How does DICOM support big data management? Investigating its use in medical imaging community

Area of Science:

• Medical Imaging
• Health Informatics
• Big Data Analytics

Background:

• The diagnostic imaging field generates vast amounts of data, presenting opportunities for big data analytics.
• Lack of standardization and privacy concerns are key barriers to capitalizing on this medical imaging data.
• The Digital Imaging and Communications in Medicine (DICOM) standard offers advanced features beyond basic image storage.

Purpose of the Study:

• To assess the effective utilization of advanced DICOM standard features in research practice.
• To investigate how publicly shared medical imaging databases and common software tools support the full potential of DICOM.
• To identify barriers and opportunities for enhancing big data management in medical imaging through DICOM.

Main Methods:

• Systematic examination of 100 public medical imaging databases and 10 common medical imaging software tools.
• Assessment of DICOM fields related to privacy, segmentation, and reporting in selected databases.
• Evaluation of software tools for their capability to read and write these specific DICOM fields.

Main Results:

• Less than one-third of examined databases utilize DICOM for meaningful image management information.
• Most software tools do not support the management, reading, or writing of all relevant DICOM fields.
• Surprisingly, only two out of 12 chest CT datasets for COVID-19 research were shared in DICOM format.

Conclusions:

• The DICOM standard has the potential to fully support big data management in medical imaging.
• Significant efforts are required from the scientific and technological communities to promote wider adoption of DICOM.
• Encouraging greater use of DICOM is crucial for advancing data sharing, interoperability, and big data analytics in the field.