Modelling variations in CTDIvol data due to patient miscentring in Bayer Radimetrics™ data. Issue 7 (July 2016)

• Record Type: Journal Article
• Authors: Rowan, Michael; Brunell, Eimear; Murphy, Dara; Cournane, Sean
• Is Part Of: Physica medica. Volume 32:Issue 7(2016)
• Page Start: 957
• Publication Date: 2016-07
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2016.05.041
• Languages: English
• ISSNs: 1120-1797

Abstract

Abstract : It has previously been established that incorrect centring of patients in CT imaging, identified as a significant difference between the patient centroid and CT scanner isocentre, may lead to poorly optimised operation of the automatic exposure control (AEC). This may manifest as a magnification effect in the patient 2D scout scan, prompting an increased mAs to achieve adequate image quality, leading to unnecessary patient radiation exposure. Phantom studies have been conducted to investigate the effect; however, due to limited patient data, no study has quantified the relationship in the clinical setting. Bayer Radimetrics™ currently collates CT radiation dose metrics and scanning parameters from all CT scanners on the National Integrated Medical Imaging System (NIMIS); the data includes CTDIvol, kV, mAs, patient water equivalent diameter (WED), CT scanner isocentre and patient centroid. Accordingly, this study sought to evaluate the relationship between CTDIvol and patient centring in clinical population studies. Experimental phantom investigations were initially carried out for different adult and paediatric CT scanners, establishing the variation of CTDIvol with incremental miscentring for Thorax–Abdomen–Pelvis (TAP) and Abdomen/Pelvis (A/P) scanning protocols. Further, TAP and A/P population datasets, collated from Bayer Radimetrics™, were examined using multivariate analysis, identifying statistically significant predictors from the clinical studies. The model proved accurate in describing CTDIvol variation with patient centroid to isocenter distance, while accounting for patient WED parameter variations, or patient size. Modelled CTDIvol changes also concurred with the experimental results, highlighting the potential for the models use with dose tracking software packages