Evaluation of noise effects in spiral MRI image reconstruction using perceptual difference model (PDM)

Abstract

Spiral sampling of k-space is a popular magnetic resonance imaging (MRI) technique. A variety of choices are available for optimizing the spiral trajectory and reconstruction methods. To evaluate the effects of noise on these choices, we used a Perceptual Difference Model (PDM), which evaluates the quality by calculating the visual difference between a test image and a gold standard image. We simulated images from six different interleave patterns, seven different sampling levels, four different density compensation methods, and three different reconstruction options under three noise levels. Noise effects were separated from reconstruction errors by comparing results to those from a noise-free spiral acquisition. Comparing many different conditions, Voronoi (VOR) plus conventional regridding was good for high SNR data. In low SNR conditions, Area Density Function (ADF) was better. One can also quantitatively compare different acquisition parameters; smaller numbers of interleaves and high number of samples were very desirable when noise was applied. We conclude that PDM scoring provides an objective, useful tool for the assessment of spiral MR quality and can greatly aid the design of MR acquisition and signal processing strategies.