In CT/PET imaging applications, reconstructing images from low-dose/low-count acquisitions often leads to lower image quality, necessitating specialized denoising methods and reconstruction algorithms to enhance diagnostic accuracy. While many recent denoising techniques employ convolutional neural networks (CNNs), these architectures may struggle with capturing long-range, nonlocal interactions, potentially resulting in inaccuracies in global structure representation. Recognizing the advantages of transformer architectures over CNNs on that front, our study introduces a novel sinogram denoising algorithm tailored at improving low-dose/low-count sinogram quality. We propose a transformer-based sinogram denoiser module specifically designed to match the structure of sinogram data, enhancing sinogram feature extraction and denoising performance. Furthermore, by incorporating image domain denoising, we propose cross-domain image reconstruction, allowing for further image quality refinement by addressing image-specific noise characteristics. Our cross-domain image reconstruction network, which incorporates the proposed sinogram denoiser module, has been trained with both synthetic and clinical data. Performance evaluations reveal that our sinogram sinusoidal-structure transformer Denoiser achieves outstanding results in sinogram denoising, while our cross-domain image reconstruction network demonstrates excellent image reconstruction capabilities, as validated by both subjective and objective metrics.