Overcoming Metadata Challenges in Medical Imaging with Data Normalization

Medical imaging is pivotal in modern healthcare, enabling clinicians to diagnose and treat various conditions precisely. Behind the scenes, there’s a treasure trove of information stored in metadata, which includes patient health data and imaging parameters. However, harnessing this valuable metadata comes with its own set of challenges.

The Metadata Puzzle

In DICOM-based imaging, metadata is pervasive, appearing at multiple levels: patient, study, series, and image. This metadata contains sensitive patient health information (PHI), such as names, medical record numbers, and dates of birth, along with critical image acquisition details like dimensions, voxel size, repetition time (TR), and data types.

At its core, metadata encapsulates a wealth of data crucial for the effective practice of medicine. Imagine it as the intricate tapestry of information that connects the dots between patient identities and the images that hold the secrets to their health. Here’s a closer look at how metadata manifests at different levels and why proper handling is paramount.

Patient-Level Metadata

Patient-level metadata is the first layer of this complex tapestry. It includes sensitive patient health information (PHI) that forms the foundation of a patient’s medical record, including names, medical record numbers, dates of birth, and other identifying information. Healthcare professionals must actively manage these details with confidentiality and in compliance with healthcare regulations since they are critical to a patient’s medical history.

Study-Level Metadata

The next level is encounter study-level metadata, where the focus shifts from individual patients to specific medical studies. Each study encompasses a set of related images that provide valuable insights into a patient’s condition. Here, metadata extends beyond PHI to include critical information like the type of study conducted and the unique study identifier (SOP UID). The SOP UID serves as a digital fingerprint, ensuring the integrity and traceability of the study data.

Series-Level Metadata

In a single study, multiple image series captures various aspects of a patient’s condition at the series-level metadata. At this level, metadata provides insights into the intricacies of image acquisition, covering specifics such as image dimensions, voxel size, and repetition time (TR). Radiologists and diagnosticians use this information to assess the suitability of the images for diagnosis and treatment planning.

Image-Level Metadata

Image-level metadata sits at the top of the pyramid of these levels. Each image within a series possesses its own set of attributes. Here, metadata defines the image’s data type, source, and other intricacies critical for accurate interpretation. It classifies whether an image is a CT scan, MRI, or X-ray and provides diagnosticians with the context to make informed decisions.

DICOM metadata from an image file. Source: ResearchGate

List of DICOM tags. Source ResearchGate

The Importance of Proper Mammography Study Normalization

To illustrate the significance of accurate metadata handling, consider the realm of mammography. Mammography study normalization is a critical process that relies heavily on precise metadata classification by radiologists and diagnosticians.

Mammography involves the imaging of breast tissue to detect and diagnose breast cancer. Proper study normalization enables all mammography images to be correctly categorized, supporting accurate interpretation and diagnosis. Failure to correctly classify attributes such as the SOP UID (which uniquely identifies the type of mammography study) and the modality type can lead to incorrect study classification and misinterpretation of the images.

Even the slightest error in metadata classification can have profound consequences in mammography. Accurate study normalization is not just a matter of convenience; it’s a matter of patient well-being. It ensures that radiologists and diagnosticians can rely on the metadata to make precise assessments, identify abnormalities, and recommend appropriate treatments.

Metadata Challenges

In DICOM-based imaging, metadata is present at the patient, study, series, and image levels. The specific workflow challenges related to metadata fall into the following categories:

1. Body Part Ambiguity
One significant challenge is the naming structure for body parts. While DICOM standardizes these names, they can sometimes be overly specific or need to be more detailed. For instance, the term “humerus” might work for an X-ray of the upper arm but is inadequate for a picture of the exact location on the skin.

2. Procedure Description Complexity
At the study level, procedure descriptions can be complex and condensed, making interpreting them difficult. For example, “RAD Hand 2-3V Right” indicates a radiograph with 2-3 views of the right hand. However, enterprise imaging has no standardized way to create such procedure descriptions, leading to potential misinterpretations.

3. Department Identification
In many radiology and cardiology Picture Archiving and Communication Systems (PACS), the DICOM department field’s value might be limited. Still, as images from various sources converge in a hospital, accurately identifying the department becomes crucial. A dermatologist, for example, must quickly distinguish dermatology images from radiologic ones of the same body part.

4. Imaging Source Differentiation
As patient information and images become more commonly uploaded to Electronic Health Records (EHRs) or enterprise imaging archives, it becomes crucial to differentiate between patient studies and those obtained by healthcare providers. This distinction is essential for quality assurance, addressing liability concerns, and fulfilling meaningful use reporting requirements.

5. Data Normalization Dilemmas
Beyond these specific challenges, there are broader issues related to metadata normalization, including transfer syntax issues, managing and converting proprietary formats, and dealing with duplicate studies.

  • Transfer Syntax Issues: Encoding and decoding image data can be problematic when different transfer syntaxes are involved. A Transfer Syntax is a set of encoding rules that unambiguously represent one or more Abstract Syntaxes. In particular, it allows communicating Application Entities to negotiate standard encoding techniques they both support (e.g., byte ordering, compression, etc.). A Transfer Syntax is an attribute of a Presentation Context where one or more establish an Association between DICOM Application Entities.
  • Proprietary Formats: Dealing with images in proprietary formats can hinder interoperability. For example, certain vendors store and display mammography exams in proprietary formats, such as Computed Tomography Object (CTO) and Secondary Capture Object (SCO), and cannot be viewed by other viewers, presenting workflow challenges. These file formats require conversion into standard Breast Tomosynthesis Objects (BTO) to pull relevant prior studies for the proper diagnosis. Recognizing which proprietary files occur in the workflow and obtaining the proper tools for conversion will help streamline the imaging workflows.
  • Duplicate Studies: Identifying and handling duplicate studies or images can be time-consuming and error-prone.

6. Dicom Viewer Compatibility
An essential part of the imaging workflow is ensuring all images and studies are viewable throughout the enterprise. Some viewers depend on specific metadata; if particular tags are absent, the study may not display correctly. This issue applies to various subspecialties, specifically mammography. Some viewers require certain tags for accurate viewing, often generated on the fly for proper visualization.

The DICOM Tag Conundrum

The seamless data transfer to and from archives is critical in medical imaging workflows. However, a seemingly innocuous obstacle often obstructs the transfer of DICOM tags. Fixing non-standard or proprietary tags before sending data to PACS or a Vendor-Neutral Archive (VNA) must be completed for quality, compliance, and downstream workflow efficiency.

Overcoming Challenges with Metadata with Enterprise Imaging

Healthcare organizations must adopt robust strategies and technologies to address particular metadata challenges and enhance DICOM image routing effectively. Here are some focus areas:

1. Intelligent Routing
Routing imaging data through PACS/MIMPS or modalities can take time and effort. For optimal workflows, consider the following:

  • Customization for HL7 and DICOM routing with advanced rule support.
  • Complex off-hours support for remote readers.
  • Routing based on HL7 fields or DICOM tags.
  • Data caching for efficient delivery.
  • TLS encryption for secure data transmission.

2. Enterprise-Grade Scalability
Healthcare is ever-changing, and organizations must plan for integration, data migration, and advanced requirements like AI integration. Address challenges related to:

  • Fragmented DICOM Modality Worklist (DMWL) providers.
  • Limited or missing documentation on existing interfaces.
  • Supporting multiple archives.
  • Removing point-to-point interfaces.
  • Supporting remote sites with limited infrastructure.
  • Enabling de-identification for research.

3. Minimizing Disruptions
Radiologists often work remotely, and regardless of their location, they require fast access to relevant prior images. To ensure streamlined patient care and reduce any intended or unintended interruptions in the radiologist workflow, consider the following solutions:

  • Sourcing priors from multiple systems.
  • Implementing redundancy and failover mechanisms.
  • Utilizing specialized DICOM/HL7 load balancing.
  • Effective management of remote devices.
  • Resolution of intermittent communication issues.

4. Leveraging Technology
Addressing these challenges may initially appear formidable, yet technology can serve as a formidable ally. Seek out solutions that offer force-multiplying advantages, adept at simultaneously resolving multiple intricacies. Here are five technologically-driven strategies to initiate your journey:

  • Metadata Standardization: Implement systems that enforce the consistency and meaningfulness of metadata.
  • Tag Normalization Tools: Employ software tools specifically designed to streamline the DICOM Tag normalization processes.
  • Intelligent Routing Solutions: Allocate resources towards advanced routing platforms, optimizing data transfer efficiency.
  • Enterprise-Grade Archives: Embrace scalable and versatile archive solutions equipped to accommodate future growth.
  • Unified Documentation: Forge comprehensive documentation to eradicate knowledge fragmentation.

Conquering metadata challenges is vital for seamless patient care, compliance, and the efficient functioning of healthcare workflows. Discover how Dicom Systems can assist you in tackling specific DICOM-related metadata challenges and streamlining your enterprise imaging workflows. Schedule a consultation today to learn more about our solutions to improve your operations.