Quantitative image quality evaluation of spiral MRI techniques under noisy conditions

Abstract

Spiral sampling of k-space is a popular technique in fast MRI. Many methods are available for spiral acquisition and reconstruction. We used a Perceptual Difference Model (PDM) to evaluate these selections and to examine the effects of noise. PDM is a human observer model that calculates the visual difference between a “test image” and a “gold standard image.” PDM has been shown to correlate well with human observers in a variety of MR experiments including added noise, increased blurring, keyhole imaging, and spiral imaging. We simulated MR images from six different interleave patterns, seven different sampling levels, three different density compensation methods, and four different reconstruction options under zero noise and three noise levels. By comparing results with and without noise, we can separate noise effects from reconstruction errors. 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, because high frequency sampling was ensured. We conclude that PDM scoring provides an objective, useful tool for the assessment of spiral MR image quality and can greatly aid the design of MR acquisition and signal processing strategies.