CT Dosimetry What Has Been Achieved and What Remains to Be Done

Radiation dose in computed tomography (CT) has become a hot topic due to an upward trend in the number of CT procedures worldwide and the relatively high doses associated with these procedures. The main aim of this review article is to provide an overview of the most frequently used metrics for CT radiation dose characterization, discuss their strengths and limitations, and present patient dose assessment methods. Computed tomography dosimetry is still based on a CT dose index (CTDI) measured using 100-mm-long pencil ionization chambers and standard dosimetry phantoms (CTDI100). This dose index is easily measured but has important limitations. Computed tomography dose index underestimates the dose generated by modern CT scanners with wide beam collimation. Manufacturers should report corrected CTDI values in the consoles of CT systems. The size-specific dose estimate has been proposed to provide an estimate of the average dose at the center of the scan volume along the z-axis of a CT scan. Size-specific dose estimate is based on CTDI and conversion factors and, therefore, its calculation incorporates uncertainties associated with the measurement of CTDI. Moreover, the calculation of size-specific dose estimate is straightforward only when the tube current modulation is not activated and when the patient body diameter does not change considerably along the z-axis of the scan. Effective dose can be used to provide typical patient dose values from CT examinations, compare dose between modalities, and communicate radiogenic risks. In practice, effective dose has been used incorrectly, for example, to characterize a CT procedure as a low-dose examination. Organ or tissue doses, not effective doses, are required for assessing the probability of cancer induction in exposed individuals. Monte Carlo simulation is a powerful technique to estimate organ and tissue dose from CT. However, vendors should make available to the research community the required information to model the imaging process of their CT scanners. Personalized dosimetry based on Monte Carlo simulation and patient models allows accurate organ dose estimation. However, it is not user friendly and fast enough to be applied routinely. Future research efforts should involve the development of advanced artificial intelligence algorithms to overcome drawbacks associated with the current equipment-specific and patient-specific dosimetry.