DOI 10.1016/j.radi.2025.103303
Fundamental study of a novel image quality index for optimising image quality and dose in digital radiography systems.
Аннотация
INTRODUCTION
The lack of comprehensive image quality index in digital radiography systems is an issue that needs to be addressed. This paper proposes a novel image quality index and reports a fundamental study employing a phantom. This study introduced SNRIQ, which is a novel image quality index developed through a novel analytical algorithm and validated by phantom experiments.
METHODS
To validate the performance of the proposed index, i.e., SNRIQ, we conducted a comparative analysis using the conventional image quality index SNRCONV and EI values. Using different subject thicknesses, grids, and doses, the CDRAD phantoms and abdominal phantoms were imaged. Inverse of image quality figure (IQFinv) and the average psychological measurement values obtained by visual assessment were analyzed, and then compared with image quality indices.
RESULTS
The SNRIQ evaluation demonstrated a significantly strong correlation with IQFinv (R2 = 0.949) evaluation and visual assessments (R2 = 0.943). The SNRIQ is a promising alternative, which correlates strongly with genuine image quality assessments, and provides immediate feedback alongside acquired images. Conversely, the SNRCONV was inadequate as a comprehensive image quality index. The EI value served limited clinical purposes related to dose assessment, and it was inadequate as a comprehensive image quality index.
CONCLUSIONS
Our proposed SNRIQ was able to assess image quality more accurately than conventional image quality indices.
IMPLICATIONS FOR PRACTICE: By incorporating SNRIQ, which is superior to conventional image quality indices, into existing systems, optimization of both dose and image quality can be improved. This rectifies the EI value's limitations, supports image quality management for various patient profiles, and enables informed decisions on dose adjustment or re-imaging. Extending exposure indicators to include SNRIQ values can enhance examinations.
Предметные рубрики MeSH
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
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