# Dose Estimation by Geant4-Based Simulations for Cone-Beam CT Applications: A Systematic Review
## 3 Results and Discussion
Proton therapy is a promising high resolution image reconstruction modality applied to CBCT that maintain the improved stopping power estimation of proton CT. [PERSON] et al. [115] propose a method to overcome the problem of limited angles using a dual modality reconstruction combining the proton data with a CBCT. However, it does not fully achieve the quality of a 360\({}^{\circ}\) proton CT scan. In 2018, [PERSON] et al. accurately characterized a proton gantry-mounted CBCT system. To examine the influence on organ doses from different acquisition modes and repeated imaging, they used two MC codes: MCNP6 and GATE. They found that organ doses significantly fluctuated depending on the acquisition mode, recommending posterior scans [97; 98]. [PERSON] also proved that the CBCT imaging doses simulated by GATE depend greatly on the imaging isocenter location, patient sex, and partial rotation angles [108]. A method to estimate dose accuracy in volumetric modulated arc therapy treatment plans by MONACO, an independent Geant4-based dose calculation algorithm, is presented in Reference [119]. [PERSON] et al. propose new dose point measurement-based method to characterize the dose distributions and the mean dose from a single partial rotation of a CBCT system over a stationary, large, body-shaped phantom validated by a Geant4 simulation [112]. From 2018 to 2020, some publications have been dedicated to developing new software tools for rapidly and accurately estimating scatter in x-ray projection images by solving the deterministic linear Boltzmann transport equation (LBTE) [92; 96; 100]. These methods use Linear Discontinuous Finite Elements, Multigroup, and Discrete Ordinates methods to maintain the accuracy of a continuous solution. To solve the high computational complexity and achieve the speeds required for clinical utilization, implementation in CUDA allowed to exploit the parallel computing capabilities of GPUs. [PERSON] et al. propose a novel strategy to accelerate simulations by also using a Geant4-based GPU code for photon transport for the simulation of an MV-CBCT [93]. RQ3: How Advances in Geant4-Based CBCT Dose Estimator May Help to Evaluate the Risk of Developing Stochastic Effects? Estimating potential risks of developing stochastic from a CBCT scanning protocol requires analysis of both the radiation dose to specific organs and the risk of developing health effects as a result of this dose. To this aim, we study the quantities of interest that provided organ doses [124] reported in our bibliographic dataset for the most common applications described in the previous section. Radiological doses from CBCT are closely related to the field of view, the imaging detector, and voxel sizes used for scanning and can be borderline between low (<1 mGy) and high radiological doses (5-100) mGy [125]. The potential health effects from radiation do not just depend on the absorbed dose but also on how sensitive a certain organ is to radiation [37]. The use of daily standard mode CBCT can result in up to 1.5-2 Gy to some critical organs and an effective dose of 600-800 mSv to the body, which may induce an additional secondary cancer risk of 3-4% [126]. As an example, a daily standard mode CBCT for a prostate of 35 fractions protocol may deliver an additional effective dose of 800 mSv to the patient, which could induce an additional secondary cancer risk of 4%. If only weekly standard mode CBCT were done, the additional risk of secondary cancer would be 0.8%. A chest treatment of 25 to 30 fractions with daily standard mode CBCT can result in an additional effective dose of 600-700 mSv, which could induce an additional secondary cancer risk of 3-3.5% [126]. High-precision radiotherapy is crucial when the surrounding tissues are highly radiosensitive [127]. IGRT can reduce the treatment margin and reduce normal tissue exposure, but it also adds a dose to patients, and its contribution has to be assessed and minimized [126].