Poor image quality constitutes a major source of unnecessary radiation to patients in developing countries. Comparison with other surveys indicates that patient dose levels in these countries are not higher than those in developed countries.

Extending cone-beam CT (CBCT) use toward dose accumulation and adaptive radiotherapy (ART) necessitates more accurate HU reproduction since cone-beam geometries are heavily degraded by photon scatter. This study proposes a novel method which aims to demonstrate how deep learning based on phantom data can be used effectively for CBCT intensity correction in patient images. Four anthropomorphic phantoms were scanned on a CBCT and conventional fan-beam CT system. Intensity correction is performed by estimating the cone-beam intensity deviations from prior information contained in the CT. Residual projections were extracted by subtraction of raw cone-beam projections from virtual CT projections. An improved version of U-net is utilized to train on a total of 2001 projection pairs. Once trained, the network could estimate intensity deviations from input patient head and neck raw projections. The results from our novel method showed that corrected CBCT images improved the (contrast-to-noise ratio) with respect to uncorrected reconstructions by a factor of 2.08. The mean absolute error and structural similarity index improved from 318 HU to 74 HU and 0.750 to 0.812 respectively. Visual assessment based on line-profile measurements and difference image analysis indicate the proposed method reduced noise and the presence of beam-hardening artefacts compared to uncorrected and manufacturer reconstructions. Projection domain intensity correction for cone-beam acquisitions of patients was shown to be feasible using a convolutional neural network trained on phantom data. The method shows promise for further improvements which may eventually facilitate dose monitoring and ART in the clinical radiotherapy workflow.

To determine the relationship between imaging frequencies and prostate motion during CyberKnife stereotactic body radiotherapy (SBRT) for prostate cancer. Methods: Intrafraction displacement data for 331 patients who received treatment with CyberKnife for prostate cancer were retrospectively analysed. Prostate positions were tracked with a large variation in imaging frequencies. The percent of treatment time that patients remained inside various motion thresholds for both real and simulated imaging frequencies was calculated. Results: 84,920 image acquisitions over 1635 fractions were analysed. Fiducial distance travelled between consecutive images were less than 2, 3, 5, and 10 mm for 92.4%, 94.4%, 96.2%, and 97.7% of all consecutive imaging pairs respectively. The percent of treatment time that patients received adequate geometric coverage increased with more frequent imaging intervals. No significant correlations between age, weight, height, Motion during CyberKnife prostate SBRT BMI, rectal, bladder or prostate volumes and intrafraction prostate motion were observed. Conclusions: There are several combinations of imaging intervals and movement thresholds that may be suitable for consideration during treatment planning with respect to imaging and CTV-to-PTV margin calculation, resulting in adequate geometric coverage for approximately 95% of treatment time. Rectal toxicities and treatment duration need to be considered when implementing combinations clinically.

In radiotherapy treatments utilizing accelerator gantry rotation, gantry-mounted kilovoltage (kV) imaging systems have become integral to treatment verification. The accuracy of such verification depends on the stability of the imaging components during gantry rotation. In this study, a simple measurement method and accurate algorithm are introduced for investigation of the kV panel and source movement during gantry rotation. The method is based on images of a ball-bearing phantom combined with a Winston-Lutz phantom, and determines the movements of all the mechanical parameters of the kV imaging system relative to the reference at zero gantry angle. Analysis was performed on different linear accelerators and both gantry rotation directions. The precision of the method was tested and was less than 0.04 mm. This method is suitable to be included in the quality assurance testing of linacs to monitor the kV imaging system performance and provides additional mechanical information that previous tests cannot.