MDIsDPI

MDIsDPI is a proposed framework intended to unify processing and interchange of imaging data across multiple domains, including medical, geospatial, and industrial imaging. The term appears in speculative literature and research proposals as a label for an interoperable pipeline that ingests data from diverse modalities, applies shared processing once, and delivers consistent results to various display systems. It is not a widely standardized or implemented technology at present.

Core concepts include a flexible, extensible data model, modular processing components, and adapters that translate modality-specific data into the common model. A central Data Interchange Format, sometimes referred to as MDIf, enables metadata, provenance, and quality metrics to accompany images throughout the pipeline. The aim is to preserve data fidelity while enabling cross-domain reuse.

Architecturally, MDIsDPI describes a layered stack: a data layer that stores raw and derived data with rich metadata; an abstraction layer that maps modality schemas to the common model; a processing layer with reproducible components for preprocessing, reconstruction, analysis, and visualization; and a presentation layer that renders results consistently across devices.

Applications span clinical imaging, crop or land-use monitoring, automated inspection in manufacturing, and scientific visualization. Potential benefits include reduced integration effort, improved traceability, and the ability to combine complementary information from different sources. Limitations include lack of formal standardization, interoperability challenges, and performance constraints in large-scale deployments.

Historically, MDIsDPI emerged in the early 2020s in cross-disciplinary discussions about data interoperability in imaging. As of now, it remains largely experimental, with pilot implementations reported in academic settings and industry labs. The concept continues to influence discussions on open data models and cross-domain imaging standards.