One of the key areas that deep learning has made a significant impact is in the field of tomographic image reconstruction . Traditionally, tomographic image reconstruction has relied on either direct methods, like the filtered back projection (FBP) algorithm , or iterative methods that depend on prior knowledge and fine-tuning. However, these methods face their own limitations, especially when it comes to scalability, handling noise and angular undersampling data, computational demand, and the necessity for absolute values in certain applications . Deep neural networks (DNNs) have emerged as a compelling solution, offering the potential to surpass the performance of these traditional physics-based approaches .