There is an extraordinary number of fast MR imaging techniques, especially for parallel imaging. When one considers
multiple reconstruction algorithms, reconstruction parameters, coil configurations, acceleration factors, noise levels, and
multiple test images, one can easily create 1000's of test images for image quality evaluation. We have found the
perceptual difference model (Case-PDM) to be quite useful as a means of rapid quantitative image quality evaluation in
such experiments, and have applied it to keyhole, spiral, SENSE, and GRAPPA applications. In this study, we have
compared human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms to
Case-PDM. We compared human DSCQS (Double Stimulus Continuous Quality Scale) scoring against Case-PDM
measurements for 3 different image types and 3 different image reconstruction algorithms. We found that Case-PDM
linearly correlated (r > 0.9) with human subject ratings over a very large range of image quality. We also compared
Case-PDM to other image quality evaluation methods. Case-PDM generally performed better than NASA's DCTune,
MITRE's IQM, Zhou Wang's NR models and mean square error (MSE) method, by showing a higher Pearson
correlation coefficient, higher Spearman rank-order correlation and lower root-mean-squared error. All three models
(Case-PDM, Sarnoff's IDM, and Zhou Wang's SSIM) performed very similarly in this experiment. To focus on high
quality reconstructions, we performed a 2-AFC (Alternate Forced Choice) experiment to determine the "just perceptible
difference" between two images. We found that threshold Case-PDM scores changed little (0.6-1.8) with 2 different
image types and 3 degradation patterns, and results with Case-PDM were much tighter than the other methods (IDM and
MSE) by showing a lower ratio of mean to standard deviation value. We conclude that Case-PDM can correctly predict
the ordering of image quality over a large range of image quality. Case-PDM can also be used to screen the images
which are "perceptually equal" to the original image. Although Case-PDM is a very useful tool for comparing "similar
raw images with similar processing," one should be careful when interpreting Case-PDM scores across MR images.
Parallel imaging using multiple coils and sub-sampled k-space data is a promising fast MR image acquisition technique. We used detection studies and perceptual difference models on image data with ¼ sampling to evaluate three different reconstruction methods: a regularization method developed by Ying and Liang of UIUC, a simplified regularization method, and an iterative method. We also included images obtained from a full complement of k-space data as "gold standard" images. Detection studies were performed using a simulated dark tumor added on MR images of bovine liver. We found that human detection depended strongly on the reconstruction methods used, with the simplified regularization and UIUC methods achieving better performance than the iterative method. We also evaluated images using detection with a Channlized Hotelling Observer (CHO) model and with a Perceptual Difference Model (PDM). Both predicted the same trends as observed in the human detection studies. We are encouraged that PDM gives trends similar to that for detection studies. Its ease of use and applicability to a variety of MR imaging situations make it attractive for evaluating image quality in a variety of MR studies.
Proc. SPIE. 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment
KEYWORDS: Signal to noise ratio, Visual process modeling, Visualization, Calibration, Magnetic resonance imaging, Image analysis, Data acquisition, Image quality, Reconstruction algorithms, Image quality standards
GRAPPA is a popular reconstruction technique in parallel imaging. In GRAPPA, a least-squares technique is used to solve the over-determined equations and get the "fitting" coefficients for the reconstruction. We developed the Robust GRAPPA method whereby robust estimation techniques are used to estimate the coefficients with discounting of k-space data outliers. One implementation, Slow Robust GRAPPA used iteratively re-weighted techniques, and it was compared to an ad hoc Fast Robust GRAPPA implementation. We evaluated these new algorithms using the Perceptual Difference Model (PDM). PDM has already been successfully applied to a variety of MR applications. We systematically investigated independent variables including algorithm, outer reduction factor, total reduction factor, outlier ratio, and noise across multiple image datasets, giving 9000 images. We conclude that Fast Robust GRAPPA method gives results very similar to Slow Robust GRAPPA and that both give significant improvements as compared to standard GRAPPA. PDM is very helpful in designing and optimizing the MR reconstruction algorithms.
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.
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.
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.