Improvement of quantification of myocardial first-pass perfusion mapping: A temporal and spatial wavelet denoising method

Mapping of myocardial blood flow (MBF) with first-pass perfusion imaging is becoming an important tool in the study of coronary artery disease. In this study a wavelet-based denoising method was developed to improve the accuracy of pixel-by-pixel MBF maps. We performed an in vivo study in five stenotic dogs with 70% stenosis in the left coronary arteries. First-pass perfusion imaging sessions were performed by administering the intravascular contrast agent Gadomer at rest and during dipyridamole-induced vasodilation. Color microspheres (MS) were injected into the dogs to measure MBF at the same time. After denoising was performed, the signal-to-noise ratio (SNR) of the first-pass perfusion image improved by approximately 180%, whereas spatial variation of MBF maps decreased 38%. It was also found that the correlation of MBFs measured by MRI with the MS method indicates a significant improvement with the denoising method (R² increased from 0.24 to 0.78, P < .001). This suggests that the wavelet denoising method may be an effective way to increase the accuracy of pixel-by-pixel MBF quantification and reduce spatial variation, and may be applicable to other forms of noise-sensitive image analysis.