Purpose: To develop and optimize a new modification of GRAPPA (generalized autocalibrating partially parallel acquisitions) MR reconstruction algorithm named "Robust GRAPPA." Materials and Methods:In Robust GRAPPA, k-space data points were weighted before the reconstruction. Small or zero weights were assigned to "outliers" in k-space. We implemented a Slow Robust GRAPPA method, which iteratively reweighted the k-space data. It was compared to an ad hoc Fast Robust GRAPPA method, which eliminated (assigned zero weights to) a fixed percentage of k-space "outliers" following an initial estimation procedure. In comprehensive experiments the new algorithms were evaluated using the perceptual difference model (PDM), whereby image quality was quantitatively compared to the reference image. Independent variables included algorithm type, total reduction factor, outlier ratio, center filling options, and noise across multiple image datasets, providing 10,800 test images for evaluation. Results:The Fast Robust GRAPPA method gave results very similar to Slow Robust GRAPPA, and showed significant improvements as compared to regular GRAPPA. Fast Robust GRAPPA added little computation time compared with regular GRAPPA. Conclusion:Robust GRAPPA was proposed and proved useful for improving the reconstructed image quality. PDM was helpful in designing and optimizing the MR reconstruction algorithms.
The authors are using a perceptual difference model (Case-PDM) to quantitatively evaluate image quality of the thousands of test images which can be created when optimizing fast magnetic resonance (MR) imaging strategies and reconstruction techniques. In this validation study, they compared human evaluation of MR images from multiple organs and from multiple image reconstruction algorithms to Case-PDM and similar models. The authors found that Case-PDM compared very favorably to human observers in double-stimulus continuous-quality scale and functional measurement theory studies over a large range of image quality. The Case-PDM threshold for nonperceptible differences in a 2-alternative forced choice study varied with the type of image under study, but was approximately 1.1 for diffuse image effects, providing a rule of thumb. Ordering the image quality evaluation models, we found in overall Case-PDM approximately IDM (Sarnoff Corporation) approximately SSIM [Wang et al. IEEE Trans. Image Process. 13, 600-612 (2004)] > mean squared error NR [Wang et al. (2004) (unpublished)] > DCTune (NASA) > IQM (MITRE Corporation). The authors conclude that Case-PDM is very useful in MR image evaluation but that one should probably restrict studies to similar images and similar processing, normally not a limitation in image reconstruction studies.
Diffusion‐weighted imaging (DWI) has shown great benefits in clinical MR exams. However, current DWI techniques have shortcomings of sensitivity to distortion or long scan times or combinations of the two. Diffusion‐weighted echo‐planar imaging (EPI) is fast but suffers from severe geometric distortion. Periodically rotated overlapping parallel lines with enhanced reconstruction diffusion‐weighted imaging (PROPELLER DWI) is free of geometric distortion, but the scan time is usually long and imposes high Specific Absorption Rate (SAR) especially at high fields. TurboPROP was proposed to accelerate the scan by combining signal from gradient echoes, but the off‐resonance artifacts from gradient echoes can still degrade the image quality. In this study, a new method called X‐PROP is presented. Similar to TurboPROP, it uses gradient echoes to reduce the scan time. By separating the gradient and spin echoes into individual blades and removing the off‐resonance phase, the off‐resonance artifacts in X‐PROP are minimized. Special reconstruction processes are applied on these blades to correct for the motion artifacts. In vivo results show its advantages over EPI, PROPELLER DWI, and TurboPROP techniques. Magn Reson Med, 2011. © 2011 Wiley‐Liss, Inc.
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