Olav Christianson1, James Winslow1, Donald P Frush2, Ehsan Samei1. 1. 1 Ravin Advanced Imaging Laboratories, Duke University, 2424 Erwin Rd, Ste 302, Durham, NC 27705. 2. 2 Department of Radiology, Duke University Medical Center, Durham, NC.
Abstract
OBJECTIVE: The purpose of this study was to develop and validate an automated method to measure noise in clinical CT examinations. MATERIALS AND METHODS: An automated algorithm was developed to measure noise in CT images. To assess its validity, the global noise level was compared with image noise measured using an image subtraction technique in an anthropomorphic phantom. The global noise level was further compared with image noise values from clinical patient CT images obtained by an observer study. Finally, the clinical utility of the global noise level was shown by assessing variability of image noise across scanner models for abdominopelvic CT examinations performed in 2358 patients. RESULTS: The global noise level agreed well with the phantom-based and clinical image-based noise measurements, with an average difference of 3.4% and 4.7% from each of these measures, respectively. No significant difference was detected between the global noise level and the validation dataset in either case. It further indicated differences across scanners, with the median global noise level varying significantly between different scanner models (15-35%). CONCLUSION: The global noise level provides an accurate, robust, and automated method to measure CT noise in clinical examinations for quality assurance programs. The significant difference in noise across scanner models indicates the unexploited potential to efficiently assess and subsequently improve protocol consistency. Combined with other automated characterization of imaging performance (e.g., dose monitoring), the global noise level may offer a promising platform for the standardization and optimization of CT protocols.
OBJECTIVE: The purpose of this study was to develop and validate an automated method to measure noise in clinical CT examinations. MATERIALS AND METHODS: An automated algorithm was developed to measure noise in CT images. To assess its validity, the global noise level was compared with image noise measured using an image subtraction technique in an anthropomorphic phantom. The global noise level was further compared with image noise values from clinical patient CT images obtained by an observer study. Finally, the clinical utility of the global noise level was shown by assessing variability of image noise across scanner models for abdominopelvic CT examinations performed in 2358 patients. RESULTS: The global noise level agreed well with the phantom-based and clinical image-based noise measurements, with an average difference of 3.4% and 4.7% from each of these measures, respectively. No significant difference was detected between the global noise level and the validation dataset in either case. It further indicated differences across scanners, with the median global noise level varying significantly between different scanner models (15-35%). CONCLUSION: The global noise level provides an accurate, robust, and automated method to measure CT noise in clinical examinations for quality assurance programs. The significant difference in noise across scanner models indicates the unexploited potential to efficiently assess and subsequently improve protocol consistency. Combined with other automated characterization of imaging performance (e.g., dose monitoring), the global noise level may offer a promising platform for the standardization and optimization of CT protocols.
Authors: Gianluca De Rubeis; Adriane E Napp; Peter Schlattmann; Jacob Geleijns; Michael Laule; Henryk Dreger; Klaus Kofoed; Mathias Sørgaard; Thomas Engstrøm; Hans Henrik Tilsted; Alberto Boi; Michele Porcu; Stefano Cossa; José F Rodríguez-Palomares; Filipa Xavier Valente; Albert Roque; Gudrun Feuchtner; Fabian Plank; Cyril Štěchovský; Theodor Adla; Stephen Schroeder; Thomas Zelesny; Matthias Gutberlet; Michael Woinke; Mihály Károlyi; Júlia Karády; Patrick Donnelly; Peter Ball; Jonathan Dodd; Mark Hensey; Massimo Mancone; Andrea Ceccacci; Marina Berzina; Ligita Zvaigzne; Gintare Sakalyte; Algidas Basevičius; Małgorzata Ilnicka-Suckiel; Donata Kuśmierz; Rita Faria; Vasco Gama-Ribeiro; Imre Benedek; Teodora Benedek; Filip Adjić; Milenko Čanković; Colin Berry; Christian Delles; Erica Thwaite; Gershan Davis; Juhani Knuuti; Mikko Pietilä; Cezary Kepka; Mariusz Kruk; Radosav Vidakovic; Aleksandar N Neskovic; Iñigo Lecumberri; Ignacio Diez Gonzales; Balazs Ruzsics; Mike Fisher; Marc Dewey; Marco Francone Journal: Eur Radiol Date: 2019-12-16 Impact factor: 5.315
Authors: Jennifer S Ngo; Justin B Solomon; Ehsan Samei; Taylor Richards; Lawrence Ngo; Alaattin Erkanli; Bohui Zhang; Brian C Allen; Joseph T Davis; Amrita Devalapalli; Raymond Groller; Daniele Marin; Charles M Maxfield; Vishwan Pamarthi; Bhavik N Patel; Gary R Schooler; Donald P Frush Journal: Radiol Imaging Cancer Date: 2019-09-27
Authors: Adam Wang; Ian Cunningham; Mats Danielsson; Rebecca Fahrig; Thomas Flohr; Christoph Hoeschen; Frederic Noo; John M Sabol; Jeffrey H Siewerdsen; Anders Tingberg; John Yorkston; Wei Zhao; Ehsan Samei Journal: J Med Imaging (Bellingham) Date: 2022-03-16