The statistical nature of SPECT imaging, due to Poisson noise effect, results in degradation of image quality, especially in case of lesions of small signal-to-noise (SNR) ratio (small size, reduced activity). In this paper, the performance of a platelet de-noising method applied, by means of a pre- processing step, on myocardial perfusion SPECT imaging is evaluated. A cardiac phantom, containing two different size cold lesions, was utilized to evaluate the platelet de-noising method performance and compare it with the performance of the Butterworth filtering method, applied on raw data in pre-processing fashion, as well as on reconstructed data, representing the clinical routine. Two experiments were conducted to simulate conditions with and without scatter irradiation from myocardial surrounding tissue. Noise, lesion contrast, SNR and lesion contrast-to-noise ratio (CNR) metrics for both lesions were computed for the three de-noising methods. Results demonstrate sufficient reduction of noise for platelet method yielding increased SNR and lesion CNR values as compared to Butterworth filtering method, applied on pre- and post-processed data, for both lesions. However, no statistically significant differences were demonstrated for all metrics considered (p>0.05). In conclusion, platelet de-noising prior to reconstruction has the potential to provide an efficient means of improving image quality in myocardial perfusion SPECT phantom.