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Medical University of South Carolina
DICOMAnon helps imaging teams anonymize, batch process, and automate DICOM workflows without writing custom scripts.
Neck CT protocols often present challenges for objective lesion detectability tasks. We systematically assessed lesion detectability in clinical scans to identify quality gaps and dose utilization inefficiencies.
To evaluate neck CT image quality (IQ) and dose efficiency for lesion assessment across multiple scanners, identifying scanner-level differences and protocol optimization opportunities.
Manual background selection for contrast-to-noise ratio (CNR) calculations in CT image quality assessment is time-consuming, operator-dependent, and introduces >15% measurement variability that compromises reproducibility. Advanced metrics such as Noise Power...