Low-dose CT allows for accurate proton therapy dose calculation and plan optimization.

Objective.Protons offer a more conformal dose delivery compared to photons, yet they are sensitive to anatomical changes over the course of treatment. To minimize range uncertainties due to anatomical variations, a new CT acquisition at every treatment session would be paramount to enable daily dose calculation and subsequent plan adaptation. However, the series of CT scans results in an additional accumulated patient dose. Reducing CT radiation dose and thereby decreasing the potential risk of radiation exposure to patients is desirable, however, lowering the CT dose results in a lower signal-to-noise ratio and therefore in a reduced quality image. We hypothesized that the signal-to-noise ratio provided by conventional CT protocols is higher than needed for proton dose distribution estimation. In this study, we aim to investigate the effect of CT imaging dose reduction on proton therapy dose calculations and plan optimization.Approach.To verify our hypothesis, a CT dose reduction simulation tool has been developed and validated to simulate lower-dose CT scans from an existing standard-dose scan. The simulated lower-dose CTs were then used for proton dose calculation and plan optimization and the results were compared with those of the standard-dose scan. The same strategy was adopted to investigate the effect of CT dose reduction on water equivalent thickness (WET) calculation to quantify CT noise accumulation during integration along the beam.Main results.The similarity between the dose distributions acquired from the low-dose and standard-dose CTs was evaluated by the dose-volume histogram and the 3D Gamma analysis. The results on an anthropomorphic head phantom and three patient cases indicate that CT imaging dose reduction up to 90% does not have a significant effect on proton dose calculation and plan optimization. The relative error was employed to evaluate the similarity between WET maps and was found to be less than 1% after reducing the CT imaging dose by 90%.Significance.The results suggest the possibility of using low-dose CT for proton therapy dose estimation, since the dose distributions acquired from the standard-dose and low-dose CTs are clinically equivalent.

Low-dose x-ray CT simulation from an available higher-dose scan.

In computed tomography (CT)-imaging an optimal compromise between the radiation burden and the image quality for the imaging task is needed. Lower-dose CT is desirable, however, lowering the dose results in a lower signal-to-noise ratio and therefore in a reduced image quality. In this research, we aim to develop a tool to simulate lower-dose scans from an existing standard-dose scan. The main application of this tool is to determine the lowest possible radiation dose that still produces sufficient clinical information. The x-ray tube current reduction is modeled by estimating the noise equivalent number of photons in the high exposure scan and applying a thinning technique to reduce that number. The proposed method accounts for the bowtie filter, for the electronic system noise, for the noise correlation between neighboring detector elements, for the beam hardening effect, and for the non-linear smoothing filter in very low-dose scans. Several phantom studies with different acquisition protocols were performed to evaluate the accuracy of the proposed framework. The results demonstrate a close agreement between the noise magnitude and texture of the measured and the simulated lower-dose scans. For instance, the standard deviation of noise in the simulation of lower-dose scans with 90% tube current reduction matches the reconstructions from the real scans with less than 1% and 3% error for sequential and helical scans, respectively. The noise texture was also assessed by analyzing the noise power spectrum of the simulated lower-dose images which matches those from the real scans. Furthermore, the relation between the measured and predicted noise in projection domain is very close to the line of identity which confirms the accuracy of the model.