A standardized data specification for reproducible, verifiable structured light and multispectral image sets

Spatial Frequency Domain Imaging (SFDI) is a quantitative wide-field imaging technique that uses light and diffuse optical spectroscopic principles to rapidly generate tissue optical property images. However, SFDI datasets present significant workflow challenges: they can be large (on the order of gigabytes), contain multiple image formats, and require computationally intensive processing. Additionally, SFDI images typically require calibration measurements, further complicating data organization. The proliferation of calibration data, imaging data, and processing options creates technical overhead and leads to reproducibility issues. While multiple "Open SFDI" initiatives exist, no standardized storage format has emerged, making the replication of published SFDI research difficult. Commercial solutions like Modulim's directory-based format contain trade secrets and lack open-source processing options. We are developing an open standard for SFDI images, extensible to multispectral images, dubbed SpectralMaps. This specification leverages modern Xarray data structures saved in Zarr format, offering fast loading of complete datasets or selected data chunks. Zarr permits lossless compression with partial loading of data, making data loading faster and partial data processing possible without loading the entire dataset. Metadata for measurements and processing history are included in the file. Datasets are identified by hashes included for verification. This specification provides a unified, transparent framework for storing, sharing, and processing SFDI data, reducing technical overhead and improving reproducibility across research groups. By adopting an open, extensible standard, the community can accelerate method development, enable cross-platform interoperability, and foster collaborative validation of SFDI techniques.