X-ray fluoroscopy is a medical imaging modality that provides continuous real-time screening of patient’s organs and various radiopaque surgical objects. Fluoroscopy usually requires long and unpredictable exposure times, thus radiation intensity must be heavily reduced to limit patient’s dose. This gives rise to the well-known Poisson noise, which results in very poor image quality. Commercial fluoroscopes usually improve image quality via real-time temporal averaging, which produces motion blur in moving scenes. The Noise Variance Conditioned Average (NVCA) algorithm exploits the a priori knowledge of Poisson noise statistics to provide efficient noise reduction, while preserving the edges of moving objects. However, accurate setting of NVCA parameters is required to achieve the best results, and this could be supported by image quality assessment (IQA) indices. This study presents a novel, edge-aware IQA index, named Sensitivity of Edge Detection (SED), and compares it against the well-established Feature Similarity (FSIM) index, to assess their efficiency in determining the optimal parameters for NVCA. The preliminary results obtained in this study suggest SED could be more efficient than FSIM in identifying the best trade-off between noise reduction and edge preservation, and could be also used to determine the optimal parameters of other denoising algorithms.