication Systems (PACS). This facilitates complex ML pipelines in research clusters, overcoming clinical resource limitations.

Keywords:
Clinical data warehouse (CDW)Digital Imaging and Communications in Medicine (DICOM)Machine learning (ML)Picture archiving and communication system (PACS)

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Health Informatics

Background:

  • Executing machine learning (ML) pipelines on radiology images is challenging due to limited clinical computing resources.
  • Research clusters require efficient data transfer from hospital Picture Archiving and Communication Systems (PACS).

Purpose of the Study:

  • To introduce Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework.
  • To enable real-time ML and processing pipelines in research clusters by efficiently retrieving images and metadata from PACS.

Main Methods:

  • Developed Niffler, an open-source DICOM framework for data retrieval and metadata extraction.
  • Deployed Niffler at Emory Healthcare, retrieving up to 350 GB/day of DICOM data over two years.
  • Utilized Niffler for real-time ML pipeline execution, bulk on-demand image retrieval, and metadata analysis.

Main Results:

  • Successfully executed a real-time IVC filter detection and segmentation pipeline on abdominal radiographs with 96.0% accuracy.
  • Validated Niffler's accuracy in identifying MRI scanner examination timeframes and idling times against Clinical Data Warehouse (CDW).
  • Identified and corrected misconfigured scanner times on five systems using Niffler-extracted metadata.

Conclusions:

  • Niffler effectively enables real-time machine learning and processing pipelines in research clusters.
  • The framework enhances data retrieval efficiency and metadata extraction from DICOM images.
  • Niffler demonstrates significant utility in clinical research, operational efficiency analysis, and system quality control.