Prospective optimization of CT under tube current modulation: I. organ dose

In an environment in which computed tomography (CT) has become an indispensable diagnostic tool employed with great frequency, dose concerns at the population level have become a subject of public attention. In that regard, optimizing radiation dose has become a core problem to the CT community. As a fundamental step to optimize radiation dose, it is crucial to effectively quantify radiation dose for a given CT exam. Such dose estimates need to be patient-specific to reflect individual radiation burden. It further needs to be prospective so that the scanning parameters can be dynamically adjusted before the scan is performed. The purpose of this study was to prospectively estimate organ dose in abdominopelvic CT exams under tube current modulation (TCM). CTDIvol-normalized-organ dose coefficients ( hfixed ) for fixed tube current were first estimated using a validated Monte Carlo simulation program and 58 computational phantoms. To account for the effect of TCM scheme, a weighted CTDIvol was computed for each organ based on the tube current modulation profile. The organ dose was predicted by multiplying the weighted CTDIvol with the organ dose coefficients ( hfixed ). To quantify prediction accuracy, each predicted organ dose was compared with organ dose simulated from Monte Carlo program with TCM profile explicitly modeled. The predicted organ dose showed good agreement with simulated organ dose across all organs and modulation strengths. For an average CTDIvol of a CT exam of 10 mGy, the absolute median error across all organs were 0.64 mGy (-0.21 and 0.97 for 25th and 75th percentiles, respectively). The percentage differences (normalized by CTDIvol of the exam) were within 15%. This study developed a quantitative model to predict organ dose under clinical abdominopelvic scans. Such information may aid in the optimization of CT protocols.

[1]  Ehsan Samei,et al.  Dose coefficients in pediatric and adult abdominopelvic CT based on 100 patient models , 2013, Physics in medicine and biology.

[2]  Joel G Fletcher,et al.  In defense of body CT. , 2009, AJR. American journal of roentgenology.

[3]  Katsuyuki Taguchi,et al.  Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT. , 2012, Radiology.

[4]  Ehsan Samei,et al.  The impact on CT dose of the variability in tube current modulation technology: a theoretical investigation , 2014, Physics in medicine and biology.

[5]  D. Brenner,et al.  Computed tomography--an increasing source of radiation exposure. , 2007, The New England journal of medicine.

[6]  Ehsan Samei,et al.  Patient-specific radiation dose and cancer risk for pediatric chest CT. , 2011, Radiology.

[7]  W P Segars,et al.  Population of anatomically variable 4D XCAT adult phantoms for imaging research and optimization. , 2013, Medical physics.

[8]  J. Sempau,et al.  Experimental benchmarks of the Monte Carlo code penelope , 2003 .

[9]  Maria Zankl,et al.  Reducing radiation dose to selected organs by selecting the tube start angle in MDCT helical scans: a Monte Carlo based study. , 2009, Medical physics.

[10]  J. Baró,et al.  PENELOPE: An algorithm for Monte Carlo simulation of the penetration and energy loss of electrons and positrons in matter , 1995 .

[11]  W. Paul Segars,et al.  Patient-specific radiation dose and cancer risk estimation in CT: part II. Application to patients. , 2010, Medical physics.

[12]  W P Segars,et al.  Realistic CT simulation using the 4D XCAT phantom. , 2008, Medical physics.