Motivation: In radiation therapy with PET images, CT and MR images are used for precise targeting, but acquiring them is expensive, time-consuming and increases radiation risk. Goal(s): Developing a deep learning model capable of dynamically switching to a specified mode enhances flexibility beyond traditional one-to-one cross-modal conversion methods. Approach: We developed a deep learning model with dynamic modality translation capabilities by the incorporation of switch layers within the decoder module. Results: The evaluations showed that our model excels at converting non-attenuation corrected PET images to attenuation corrected PET, MR, or CT images, making it easier to obtain additional modality images for radiation therapy. Impact: Dynamic conversion from NAC PET to desired modalities like AC PET, CT, or MRI on demand is more efficient, saving on data storage and processing, and offers customized imaging for specific clinical needs, enhancing workflow efficiency.