Dose reduction in cardiac SPECT perfusion imaging is of great clinical importance owing to its potential radiation risks. In this study, we investigate the benefit of using full-dose data processed by deep learning (DL) as a learning target for improving the accuracy of reduced-dose studies. We demonstrated this approach in the experiments with a set of 895 clinical cases, in which we employed a pre-trained DL model for obtaining the full-dose target used for training a denoising network on reduced-dose images. The quantitative results show that the proposed approach could further improve the detection accuracy of perfusion defects (using the non-prewhitening matched filter as a numerical observer) on 50% dose studies over that of training directly with full-dose reconstruction. In addition, there was no loss observed in the spatial resolution of the reconstructed left ventricular wall as measured by its full-width at half-maximum.