This study develops a mathematical framework for determining the best possible reconstruction accuracy in inverse problems, including applications in medical imaging, radar, and astronomy. It establishes universal error bounds for different reconstruction methods, including deep learning, and identifies solutions that achieve the lowest possible reconstruction error.