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.

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