Image noise and dose performance across a clinical population: Patient size adaptation as a metric of CT performance.

Published

Journal Article

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.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.The data show different scanner dependencies in terms of adaptability: the RMSE values for the three scanners are between 0.0385 HU1/2 and 0.0215 HU1/2 .A theoretical relationship between image noise, CTDIvol , 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.

Full Text

Duke Authors

Cited Authors

  • Ria, F; Wilson, JM; Zhang, Y; Samei, E

Published Date

  • June 2017

Published In

Volume / Issue

  • 44 / 6

Start / End Page

  • 2141 - 2147

PubMed ID

  • 28235130

Pubmed Central ID

  • 28235130

Electronic International Standard Serial Number (EISSN)

  • 2473-4209

International Standard Serial Number (ISSN)

  • 0094-2405

Digital Object Identifier (DOI)

  • 10.1002/mp.12172

Language

  • eng