Respiratory and cardiac motions can degrade myocardial perfusion SPECT (MPS) image quality and reduce defect detection and quantitative accuracy. In this study, we developed a dual respiratory and cardiac gating system for a high-resolution fully stationary cardiac SPECT scanner in order to improve the image quality and defect detection. Respiratory motion was monitored using a compressive sensor pillow connected to a dual respiratory-cardiac gating box, which sends cardiac triggers only during end-expiration phases to the single cardiac trigger input on the SPECT scanners. The listmode data were rebinned retrospectively into end-expiration frames for respiratory motion reduction or eight cardiac gates only during end-expiration phases to compensate for both respiratory and cardiac motions. The proposed method was first validated on a motion phantom in the presence and absence of multiple perfusion defects, and then applied on 11 patient studies with and without perfusion defects. In the normal phantom studies, the end-expiration gated SPECT (EXG-SPECT) reduced respiratory motion blur and increased myocardium to blood pool contrast by 51.2% as compared to the ungated images. The proposed method also yielded an average of 11.2% increase in myocardium to defect contrast as compared to the ungated images in the phantom studies with perfusion defects. In the patient studies, EXG-SPECT significantly improved the myocardium to blood pool contrast (p < 0.005) by 24% on average as compared to the ungated images, and led to improved perfusion uniformity across segments on polar maps for normal patients. For a patient with defect, EXG-SPECT improved the defect contrast and definition. The dual respiratory-cardiac gating further reduced the blurring effect, increased the myocardium to blood pool contrast significantly by 36% (p < 0.05) compared to EXG-SPECT, and further improved defect characteristics and visualization of fine structures at the expense of increased noise on the patient with defect. The results showed that the proposed methods can effectively reduce motion blur in the images caused by both respiratory and cardiac motions, which may lead to more accurate defect detection and quantifications. This approach can be easily adapted in routine clinical practice on currently available commercial systems.