Donglai Huo

Perceptual difference paradigm for analyzing image quality of fast MRI techniques

We are developing a method to objectively quantify image quality and applying it to the optimization of fast magnetic resonance imaging methods. In MRI, to capture the details of a dynamic process, it is critical to have both high temporal and spatial resolution. However, there is typically a trade-off between the two, making the sequence engineer choose to optimize imaging speed or spatial resolution. In response to this problem, a number of different fast MRI techniques have been proposed. To evaluate different fast MRI techniques quantitatively, we use a perceptual difference model (PDM) that incorporates various components of the human visual system. The PDM was validated using subjective image quality ratings by naive observers and task-based measures as defined by radiologists. Using the PDM, we investigated the effects of various imaging parameters on image quality and quantified the degradation due to novel imaging techniques including keyhole, keyhole Dixon fat suppression, and spiral imaging. Results have provided significant information about imaging time versus quality tradeoffs aiding the MR sequence engineer. The PDM has been shown to be an objective tool for measuring image quality and can be used to determine the optimal methodology for various imaging applications.

Optimization of Spiral MRI Using a Perceptual Difference Model

We systematically evaluated a variety of MR spiral imaging acquisition and reconstruction schemes using a computational perceptual difference model (PDM) that models the ability of humans to perceive a visual difference between a degraded “fast” MRI image with subsampling of k-space and a “gold standard” image mimicking full acquisition. Human subject experiments performed using a modified double-stimulus continuous-quality scale (DSCQS) correlated well with PDM, over a variety of images. In a smaller set of conditions, PDM scores agreed very well with human detectability measurements of image quality. Having validated the technique, PDM was used to systematically evaluate 2016 spiral image conditions (six interleave patterns, seven sampling densities, three density compensation schemes, four reconstruction methods, and four noise levels). Voronoi (VOR) with conventional regridding gave the best reconstructions. At a fixed sampling density, more interleaves gave better results. With noise present more interleaves and samples were desirable. With PDM, conditions were determined where equivalent image quality was obtained with 50% sampling in noise-free conditions. We conclude that PDM scoring provides an objective, useful tool for the assessment of fast MR image quality that can greatly aid the design of MR acquisition and signal processing strategies.

Using the Perceptual Difference Model (PDM) to Optimize GRAPPA Reconstruction

Parallel imaging techniques are being applied in MRI to improve the spatial or temporal resolution. Generalized autocalibrating partially parallel acquisitions (GRAPPA) is one of the most popular reconstruction techniques in parallel imaging. In GRAPPA, several k-space lines are acquired in addition to the normal subsampled data acquisition. Coil mapping information is extracted from these lines and used to reconstruct the missing k-space lines. These additionally acquired k-space lines can also be used in the final reconstruction so as to improve the image quality. In GRAPPA, carefully selecting the calibration region and sampling schemes can greatly reduce the noise and reconstruction artifact and improve the image quality. Perceptual difference model (PDM) is a quantitative image quality evaluation tool which has been successfully applied to varieties of MR applications. High correlation between human rating and PDM scores in previous studies shows that PDM is suitable for evaluating image quality in parallel MR imaging. We used PDM to quantitatively compare the quality of images reconstructed with different calibration regions and sampling schemes. We conclude that when the location of the calibration region is set at 0.8 of the phase encoding direction, and the width is set as 20% of total available fitting length, the best reconstruction image could be achieved. One should also set the outer region factor as small as possible. As an example, with all these optimizations, the time used to achieve the same image quality would be reduced by 16% as compared to unoptimized GRAPPA

Application of perceptual difference model (PDM) on regularization techniques of parallel MR imaging

Parallel magnetic resonance imaging through sensitivity encoding using multiple receiver coils has emerged as an effective tool to reduce imaging time or improve the image quality. Reconstructed image quality is limited by the noise in the acquired k-space data, inaccurate estimation of the sensitivity map, and the ill-conditioned nature of the coefficient matrix. Tikhonov Regularization is currently the most popular method to solve the ill-condition problem. Selections of the regularization map and the regularization parameter are very important. The Perceptual Difference Model (PDM) is a quantitative image quality evaluation tool which has been successfully applied to varieties of MR applications. High correlation between the human rating and the PDM score shows that PDM could be suitable for evaluating image quality in parallel MR imaging. By applying PDM, we compared four methods of selecting the regularization map and four methods of selecting regularization parameter. We find that generalized series (GS) method to select the regularization map together with spatially adaptive method to select the regularization parameter gives the best solution to reconstruct the image. PDM also work as a quantitative image quality index to optimize two important free parameters in spatially adaptive method. We conclude that PDM is an effective tool in helping design and optimize reconstruction methods in parallel MR imaging.

Quantitative image quality evaluation of spiral MRI techniques under noisy conditions

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.