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Image noise and dose performance across a clinical population: Patient size adaptation as a metric of CT performance
Author(s) -
Ria Francesco,
Wilson Joshua Mark,
Zhang Yakun,
Samei Ehsan
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12172
Subject(s) - metric (unit) , adaptation (eye) , medical imaging , medical physics , noise (video) , dosimetry , population , medicine , nuclear medicine , computer science , artificial intelligence , radiology , image (mathematics) , optics , physics , engineering , operations management , environmental health
Purpose Modern CT systems adjust X‐ray flux accommodating for patient size to achieve certain image noise values. The effectiveness of this adaptation is an important aspect of CT performance and should ideally be characterized in the context of real patient cases. The objective of this study was to characterize CT performance with a new metric that includes image noise and radiation dose across a clinical patient population. Materials and methods The study included 1526 examinations performed by three CT scanners (one GE Healthcare Discovery CT750HD, one GE Healthcare Lightspeed VCT, and one Siemens SOMATOM definition Flash) used for two routine clinical protocols (abdominopelvic with contrast and chest without contrast). An institutional monitoring system recorded all the data involved in the study. The dose–patient size and noise–patient size dependencies were linearized by considering a first‐order approximation of analytical models that describe the relationship between ionization dose and patient size, as well as image noise and patient size. A 3D‐fit was performed for each protocol and each scanner with a planar function, and the root mean square error (RMSE) values were estimated as a metric of CT adaptability across the patient population. Results The data show different scanner dependencies in terms of adaptability: the RMSE values for the three scanners are between 0.0385 HU 1/2 and 0.0215 HU 1/2 . Conclusion A theoretical relationship between image noise, CTDI vol , and patient size was determined based on real patient data. This relationship may be interpreted as a new metric related to the scanners' adaptability concerning image quality and radiation dose across a patient population. This method could be implemented to investigate the adaptability related to other image quality indexes and radiation dose in a clinical population.