A static, multi-source x-ray Computed Tomography (CT) system facilitates rapid multi-view x-ray radiography, significantly improving the efficiency of cargo scanning. However, reconstructing images from sparse-view x-ray data in cargo scanning is challenging, particularly when conventional deep learning reconstruction techniques are hampered by a scarcity of training data. This work proposes the application of Deep Image Prior (DIP), which does not require training data, to reduce undersampling reconstruction artifacts arising from sparse-view and restricted opening angle acquisition in x-ray CT systems tailored for large-scale cargo scanning in harbors. The work particularly targets a rectangular multi-source x-ray CT system, featuring up to 40 equidistantly distributed static x-ray sources with a 30-degree opening angle. Our study demonstrates that DIP improves the quality of of sparse-view cargo CT in terms of PSNR and SSIM compared to traditional reconstruction methods.