Update the documentation for the data pipeline to include detailed instructions on setting up, configuring, and running the DICOM file comparison script. This documentation update will provide clarity and guidance for users, facilitating easier adoption and usage of the pipeline.

Implement a mechanism to rename DICOM files following the proposed structure after the anonymization process. This mechanism should parse necessary information either from the original filename or DICOM metadata.

The proposed structure is the following:

AnonimizedPatientID_YYYYMMDD_MODALITY_SEQUENCE_INSTANCE.dcm

Acceptance Criteria:

• Successfully rename DICOM files according to the specified naming convention.
• Extract required information from DICOM metadata if available.
• Log any errors or files that cannot be renamed due to missing information.

I encountered an issue while running the anonymization process for DICOM files. The process failed for some files due to missing attributes in the DICOM dataset. Specifically, the attribute PatientAge was not found in one of the DICOM files, leading to an error during anonymization.

Steps to Reproduce

1. Run the anonymization process for a set of DICOM files.

2. Encounter an error similar to the following:

Anonymization failed for /path/to/dicom/file: 'FileDataset' object has no attribute 'PatientAge'

Expected Behavior

The anonymization process should handle cases where certain attributes are missing from the DICOM dataset gracefully, without causing errors. This should be done to all DICOM tags.

Proposed Solution

Update the anonymization logic to check whether the required attributes exist before attempting to anonymize them. Specifically, change the order of operations to first check if the attribute exists before attempting to access it in all DICOM tags.

For instance, from:

# Anonymize Patient's Age
logging.info(f"Anonymizing Patient's Age: {getattr(ds, 'PatientAge', 'N/A')}")
if hasattr(ds, 'PatientAge'):
  ds.PatientAge = ""
  logging.info(f"PatientAge: {ds.PatientAge}")
else:
  logging.info("Attribute 'PatientAge' not found in DICOM file.")

To:

# Anonymize Patient's Age
if hasattr(ds, 'PatientAge'):
  logging.info(f"Anonymizing Patient's Age: {getattr(ds, 'PatientAge', 'N/A')}")
  ds.PatientAge = ""
  logging.info(f"PatientAge: {ds.PatientAge}")
else:
  logging.info("Attribute 'PatientAge' not found in DICOM file.")

Additional Information

• Operating System: macOS 14.4.1
• Python Version: Python 3.9.7
• Pydicom Version: Pydicom 2.3.0

Enhance the DICOM file comparison script by improving the handling of DICOM files. This includes implementing error handling mechanisms, validation checks, and robust file parsing to ensure reliable processing.

Streamline the deployment process of the data pipeline by incorporating best practices and automation techniques. This optimization effort will simplify setup procedures, automate deployment tasks, and ensure compatibility with various environments.

Create a script to transfer the anonymized and renamed DICOM files to the dataset-multimodal-breast repository. This script should ensure that the data is organized correctly in the target repository.

Acceptance Criteria:

• All anonymized DICOM files are copied to the target repository without data loss.
• Files are organized in the target repository according to predefined directory structures.
• Include error handling for transfer failures and logging for transfer activities.

We need to develop a script that anonymizes DICOM images by removing or obfuscating Personal Identifiable Information (PII) contained within the DICOM file headers. The anonymization process should comply with HIPAA guidelines and other relevant standards for data privacy.

Acceptance Criteria:

• Remove or obfuscate all PII from DICOM headers.
• Ensure the anonymized DICOM files retain their integrity for medical research purposes.
• Include unit tests to verify the anonymization process.

Extend the DICOM file comparison script to include comparison of instance numbers in addition to file equality. This feature enhancement will enhance the accuracy of matching criteria and improve the effectiveness of the script.

Provide comprehensive documentation for the anonymization and transfer process, including setup, execution instructions, and troubleshooting tips. This documentation will be essential for other researchers and contributors to understand and use the data-pipeline effectively.

Acceptance Criteria:

• Documentation includes step-by-step setup and execution instructions.
• Troubleshooting section for common issues.
• Clear description of the anonymization process and data transfer mechanism.

Address warnings generated during script execution, such as compatibility warnings for dependencies, by implementing proper warning suppression mechanisms. This bug fix will ensure smooth execution of the script without unnecessary warnings.