Publications (10)21.76 Total impact
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Article: A new perceptual difference model for diagnostically relevant quantitative image quality evaluation: A preliminary study.
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ABSTRACT: PURPOSE: Most objective image quality metrics average over a wide range of image degradations. However, human clinicians demonstrate bias toward different types of artifacts. Here, we aim to create a perceptual difference model based on Case-PDM that mimics the preference of human observers toward different artifacts. METHOD: We measured artifact disturbance to observers and calibrated the novel perceptual difference model (PDM). To tune the new model, which we call Artifact-PDM, degradations were synthetically added to three healthy brain MR data sets. Four types of artifacts (noise, blur, aliasing or "oil painting" which shows up as flattened, over-smoothened regions) of standard compressed sensing (CS) reconstruction, within a reasonable range of artifact severity, as measured by both PDM and visual inspection, were considered. After the model parameters were tuned by each synthetic image, we used a functional measurement theory pair-comparison experiment to measure the disturbance of each artifact to human observers and determine the weights of each artifact's PDM score. To validate Artifact-PDM, human ratings obtained from a Double Stimulus Continuous Quality Scale experiment were compared to the model for noise, blur, aliasing, oil painting and overall qualities using a large set of CS-reconstructed MR images of varying quality. Finally, we used this new approach to compare CS to GRAPPA, a parallel MRI reconstruction algorithm. RESULTS: We found that, for the same Artifact-PDM score, the human observer found incoherent aliasing to be the most disturbing and noise the least. Artifact-PDM results were highly correlated to human observers in both experiments. Optimized CS reconstruction quality compared favorably to GRAPPA's for the same sampling ratio. CONCLUSIONS: We conclude our novel metric can faithfully represent human observer artifact evaluation and can be useful in evaluating CS and GRAPPA reconstruction algorithms, especially in studying artifact trade-offs.Magnetic Resonance Imaging 12/2012; · 1.99 Impact Factor -
Article: Recovery of chemical estimates by field inhomogeneity neighborhood error detection (REFINED): Fat/Water separation at 7 tesla.
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ABSTRACT: PURPOSE: To reduce swaps in fat-water separation methods, a particular issue on 7 Tesla (T) small animal scanners due to field inhomogeneity, using image postprocessing innovations that detect and correct errors in the B0 field map. MATERIALS AND METHODS: Fat-water decompositions and B0 field maps were computed for images of mice acquired on a 7T Bruker BioSpec scanner, using a computationally efficient method for solving the Markov Random Field formulation of the multi-point Dixon model. The B0 field maps were processed with a novel hole-filling method, based on edge strength between regions, and a novel k-means method, based on field-map intensities, which were iteratively applied to automatically detect and reinitialize error regions in the B0 field maps. Errors were manually assessed in the B0 field maps and chemical parameter maps both before and after error correction. RESULTS: Partial swaps were found in 6% of images when processed with FLAWLESS. After REFINED correction, only 0.7% of images contained partial swaps, resulting in an 88% decrease in error rate. Complete swaps were not problematic. CONCLUSION: Ex post facto error correction is a viable supplement to a priori techniques for producing globally smooth B0 field maps, without partial swaps. With our processing pipeline, it is possible to process image volumes rapidly, robustly, and almost automatically. J. Magn. Reson. Imaging 2012. © 2012 Wiley Periodicals, Inc.Journal of Magnetic Resonance Imaging 09/2012; · 2.70 Impact Factor -
Article: A simple application of compressed sensing to further accelerate partially parallel imaging.
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ABSTRACT: Compressed sensing (CS) and partially parallel imaging (PPI) enable fast magnetic resonance (MR) imaging by reducing the amount of k-space data required for reconstruction. Past attempts to combine these two have been limited by the incoherent sampling requirement of CS since PPI routines typically sample on a regular (coherent) grid. Here, we developed a new method, "CS+GRAPPA," to overcome this limitation. We decomposed sets of equidistant samples into multiple random subsets. Then, we reconstructed each subset using CS and averaged the results to get a final CS k-space reconstruction. We used both a standard CS and an edge- and joint-sparsity-guided CS reconstruction. We tested these intermediate results on both synthetic and real MR phantom data and performed a human observer experiment to determine the effectiveness of decomposition and to optimize the number of subsets. We then used these CS reconstructions to calibrate the generalized autocalibrating partially parallel acquisitions (GRAPPA) complex coil weights. In vivo parallel MR brain and heart data sets were used. An objective image quality evaluation metric, Case-PDM, was used to quantify image quality. Coherent aliasing and noise artifacts were significantly reduced using two decompositions. More decompositions further reduced coherent aliasing and noise artifacts but introduced blurring. However, the blurring was effectively minimized using our new edge- and joint-sparsity-guided CS using two decompositions. Numerical results on parallel data demonstrated that the combined method greatly improved image quality as compared to standard GRAPPA, on average halving Case-PDM scores across a range of sampling rates. The proposed technique allowed the same Case-PDM scores as standard GRAPPA using about half the number of samples. We conclude that the new method augments GRAPPA by combining it with CS, allowing CS to work even when the k-space sampling pattern is equidistant.Magnetic Resonance Imaging 08/2012; · 1.99 Impact Factor -
Article: Body composition analysis of obesity and hepatic steatosis in mice by relaxation compensated fat fraction (RCFF) MRI.
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ABSTRACT: To develop and validate a quantitative magnetic resonance imaging (MRI) methodology for phenotyping animal models of obesity and fatty liver disease on 7T small animal MRI scanners. A new MRI acquisition and image analysis technique, relaxation-compensated fat fraction (RCFF), was developed and validated by both magnetic resonance spectroscopy and histology. This new RCFF technique was then used to assess lipid biodistribution in two groups of mice on either a high-fat (HFD) or low-fat (LFD) diet. RCFF demonstrated excellent correlation in phantom studies (R(2) = 0.99) and in vivo compared to histological evaluation of hepatic triglycerides (R(2) = 0.90). RCFF images provided robust fat fraction maps with consistent adipose tissue values (82% ± 3%). HFD mice exhibited significant increases in peritoneal and subcutaneous adipose tissue volumes in comparison to LFD controls (peritoneal: 6.4 ± 0.4 cm(3) vs. 0.7 ± 0.2, P < 0.001; subcutaneous: 14.7 ± 2.0 cm(3) vs. 1.2 ± 0.3 cm(3) , P < 0.001). Hepatic fat fractions were also significantly different between HFD and LFD mice (3.1% ± 1.7% LFD vs. 27.2% ± 5.4% HFD, P = 0.002). RCFF can be used to quantitatively assess adipose tissue volumes and hepatic fat fractions in rodent models at 7T.Journal of Magnetic Resonance Imaging 11/2011; 35(4):837-43. · 2.70 Impact Factor -
Article: K-space reconstruction with anisotropic kernel support (KARAOKE) for ultrafast partially parallel imaging.
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ABSTRACT: Partially parallel imaging (PPI) greatly accelerates MR imaging by using surface coil arrays and under-sampling k-space. However, the reduction factor (R) in PPI is theoretically constrained by the number of coils (N(C)). A symmetrically shaped kernel is typically used, but this often prevents even the theoretically possible R from being achieved. Here, the authors propose a kernel design method to accelerate PPI faster than R = N(C). K-space data demonstrates an anisotropic pattern that is correlated with the object itself and to the asymmetry of the coil sensitivity profile, which is caused by coil placement and B(1) inhomogeneity. From spatial analysis theory, reconstruction of such pattern is best achieved by a signal-dependent anisotropic shape kernel. As a result, the authors propose the use of asymmetric kernels to improve k-space reconstruction. The authors fit a bivariate Gaussian function to the local signal magnitude of each coil, then threshold this function to extract the kernel elements. A perceptual difference model (Case-PDM) was employed to quantitatively evaluate image quality. A MR phantom experiment showed that k-space anisotropy increased as a function of magnetic field strength. The authors tested a K-spAce Reconstruction with AnisOtropic KErnel support ("KARAOKE") algorithm with both MR phantom and in vivo data sets, and compared the reconstructions to those produced by GRAPPA, a popular PPI reconstruction method. By exploiting k-space anisotropy, KARAOKE was able to better preserve edges, which is particularly useful for cardiac imaging and motion correction, while GRAPPA failed at a high R near or exceeding N(C). KARAOKE performed comparably to GRAPPA at low Rs. As a rule of thumb, KARAOKE reconstruction should always be used for higher quality k-space reconstruction, particularly when PPI data is acquired at high Rs and/or high field strength.Medical Physics 11/2011; 38(11):6138-42. · 2.83 Impact Factor -
Article: Modeling non-stationarity of kernel weights for k-space reconstruction in partially parallel imaging.
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ABSTRACT: In partially parallel imaging, most k-space-based reconstruction algorithms such as GRAPPA adopt a single finite-size kernel to approximate the true relationship between sampled and nonsampled signals. However, the estimation of this kernel based on k-space signals is imperfect, and the authors are investigating methods dealing with local variation of k-space signals. To model nonstationarity of kernel weights, similar to performing a spatially adaptive regularization, the authors fit a set of linear functions using concepts from geographically weighted regression, a methodology used in geophysical analysis. Instead of a reconstruction with a single set of kernel weights, the authors use multiple sets. A missing signal is reconstructed with its kernel weights set determined by k-space clustering. Simulated and acquired MR data with several different image content and acquisition schemes, including MR tagging, were tested. A perceptual difference model (Case-PDM) was used to quantitatively evaluate the quality of over 1000 test images, and to optimize the parameters of our algorithm. A MOdeling Non-stationarity of KErnel wEightS ("MONKEES") reconstruction with two sets of kernel weights gave reconstructions with significantly better image quality than the original GRAPPA in all test images. Using more sets produced improved image quality but with diminishing returns. As a rule of thumb, at least two sets of kernel weights, one from low- and the other from high frequency k-space, should be used. The authors conclude that the MONKEES can significantly and robustly improve the image quality in parallel MR imaging, particularly, cardiac imaging.Medical Physics 08/2011; 38(8):4760-73. · 2.83 Impact Factor -
Article: Fast lipid and water levels by extraction with spatial smoothing (FLAWLESS): three-dimensional volume fat/water separation at 7 Tesla.
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ABSTRACT: To quickly and robustly separate fat/water components of 7T MR images in the presence of field inhomogeneity for the study of metabolic disorders in small animals. Starting with a Markov random field (MRF) based formulation for the 3-point Dixon separation problem, we incorporated new implementation strategies, including stability tracking, multiresolution image pyramid, and improved initial value generation. We term the new method FLAWLESS (Fast Lipid And Water Levels by Extraction with Spatial Smoothing). Compared with non-MRF techniques, FLAWLESS decreased the fat-water swapping mistakes in all of the three-dimensional (3D) animal volumes that we tested. FLAWLESS converged in approximately 1/60th of the computation time of other MRF approaches. The initial value generation of FLAWLESS further improved robustness to field inhomogeneity in 3D volume data. We have developed a novel 3-point Dixon technique found to be useful for high field small animal imaging. It is being used to assess lipid depots and metabolic disorders as a function of genes, diet, age, and therapy.Journal of Magnetic Resonance Imaging 06/2011; 33(6):1464-73. · 2.70 Impact Factor -
Article: A Fast Iterated Conditional Modes Algorithm for Water-Fat Decomposition in MRI.
IEEE Trans. Med. Imaging. 01/2011; 30:1480-1492. -
Article: Improved fat-water reconstruction algorithm with graphics hardware acceleration.
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ABSTRACT: To develop a fast and robust Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares (IDEAL) reconstruction algorithm using graphics processor unit (GPU) computation. The fat-water reconstruction was expedited by vectorizing the fat-water parameter estimation, which was implemented on a graphics card to evaluate potential speed increases due to data-parallelization. In addition, we vectorized and compared Brent's method with golden section search for the optimization of the unknown field inhomogeneity parameter (psi) in the IDEAL equations. The algorithm was made more robust to fat-water ambiguities using a modified planar extrapolation (MPE) of psi algorithm. As compared to simple planar extrapolation (PE), the use of an averaging filter in MPE made the reconstruction more robust to neighborhoods poorly fit by a two-dimensional plane. Fat-water reconstruction time was reduced by up to a factor of 11.6 on a GPU as compared to CPU-only reconstruction. The MPE algorithms incorrectly assigned fewer pixels than PE using careful manual correction as a gold standard (0.7% versus 4.5%; P < 10(-4)). Brent's method used fewer iterations than golden section search in the vast majority of pixels (6.8 +/- 1.5 versus 9.6 +/- 1.6 iterations). Data sets acquired on a high field scanner can be quickly and robustly reconstructed using our algorithm. A GPU implementation results in significant time savings, which will become increasingly important with the trend toward high resolution mouse and human imaging.Journal of Magnetic Resonance Imaging 02/2010; 31(2):457-65. · 2.70 Impact Factor -
Article: Involuntary, electrically excitable nerve transfer for denervation: results from an animal model.
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ABSTRACT: The purpose of this study was to evaluate the efficacy of "paralyzed" nerve transfer (ie, transfer of an involuntary, nondegenerated, electrically excitable nerve onto an involuntary, degenerated, non-electrically excitable nerve) and functional electrical stimulation for reinnervation. We hypothesized that lower motor neuron cell body continuity with the motor cortex, via intact upper motor neurons, is not necessary for reinnervation of the extremities. Fischer 344 rats had lower thoracic spinal cord injury (SCI) followed by unilateral tibial nerve transection and delayed peroneal ("paralyzed") to tibial nerve transfer (group A) or primary neurorrhaphy (group B). Control groups had SCI and a unilateral hindlimb incision and nerve exposure only (group C) or a unilateral hindlimb disection and transection of both the tibial and peroneal nerves (group D). Three months after surgery, the proximal peroneal (group A) or proximal tibial (groups B, C, and D) nerves were electrically stimulated in vivo, and gastrocnemius force production was measured on both the operative and nonoperative hindlimbs. In addition, the distal tibial nerves from both the experimental and control-side hindlimbs were sectioned and stained with anti-neurofilament protein to determine total axon counts. Mean gastrocnemius force return and mean axonal regeneration was 47% and 51%, respectively, for group A animals (n = 9), 68% and 73% for group B animals (n = 4), 97% and 99% for group C animals (n = 4), and 0 and 2% for group D animals (n = 4). A 1-way analysis of variance for independent samples yielded significant differences between groups A, B, and C for gastrocnemius force return and between all groups for axonal regeneration. Paralyzed nerve transfer produces a mean of approximately 50% return of gastrocnemius force and axonal regeneration. Paralyzed nerve transfer combined with functional electrical stimulation is a viable method for reanimating denervated motor units in the setting of SCI.The Journal of hand surgery 04/2009; 34(3):479-487, 487.e1-3. · 1.33 Impact Factor
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2010–2012
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Case Western Reserve University
- Department of Biomedical Engineering
Cleveland, OH, USA
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