## 1 Introduction  
The recent technological advancements in radiotherapy (RT) and the introduction of Artificial Intelligence (AI) in this context have made it possible to offer even more personalised treatments to patients, effectively compensating for the anatomical variations observed during each RT treatment fraction [1; 2; 3].  
In recent years, different studies have proved that quickly adapting the RT treatment plan to the patient's daily anatomy can lead to remarkable clinical benefits in terms of quality of life and disease control [4; 5].  
AI plays a key role in the online adaptive procedure by supporting clinicians and medical physicists in automating different manual procedures, such as contouring and planning, making the whole pipeline faster and less user-dependent. In particular, the AI implementation can significantly reduce treatment slot times, moving from the current 30 to 50 min to about 15 min, making possible the diffusion of this procedure to a larger community of patients [6].  
Another important step forward obtainable thanks to AI is represented by the removal of the Computed Tomography (CT) simulation procedure. The core idea is to generate a synthetic Computed Tomography (sCT) image starting from the daily imaging on-board acquired for patient positioning and using this image as an electron density map for dose calculation [7; 8].  
The adoption of synthetic CT in clinical practice would reduce uncertainties due to the co-registration between simulation CT and daily positioning images, avoid simulation dose exposure to the patient, and speed up RT treatment workflow, opening up single-step procedures that can have a huge impact in the case of palliative treatments [9].  
Various non-AI-based strategies have been proposed so far to remove CT simulation; these strategies include the use of pre-generated atlases of known cases, the identification of pre-defined tissue density levels, and the consequent assignment of bulk electron density (ED) values [10; 11; 12]. Another approach was the use of deformable image registration, which today represents the most diffused strategy in clinical practice. However, several comparison studies have shown that all these approaches yield suboptimal results when compared to AI-based strategies in terms of dose accuracy and generation time [7; 13].  
Today, the most diffused AI approaches are based on Deep Learning (DL) architectures, such as Generative Adversarial Neural (GAN) networks and U-shaped convolutional neural networks (U-Net), which have proved the feasibility of generating high-quality sCT images from different positioning images, such as Cone Beam Computed Tomography (CBCT) and Magnetic Resonance Imaging (MRI) [14; 15; 16; 17].  
Providing ED information from CBCT images was a topic of research for several years, but only with the use of DL approaches was it possible to obtain levels of dose accuracy reliable for clinical use [18; 19].  
Recently, the first RT system able to integrate AI technology to perform online adaptive RT treatments has been released for clinical use (Varian Ethos, Varian Medical System, US), equipped with a low-dose CBCT on-board system, able to ensure high image quality using low-dose protocols through a dedicated iterative reconstruction algorithm [4].  
Thanks to the AI integration, this system is able to speed up the online adaptive workflow, offering clinicians auto-contouring and auto-planning solutions that allow for treatment standardisation and reduce workload during on-table adaptive procedures.  
The aim of this study is to propose a DL algorithm able to generate high-quality sCT from CBCT images in a reliable and fast way, making it possible to remove the CT simulation step from the RT workflow and collapsing the whole RT back-off workflow online, offering reliable solutions to patients requiring fast treatments, such as those needing palliative care.