Sean Cournane1, Eimear Brunell2, Michael Rowan3. 1. 1 Department of Medical Physics and Bioengineering, St James's hospital , Dublin , Ireland. 2. 2 Department Medical Physics and Clinical engineering, Tallaght University Hospital , Dublin , Ireland. 3. 3 Department of Radiology, Mater Misericordiae Hospital , Dublin , Ireland.
Abstract
OBJECTIVES: The work aimed to investigate and model the relationship between CT dose index (CTDIvol), patient size and miscentring metrics using data extracted from a dose tracking software tool. Further, using the established model we aimed to extract and estimate further AEC metrics and, finally, demonstrate how the CTDIvol may vary across a range of imaging exams and with variations in patient size, positional metrics and with scan parameters. METHODS: CT scan parameters, patient dose metrics, patient positioning information and patient and water equivalent diameter information was extracted from abdomen, thorax-abdomen-pelvis and thorax imaging exams acquired on a 128-slice Siemens Somatom Definition AS + CT system using dose tracking software over a 1 year period. A miscentring factor, accounting for the magnification of the patient due to miscentring was derived. Significant predictors ( p-value < 0.001) of the CTDIvol were considered as inputs into the model following regression analysis. RESULTS: The model was capable of describing the CTDIvol and its variation with patient miscentring and patient size. AEC parameters, such as the reference CTDIvol, reference diameter and the AEC strength were estimated. Further, the model allowed for comparisons of how changes in scan settings, such as kVp, pitch and slice thickness affected CTDIvol. CONCLUSIONS: We demonstrate the use of clinical data, extracted from dose tracking software, to investigate and monitor AEC behaviour and CT output. ADVANCES IN KNOWLEDGE: The presented model allows for the remote investigation of AEC behaviour using patient data.
OBJECTIVES: The work aimed to investigate and model the relationship between CT dose index (CTDIvol), patient size and miscentring metrics using data extracted from a dose tracking software tool. Further, using the established model we aimed to extract and estimate further AEC metrics and, finally, demonstrate how the CTDIvol may vary across a range of imaging exams and with variations in patient size, positional metrics and with scan parameters. METHODS: CT scan parameters, patient dose metrics, patient positioning information and patient and water equivalent diameter information was extracted from abdomen, thorax-abdomen-pelvis and thorax imaging exams acquired on a 128-slice Siemens Somatom Definition AS + CT system using dose tracking software over a 1 year period. A miscentring factor, accounting for the magnification of the patient due to miscentring was derived. Significant predictors ( p-value < 0.001) of the CTDIvol were considered as inputs into the model following regression analysis. RESULTS: The model was capable of describing the CTDIvol and its variation with patient miscentring and patient size. AEC parameters, such as the reference CTDIvol, reference diameter and the AEC strength were estimated. Further, the model allowed for comparisons of how changes in scan settings, such as kVp, pitch and slice thickness affected CTDIvol. CONCLUSIONS: We demonstrate the use of clinical data, extracted from dose tracking software, to investigate and monitor AEC behaviour and CT output. ADVANCES IN KNOWLEDGE: The presented model allows for the remote investigation of AEC behaviour using patient data.
Authors: Willi A Kalender; Stefanie Buchenau; Paul Deak; Markus Kellermeier; Oliver Langner; Marcel van Straten; Sabrina Vollmar; Sylvia Wilharm Journal: Phys Med Date: 2008-03-10 Impact factor: 2.685
Authors: Cynthia McCollough; Donovan M Bakalyar; Maryam Bostani; Samuel Brady; Kristen Boedeker; John M Boone; H Heather Chen-Mayer; Olav I Christianson; Shuai Leng; Baojun Li; Michael F McNitt-Gray; Roy A Nilsen; Mark P Supanich; Jia Wang Journal: AAPM Rep Date: 2014-09