Acquisition parameters like kernel and noise produce axis-specific effects on lung-nodule AI (measurement vs detection) that a 4-feature pixel fingerprint can recover even when DICOM metadata cannot.
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MG-SpaIR introduces a multi-grade sparse-guided implicit neural representation framework for training-data-free image restoration that outperforms Deep Image Prior on mixed-degradation benchmarks.
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Acquisition state behaves as a structured, measurable variable governing lung-nodule AI: kernel-driven measurement instability and noise-driven detection fragility, invisible to DICOM metadata
Acquisition parameters like kernel and noise produce axis-specific effects on lung-nodule AI (measurement vs detection) that a 4-feature pixel fingerprint can recover even when DICOM metadata cannot.