Articles
Working notes on quantitative imaging, DICOM metadata quality, scanner variability and the validation of imaging AI. Written for people who build, curate and qualify imaging pipelines.
3 articles
What DICOM Metadata Guarantees About a Series, and What It Only Suggests
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.
DICOM & Metadata QualityScanner & Multi-site Variability
The Header Fields That Break Quantitative Analysis Across Sites
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.
DICOM & Metadata QualityQuantitative Imaging Biomarkers
Metadata Provenance in 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.
DICOM & Metadata QualityImaging AI Validation