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Background and purpose:: In radiotherapy, precise comparison of fan-beam computed tomography (CT) and cone-beam CT (CBCT) arises as a commonplace, yet intricate task. This paper proposes a publicly available end-to-end pipeline featuring an intrinsic deep-learning-based speedup technique for generating virtual 3D and 4D CBCT from CT images. Materials and methods:: Physical properties, derived from CT intensity information, are obtained through automated whole-body segmentation of organs and tissues. Subsequently, Monte Carlo (MC) simulations generate CBCT X-ray projections for a full circular arc around the patient employing acquisition settings matched with a clinical CBCT scanner (modeled according to Varian TrueBeam specifications). In addition to 3D CBCT reconstruction, a 4D CBCT can be simulated with a fully time-resolved MC simulation by incorporating respiratory correspondence modeling. To address the computational complexity of MC simulations, a deep-learning-based speedup technique is developed and integrated that uses projection data simulated with a reduced number of photon histories to predict a projection that matches the image characteristics and signal-to-noise ratio of the reference simulation. Results:: MC simulations with default parameter setting yield CBCT images with high agreement to ground truth data acquired by a clinical CBCT scanner. Furthermore, the proposed speedup technique achieves up to 20-fold speedup while preserving image features and resolution compared to the reference simulation. Conclusion:: The presented MC pipeline and speedup approach provide an openly accessible end-to-end framework for researchers and clinicians to investigate limitations of image-guided radiation therapy workflows built on both (4D) CT and CBCT images.