The synthesis of computed tomography images can supplement electron density information and eliminate MR-CT image registration errors. Consequently, an increasing number of MR-to-CT image translation approaches are being proposed for MR-only radiotherapy planning. However, due to substantial anatomical differences between various regions, traditional approaches often require each model to undergo independent development and use. In this paper, we propose a unified model driven by prompts that dynamically adapt to the different anatomical regions and generates CT images with high structural consistency. Specifically, it utilizes a region-specific attention mechanism, including a region-aware vector and a dynamic gating factor, to achieve MRI-to-CT image translation for multiple anatomical regions. Qualitative and quantitative results on three datasets of anatomical parts demonstrate that our models generate clearer and more anatomically detailed CT images than other state-of-the-art translation models. The results of the dosimetric analysis also indicate that our proposed model generates images with dose distributions more closely aligned to those of the real CT images. Thus, the proposed model demonstrates promising potential for enabling MR-only radiotherapy across multiple anatomical regions. we have released the source code for our RSAM model. The repository is accessible to the public at: https://github.com/yhyumi123/RSAM.

As the Artificial Intelligence (AI) industry rapidly expands in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China, the demand for AI talent continues to grow, higher educational institutions need to continuously update its AI education to adapt technological progress. However, there is currently a lack of understanding on AI education in the GBA. This work-in-progress research paper analyzes the current state of AI bachelor degrees within the GBA and compares it with the well-established curricula in the United States (US). The study selected a sample from the top 8 universities based on QS rankings and employed an enhanced AI course literacy categories model for the inductive and comparative analysis of AI course settings. The preliminary study highlights differences between universities in the GBA and the US in terms of course content depth and teaching strategies. The GBA universities emphasize a balanced and comprehensive curriculum that ensures all students have a solid foundation in both AI theories and technologies. In contrast, US universities prioritize a broader, interdisciplinary approach that fosters innovation and development of practical skills. The findings suggest that GBA universities could benefit from adopting some of the strategies used by US universities, such as adjusting the proportion of elective courses and increasing the availability of interdisciplinary courses. This would promote personalized student development and interdisciplinary learning, better preparing students to meet the challenges and opportunities of the AI era.