CT beam dosimetric characterization procedure for personalized dosimetry

Abstract

Personalized dosimetry in computed tomography (CT) can be realized by a full Monte Carlo (MC) simulation of the scan procedure. Essential input data needed for the simulation are appropriate CT x-ray source models and a model of the patient’s body which is based on the CT image. The purpose of this work is to develop comprehensive procedures for the determination of CT x-ray source models and their verification by comparison of calculated and measured dose distributions in physical phantoms. Mobile equipment together with customized software was developed and used for non-invasive determination of equivalent source models of CT scanners under clinical conditions. Standard and physical anthropomorphic CT dose phantoms equipped with real-time CT dose probes at five representative positions were scanned. The accumulated dose was measured during the scan at the five positions. ImpactMC, an MC-based CT dose software program, was used to simulate the scan. The necessary inputs were obtained from the scan parameters, from the equivalent source models and from the material-segmented CT images of the phantoms. 3D dose distributions in the phantoms were simulated and the dose values calculated at the five positions inside the phantom were compared to measured dose values. Initial results were obtained by means of a General Electric Optima CT 660 and a Toshiba (Canon) Aquilion ONE. In general, the measured and calculated dose values were within relative uncertainties that had been estimated to be less than 10%. The procedures developed were found to be viable and rapid. The procedures are applicable to any scanner type under clinical conditions without making use of the service mode with stationary x-ray tube position. Results show that the procedures are well suited for determining and verifying the equivalent source models needed for personalized CT dosimetry based on post-scan MC calculations.

Patient-Specific Dose Estimates in Dynamic Computed Tomography Myocardial Perfusion Examination

“…Methods to acquire these parameters have been developed and tested in an earlier study. ( 2 ) In this study, Monte Carlo simulations and previously developed methods to acquire necessary scan parameters were used to evaluate patient-specific dose estimates for 20 patients in dynamic CT myocardial perfusion examination.…”

“…COBRA formalism (characterisation of bowtie relative attenuation) was used to determine bowtie filter shape, and x-ray spectra were calculated using aluminium attenuation measurements together with SpekCalc software tool and iterative algorithms. ( 2 , 11 , 12 ) The bowtie attenuation characteristics with inherent filtering were measured for the 80 kV spectrum for tube A and tube B and Al-equivalent bowtie filters were calculated, and the resulting thickness is used in the simulation presented in Figure 3 .…”

“…The distance from the focus to the centre of rotation was 595 mm, and the fan angle for tube A was 0.7955 rad and for tube B 0.5441 rad. Values for the Air KERMA free-in air at the iso-centre were measured ( 2 ) and used for the simulation: 5.66 mGy/(100 mAs) was applied for 80 kV simulations and 10.35 mGy/(100 mAs) for 100 kV simulations. The bowtie filter model for the 80 kV spectrum was used in the simulations.…”

“…Measured CT scanner radiation dose output is required to connect simulations to real patient dose. In this study, the CT scanner information is acquired with the procedure developed and tested in an earlier study, ( 2 ) where it was demonstrated that accuracy of dose (simulation vs. measurement) to within 10% is achievable in the applied anthropomorphic phantoms.…”

Simulation study on the conversion between CT and CBCT dose quantities via the effective dose

“…• Spectra: The x-ray spectra of the CT scanner were established by measurements using a procedure published by (Rosendahl et al 2019). The x-ray spectra of the CBCT scans were calculated using Spekpy-generated tungsten spectra with the nominal Al and Cu filtration as the starting point (Bujila et al 2020, Poludniowski et al 2021.…”