Harmonizing quantitative imaging feature values in CT using image quality metrics as a basis

Morgan A. Daly; John M. Hoffman; Andrew M. Hernandez; Ali Uneri; Jeffrey H. Siewerdsen; Paul E. Kinahan; Nicholas B. Bevins; Kalpana M. Kanal; David A. Zamora; Benjamin W. Maloney; J. Anthony Seibert; Mark P. Supanich; M. Mahesh; John M. Boone; Michael F. McNitt-Gray

doi:10.1117/12.3006885

3 April 2024 Harmonizing quantitative imaging feature values in CT using image quality metrics as a basis

Morgan A. Daly, John M. Hoffman, Andrew M. Hernandez, Ali Uneri, Jeffrey H. Siewerdsen, Paul E. Kinahan, Nicholas B. Bevins, Kalpana M. Kanal, David A. Zamora, Benjamin W. Maloney, J. Anthony Seibert, Mark P. Supanich, M. Mahesh, John M. Boone, Michael F. McNitt-Gray

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129271B (2024) https://doi.org/10.1117/12.3006885
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

This study is an initial investigation into methods to harmonize quantitative imaging (QI) feature values across CT scanners based on image quality metrics. To assess the impact of harmonization on QI features, we: (1) scanned an image quality assessment phantom on three scanners over a wide range of acquisition and reconstruction conditions; (2) from those scans, assessed image quality for each scanner at each acquisition and reconstruction condition; (3) from these assessments, identified a set of parameters for each scanner that yielded similar image quality values (“harmonized condition”); (4) scanned a second phantom with texture (i.e., local variations in attenuation) under the same set of conditions; and (5) extracted QI features and compared values between non-harmonized and harmonized image quality conditions. Quantitative image quality assessments provided contrast to noise ratio (CNR) and modulation transfer function frequency at 50% (MTF f50) values for each scanner and each condition used. A set of harmonized conditions was identified across three CT scanners based on the similarity of CNR and MTF f₅₀. To provide a comparison, several non-harmonized condition sets were identified. From the texture phantom, the standard deviation of the QI feature values (intensity mean and variance, GLCM autocorrelation and cluster tendency, GLDM high and low gray level emphasis) across the three CT systems decreased between 72.8% and 81.1% between the unharmonized and harmonized groups (with exception of intensity mean which showed little difference across scanners). These initial results suggest that selecting protocols that produce similar quantitative image quality metric values across different CT systems can reduce the variance of QI feature values across those systems.

Conference Presentation

Citation Download Citation

Morgan A. Daly, John M. Hoffman, Andrew M. Hernandez, Ali Uneri, Jeffrey H. Siewerdsen, Paul E. Kinahan, Nicholas B. Bevins, Kalpana M. Kanal, David A. Zamora, Benjamin W. Maloney, J. Anthony Seibert, Mark P. Supanich, M. Mahesh, John M. Boone, and Michael F. McNitt-Gray "Harmonizing quantitative imaging feature values in CT using image quality metrics as a basis", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129271B (3 April 2024); https://doi.org/10.1117/12.3006885

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