The Gold Standard in Radiology: DICOM

DICOM (Digital Imaging and Communications in Medicine) is an international standard for the exchange and management of medical images, enabling unified communication between various medical systems and devices.

DICOM provides detailed technical guidelines that help different medical imaging devices from various manufacturers communicate with each other over a network. It explains how to format and share medical images and related information both inside hospitals and beyond, such as in teleradiology or telemedicine. Thanks to DICOM, devices like CT scanners, MRI machines, ultrasound units, image storage systems, workstations for viewing and processing images, and even printers can all connect and work together smoothly.

The National Electrical Manufacturers Association (NEMA) and The American College of Radiology (ACR) worked together to create the DICOM format because they needed a way to make a single exam readable across different devices. Prior to its creation, a common problem in the medical community was the difficult interpretation of test results, which depended on the interpretation of manufacturers’ hardware recommendations.

Even today, doctors rely heavily on research results to make key treatment decisions, such as determining the right radiation dose. Misreading this data can lead to serious complications. To ensure maximum precision, experts needed to create a standard. Standard image formats couldn’t store all the necessary information about both the study and the patient. Developers began working on a solution in the 1980s and achieved success in 1992, when they introduced DICOM. Since then, they have continued to expand and improve it.

DICOM Benefits:

• Enhanced Accessibility: DICOM allows medical images to be sent to radiologists anywhere in the world, helping bridge geographic gaps in care.
• Faster Turnaround Times: It supports quicker image transfer and retrieval, which greatly shortens the time needed for diagnosis.
• Improved Patient Outcomes: By enabling timely diagnoses, DICOM can contribute directly to better and even life-saving treatment decisions.
• Cost-Effectiveness: Physicians can interpret images remotely, giving healthcare facilities access to expert opinions without requiring specialists on-site.
• Scalable Solutions: DICOM makes it possible for teleradiology systems to expand and adapt as demand for remote imaging grows.

DICOM Challenges:

• Limited Bandwidth and Storage: DICOM files are often large, which makes transferring and storing them difficult when resources are limited.
• Privacy and Security of Data: These files contain sensitive patient information and must comply with regulations such as HIPAA. Even with better encryption and secure transmission, protecting this data is still a priority.
• System Compatibility: Integrating DICOM with various systems can still pose difficulties.

Although challenges like large file sizes and integration issues persist, modern technologies are helping to solve them and support continued progress.

DICOM supports five key areas:

• Image management across networks
• Interpretation of images over networks
• Print management across networks
• Management of imaging procedures
• Management of offline storage media

The standard includes a complete specification, from the application layer down to bitstream encoding, to enable reliable and automatic interoperability between biomedical imaging systems. DICOM uses a modular design so it can easily adapt to new technologies and evolving use cases. It also offers interfaces to other information systems, helping coordinate patient data, procedures, and diagnostic results.

One of the reasons why DICOM has been successful in so many different clinical imaging contexts is that it includes a Conformance Statement, which streamlines communication regarding imaging hardware software specifications. This statement helps customers evaluate whether a product meets their image management needs and allows manufacturers to clearly define the DICOM capabilities their equipment provides.

Artificial intelligence (AI) is at the forefront of the revolution, with its potential to transform diagnostic accuracy, efficiency, and patient outcomes.

Just as imaging devices generate DICOM instances, pre- and post-processing systems create secondary objects, and humans supplement DICOM studies with their own objects, machine learning and deep learning systems have a role to play in the medical imaging ecosystem.

However, as with any other DICOM data producer, it is important to meet certain criteria in order to act responsibly in this environment. In particular, attention should be paid to:

• A DICOM Conformance Statement should be published on the website of every DICOM data producer – including AI algorithm vendors, specifying what types of objects are created and how they are used in the ecosystem.
• It is worth reading important publications and presentations on issues such as: ethics of using artificial intelligence in radiology.
• It is necessary to ensure that the objects created comply with the specification – e.g. they use the appropriate SOP classes and the appropriate metadata values.

As you can see, AI brings great potential to the DICOM ecosystem, but its effective and safe implementation requires conscious adherence to interoperability standards and rules. Only then can it realistically support diagnostics and bring clinical value.

Sources:

Larobina M. Thirty Years of the DICOM Standard. Tomography. 2023 Oct 6;9(5):1829-1838. doi: 10.3390/tomography9050145. PMID: 37888737; PMCID: PMC10610864.

Bidgood WD Jr, Horii SC, Prior FW, Van Syckle DE. Understanding and using DICOM, the data interchange standard for biomedical imaging. J Am Med Inform Assoc. 1997 May-Jun;4(3):199-212. doi: 10.1136/jamia.1997.0040199. PMID: 9147339; PMCID: PMC61235.

DICOM Official website: https://www.dicomstandard.org/ai