Assessment of cervical spine CT by an image quality audit using qualitative and quantitative methods

Hooryia Bajwa; Thenugaa Sritharan; Tanita Botha; Price Jackson; Kim McAnulty; Lincoln J Lim; Phillip V Tran; Shane Reeves; Leah Biffin; Harish Narayanan

doi:10.1111/1754-9485.13791

Assessment of cervical spine CT by an image quality audit using qualitative and quantitative methods

J Med Imaging Radiat Oncol. 2024 Oct 31. doi: 10.1111/1754-9485.13791. Online ahead of print.

Authors

Hooryia Bajwa^{1

2}, Thenugaa Sritharan¹, Tanita Botha^{1

3}, Price Jackson², Kim McAnulty¹, Lincoln J Lim^{1

4}, Phillip V Tran¹, Shane Reeves¹, Leah Biffin¹, Harish Narayanan¹

Affiliations

¹ Department of Medical Imaging, Western Health, Melbourne, Victoria, Australia.
² Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
³ Biostatistics Unit, Faculty of Health, Deakin University, Geelong, Victoria, Australia.
⁴ Department of Surgery, Western Precinct, The University of Melbourne, Melbourne, Victoria, Australia.

PMID: 39482850
DOI: 10.1111/1754-9485.13791

Abstract

Introduction: To study the feasibility and assess the correlation of qualitative and quantitative methods for an image quality (IQ) audit of a Cervical spine CT.

Methods: Five radiologists retrospectively performed a blinded visual grading analysis (VGA) on 20 studies (10 from Protocol 1 and 10 from Protocol 2), using the RANZCR CT IQ Self-Audit worksheet. A Visual Grading Analysis Score (VGAS) and Area under the curve using Visual Grading Characteristics (AUC_VGC) were the figures of merit. Quantitative metrics for noise and contrast were correlated to the qualitative assessment.

Results: No statistically significant difference was observed in the IQ, VGAS_{Protocol 1} = 0.65, 95% CI [0.54, 0.75] and VGAS_{Protocol 2} = 0.73, 95% CI [0.67, 0.79] and AUC_VGC = 0.548, 95% CI [0.40, 0.69]. Protocol 2 indicated a statistically significant average dose reduction of 35% in CTDI_vol (P = 0.020) and a higher noise; however, the difference was statistically insignificant. There was a moderate correlation between the manual noise measurements in soft tissue and air (P = 0.035) and a strong correlation between the manual and automated noise measurements (P < 0.001). The contrast resolution-based quantitative parameter, EdgeGradientSoft, correlated to the qualitative scores (P = 0.031).

Conclusion: Validated VGA tools can be used for IQ audits; however, tailoring the image criteria and rating scale to the clinical practice is suggested. The use of contrast-based IQ metrics showed encouraging results, and further larger-scale studies are needed to explore their potential use in quality management.

Keywords: RANZCR CT Image Quality Self‐Audit worksheet; cervical spine CT; image quality audit; qualitative and quantitative assessment; visual grading analysis.