Abstract: Sparse-view tomographic reconstruction is of significant importance for reducing radiation dose in clinical practice. In recent years, Implicit Neural Representation (INR) methods have been widely applied to medical image reconstruction in sparse-view scenario and have achieved competitive performance. However, traditional INR methods treat each sampling point individually as input, which neglect the inherent relations among neighboring sampling points, thus weakening the reconstruction performance. To address this, this paper proposes a novel INR method. The proposed method reorganizes neighboring sampling points on adjacent rays into multiple windows-of-interest, which are then fed into a Transformer query network equipped with a skip connection. By leveraging the self-attention mechanism of the Transformer network, the proposed method is able to capture the intrinsic relations among sampling points within each window-of-interest, thereby effectively enhancing the reconstructed image quality. This paper conducts extensive numerical experiments in two tomographic imaging modalities: Cone-Beam Computed Tomography (CBCT) and parallel-beam Single-Photon Emission Computed Tomography (SPECT) . The experimental results show that, compared to the advanced INR method Freq-NAF, the proposed method achieves superior performance in terms of reconstruction accuracy and image visualization under sparse-view conditions, particularly obtaining a 0.45 dB improvement in Peak Signal-to-Noise Ratio (PSNR) on the chest CBCT dataset.