Estimation of in vivo noise in clinical CT images: comparison and validation of three different methods against ensemble noise gold-standard

Francesco Ria; Taylor B. Smith; Ehsan Abadi; Justin B. Solomon; Ehsan Samei

doi:10.1117/12.2580954

15 February 2021 Estimation of in vivo noise in clinical CT images: comparison and validation of three different methods against ensemble noise gold-standard

Francesco Ria, Taylor B. Smith, Ehsan Abadi, Justin B. Solomon, Ehsan Samei

Author Affiliations +

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 115952P (2021) https://doi.org/10.1117/12.2580954
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Image quality estimation is crucial in modern CT with noise magnitude playing a key role. Several methods have been proposed to estimate noise surrogates in vivo. This study aimed to ascertain the accuracy of three different noise-magnitude estimation methods. We used ensemble noise as the ground truth. The most accurate approach to assess ensemble noise is to scan a patient repeatedly and assess the noise for each pixel across the ensemble of images. This process is ethically undoable on actual patients. In this study, we surmounted this impasse using Virtual Imaging Trials (VITs) that simulate clinical scenarios using computer-based simulations. XCAT phantoms were imaged 47 times using a scanner-specific simulator (DukeSim) and reconstructed with filtered back projection (FBP) and iterative (IR) algorithms. Noise magnitudes were calculated in lung (ROIn), soft tissues (GNI), and air surrounding the patient (AIRn), applying different HU thresholds and techniques. The results were compared with the ensemble noise magnitudes within soft tissue (En). For the FBP-reconstructed images, median En was 30.6 HU; median ROIn was 46.6 HU (+52%), median GNI was 40.1 HU (+31%), and median AIRn 25.1 HU (-18%). For the IR images, median En was 19.5 HU; median ROIn was 31.2 HU (+60%), median GNI was 25.1 HU (+29%), and median AIRn 18.8 HU (-4%). Compared to ensemble noise, GNI and ROIn overestimate the tissue noise, while AIRn underestimates it. Air noise was least representative of variations in tissue noise due to imaging condition. These differences may be applied as adjustment or calibration factors to better represent clinical results.

Conference Presentation

Citation Download Citation

Francesco Ria, Taylor B. Smith, Ehsan Abadi, Justin B. Solomon, and Ehsan Samei "Estimation of in vivo noise in clinical CT images: comparison and validation of three different methods against ensemble noise gold-standard", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115952P (15 February 2021); https://doi.org/10.1117/12.2580954

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available