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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2024
Subtitle of host publicationComputer-Aided Diagnosis
EditorsWeijie Chen, Susan M. Astley
ISBN (Electronic)9781510671584
StatePublished - 2024
EventMedical Imaging 2024: Computer-Aided Diagnosis - San Diego, United States
Duration: Feb 19 2024Feb 22 2024

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2024: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego


  • computed tomography
  • image quality
  • quantitative imaging
  • radiomics

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Harmonizing quantitative imaging feature values in CT using image quality metrics as a basis'. Together they form a unique fingerprint.

Cite this