https://measuredimaging.com/Quantitative medical imaging, imaging data quality, DICOM metadata, and the validation of imaging AI.en-us© 2026 AGCPMon, 17 Aug 2026 00:00:00 +0000https://measuredimaging.com/articles/what-dicom-metadata-guarantees/Mon, 17 Aug 2026 00:00:00 +0000https://measuredimaging.com/articles/what-dicom-metadata-guarantees/Type 1, 2 and 3 attributes carry very different obligations, and almost every field a quantitative pipeline depends on sits in the weakest tier. A working taxonomy of which header values you can rely on and which you must verify against the pixel data.https://measuredimaging.com/articles/dicom-header-fields-that-break-quantitative-analysis/Sun, 16 Aug 2026 00:00:00 +0000https://measuredimaging.com/articles/dicom-header-fields-that-break-quantitative-analysis/Multi-site quantitative studies rarely fail on the obvious parameters. They fail on rescale handling, reconstruction kernels, units declarations and private diffusion tags. A field-by-field account of where cross-site comparability is actually lost.https://measuredimaging.com/articles/metadata-provenance-imaging-ai-validation-sets/Sat, 15 Aug 2026 00:00:00 +0000https://measuredimaging.com/articles/metadata-provenance-imaging-ai-validation-sets/A validation cohort is a claim about independence. De-identification, format conversion and re-curation routinely destroy the evidence needed to support that claim, and the destruction is invisible in every metric the model reports.