A real-time Monte Carlo tool for individualized dose estimations in clinical CT

Abstract

The increasing awareness of the adverse effects associated with radiation exposure in computed tomography (CT) has necessesitated the quantification of dose delivered to patients for better risk assessment in the clinic. The current methods for dose quantification used in the clinic are approximations, lacking realistic models for the irradiation conditions utilized in the scan and the anatomy of the patient being imaged, which limits their relevance for a particular patient. The established gold-standard technique for individualized dose quantification uses Monte Carlo (MC) simulations to obtain patient-specific estimates of organ dose in anatomically realistic computational phantoms to provide patient-specific estimates of organ dose. Although accurate, MC simulations are computationally expensive, which limits their utility for time-constrained applications in the clinic. To overcome these shortcomings, a real-time GPU-based MC tool based on FDA's MC-GPU framework was developed for patient and scanner-specific dosimetry in the clinic. The tool was validated against (1) AAPM's TG-195 reference datasets and (2) physical measurements of dose acquired using TLD chips in adult and pediatric anthropomorphic phantoms. To demonstrate its utility towards providing individualized dose estimates, it was integrated with an automatic segmentation method for generating patient-specific models, which were then used to estimate patient-and scanner-specific organ doses for a select population of 50 adult patients using a clinically relevant CT protocol. The organ dose estimates were compared to corresponding dose estimates from a previously validated MC method based on Penelope. The dose estimates from our MC tool agreed within 5% for all organs (except thyroid) tabulated by TG-195 and within 10% for all TLD locations in the adult and pediactric phantoms, across all validation cases. Compared against Penelope, the organ dose estimates agreed within 3% on average for all organs in the patient population study. The average run duration for each patient 6

Deterministic linear Boltzmann transport equation solver for patient‐specific CT dose estimation: Comparison against a Monte Carlo benchmark for realistic scanner configurations and patient models

“…At this regard, to compare measured doses with simulated doses, with the latter given in terms of dose per unit photon, a factor is needed to account for the number of photons emitted by the scanner during the procedure. This factor can be obtained from the ratio of the CTDI vol provided by the CT scanner during the CT examination and the CTDI vol estimated by simulating the CTDI phantom, as has been performed for previous Monte Carlo validation studies 28 …”

“…The total CPU run time depended on the parallelization and no attempts were made to improve code efficiency. However, recent works on accelerated Monte Carlo codes running on GPUs have reported simulation times of down to a few seconds 28,29 …”

“…However, recent works on accelerated Monte Carlo codes running on GPUs have reported simulation times of down to a few seconds. 28,29 Acuros is not subject to statistical fluctuations typical of Monte Carlo simulation, and consequently its dose maps are virtually free from photon noise. Furthermore, in this study, phantoms were already segmented into organs, facilitating the calculation of organ doses from the dose maps.…”

“…This factor can be obtained from the ratio of the CTDI vol provided by the CT scanner during the CT examination and the CTDI vol estimated by simulating the CTDI phantom, as has been performed for previous Monte Carlo validation studies. 28 Author to whom correspondence should be addressed. Electronic mail: sprincipi@mcw.edu.…”

Virtual clinical trials in medical imaging: a review

“…They should ideally take place at multiple levels of granularity. They can be applied to a simulation in its subcomponents 41,369,370 (e.g., model of x-ray spectrum), whole component 141,142,262,371,372 (e.g., accuracy in creating realistic simulated images), or multicomponent 26,278 (e.g., accuracy in creating the complete human imaging process from the patient to the output of the imaging task). One approach for these validations has been through the simulation of IEC standard tests.…”