Fw-classification, an open-source package for MRI scan classification

Motivation: Our motivation is the challenge presented by the variability of DICOM metadata across different MRI scan manufacturers and protocols which can complicate scan type classification. Goal(s): To provide a simple yet versatile tool for the classification of MRI scan types, which enhances classification accuracy through a refined methodology. Approach: We use YAML-based declarative rules to process the arbitrary DICOM metadata, enabling nuanced categorization of MRI scans that can adapt to the mentioned variability. Results: The ability to accurately map complex combinations of metadata characteristics to define scan types and intrinsic features, thereby achieving a classification process that is both precise and quick. Impact: The fw-classification package simplifies the image classification workflow, minimizing potential for human error, and increasing throughput. This adaptable framework handles complex and heterogeneous metadata structures, which is necessary for robust classification across a variety of manufacturers and protocols.