{'fraction_non_alphanumeric': 0.02471264367816092, 'fraction_numerical': 0.013793103448275862, 'mean_word_length': 5.0874125874125875, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Validation of noise estimation for a clinical contrast-to-noise ratio for digital mammographic imaging

Mammographic image quality is important to monitor to maximize diagnostic performance while minimizing patient exposure to ionizing radiation. Phantom imaging for quality control permits practical monitoring of signal and noise, and to optimize use of dose via the contrast-to-noise ratio (CNR). However, it remains a challenge to directly and objectively evaluate CNR in clinical images due to subject variability. A novel clinical image CNR metric has been developed that derives an estimate of system-dependent image noise and references contrast to tissue composition. The present work uses phantom images to validate the noise estimates and to demonstrate sensitivity to imaging conditions. Images of 1 cm adipose-equivalent blocks with 2, 3, and 5 cm 50/50 swirl phantoms and uniform 50/50 blocks were acquired using AEC-selected parameters, and at 0.33 and 0.5 of the AEC-selected mAs at 6 cm. Digital mammograms (DM) were acquired on a GE Essential with and without FineView processing, and in conventional and digital breast tomosynthesis (DBT) views on a Hologic Selenia Dimensions. The CNR was computed using contrast between a 0.4 mm CaCO3 speck in a target slab and adjacent background signal, and noise derived from paired raw and subtracted swirl phantom images. Swirl phantom CNR was estimated to within ±10% of uniform image CNR for GE and Hologic DM, and ±3% for Hologic DBT, and showed good sensitivity to acquisition technique. These results demonstrate promise for objective and efficient image quality evaluation from patient images, using noise estimates that effectively avoid signal related to tissue structure.