Biomedical subjects

Gary X Shen

Publications and source records attributed to Gary X Shen.

7 recordsLinked to original sources

PubMed2006

Comparing diffusion-weighted and T2-weighted MR imaging for the quantification of infarct size in a neonatal rat hypoxic-ischemic model at 24h post-injury.

PURPOSE: In a neonatal rat model of hypoxic-ischemic (HI) brain injury, using T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), we aim to determine the best MRI method of lesion quantification that reflects infarct size. MATERIALS AND METHODS: Twenty 7-day-old rats underwent MRI 24h after HI brain injury was induced. Lesion size relative to whole brain was measured using T2WI and apparent diffusion coefficient (ADC) maps, applying thresholds of 60%, 70% and 80% contralateral control hemisphere mean ADC, and at day 10 post-HI on pathology with TTC staining. Multiple linear regression analysis was used to study the relationships between lesion size at MRI and pathology. RESULTS: Lesion size measurement using all MRI methods significantly correlated with infarct size at pathology; using T2WI, r=0.808 (p<0.001), using 80% ADC, 70% ADC and 60% ADC thresholds, r=0.888 (p<0.001), 0.761, (p<0.001) and 0.569 (p=0.014), respectively. Eighty percent ADC threshold was found to be the only significant independent predictor of final infarct volume (adjusted R(2)=0.775). CONCLUSION: At 24h post-HI, lesion size on DWI, using 80% ADC threshold is the best predictor of final infarct volume. Although T2WI performed less well, it has the advantage of superior spatial resolution and is technically less demanding. These are important considerations for experiments which utilize MRI as a surrogate method for lesion quantification in the neonatal rat HI model.

Animals↗

PubMed2006

Potential advantage of higher-order modes of birdcage coil for parallel imaging.

The potential advantages of the higher-order resonant modes of a low-pass birdcage coil for parallel imaging are investigated using FDTD simulations in a spherical phantom. Better parallel imaging performance can be achieved in axial planes than in sagittal planes. If more modes are employed, the average g-factor (gmean) and maximum g-factor (gmax) will be improved for a specific acceleration factor (R). G-factor performance at 3 T (gmean=1.79, gmax=3.55) and 7 T (gmean=1.60, gmax=2.45) can be achieved even with an acceleration factor as high as four when all six order modes of 12-rung low-pass birdcage coil are incorporated for imaging on a mid-axial plane. For a specific number of channels, the optimum combination of corresponding modes can be obtained for different acceleration factors. Based on the g-factor and SNR performance, the total degenerate multi-mode birdcage coil with six order resonant modes has better homogeneous coverage and SENSE performance than the 8-element phased array coil, although requiring fewer channels. In addition, the dielectric effects at high field can improve the parallel imaging performance.

Journal Article↗

PubMed2006

Design of an inductively decoupled microstrip array at 9.4 T.

By independent control of the phases and amplitudes of its elements, the microstrip transmission-line array can mitigate sample-induced RF non-uniformities, and has been widely used as the transceiver in parallel imaging applications. One major challenge in implementing the microstrip array is the reduction of mutual coupling among individual elements. The low-input impedance preamplifier is commonly used for the decoupling purpose. However, it is impractical in the transceiver array design. Although interconnecting capacitors can be utilized to reduce the mutual coupling, they only efficiently work for the neighbor elements. In addition, this approach is impractical at fields higher than 300 MHz, in which the required decoupling capacitance is commonly less than 0.5 pF. We propose a novel decoupling approach by using decoupling inductors in this study. Due to the fact that the decoupling inductance is independent of the resonant frequency, the microstrip arrays can be well decoupled at ultra-high fields. To verify the proposed approach, an eight-channel microstrip array is fabricated and tested at 9.4 T. For this prototype, couplings between elements are significantly reduced by using the interconnecting inductors. The phantom experiment shows that the inductively decoupled microstrip array has good parallel imaging performance.

Equipment Design↗

PubMed2006

Discrepancy-based adaptive regularization for GRAPPA reconstruction.

PURPOSE: To develop a novel regularization method for GRAPPA by which the regularization parameters can be optimally and adaptively chosen. MATERIALS AND METHODS: In the fit procedures in GRAPPA, the discrepancy principle, which chooses the regularization parameter based on a priori information about the noise level in the autocalibrating signals (ACS), is used with the truncated singular value decomposition (TSVD) regularization and the Tikhonov regularization, and its performance is compared with the singular value (SV) threshold method and the L-curve method, respectively by axial and sagittal head imaging experiments. RESULTS: In both axial and sagittal reconstructions, normal GRAPPA reconstruction results exhibit a relatively high level of noise. With discrepancy-based choices of parameters, regularization can improve the signal-to-noise ratio (SNR) with only a very modest increase in aliasing artifacts. The L-curve method in all of the reconstructions leads to overregularization, which causes severe residual aliasing artifacts. The 10% SV threshold method yields good overall image quality in the axial case, but in the sagittal case it also leads to an obvious increase in aliasing artifacts. CONCLUSION: Neither a fixed SV threshold nor the L-curve are robust means of choosing the appropriate parameters in GRAPPA reconstruction. However, with the discrepancy-based parameter-choice strategy, adaptively regularized GRAPPA can be used to automatically choose nearly optimal parameters for reconstruction and achieve an excellent compromise between SNR and artifacts.

Algorithms↗

PubMed2006

B1 field, SAR, and SNR comparisons for birdcage, TEM, and microstrip coils at 7T.

PURPOSE: To compare the performance of birdcage, transverse electromagnetic (TEM) and microstrip volume coils at 7T under the same geometric conditions. MATERIALS AND METHODS: Birdcage, TEM, and microstrip coils are modeled with the same dimensions. The finite difference time domain (FDTD) method is adopted to calculate the electromagnetic fields of the coils. Further, B(1) field, specific absorption rate (SAR) and signal-to-noise ratio (SNR) are calculated for these coils. RESULTS: In the unloaded case, within the central axial plane, the variation of B(1) field magnitude over 18-cm distance is about 15% for the birdcage coil, 23% for the TEM coil, and 38% for the microstrip coil. In the loaded case, the percentages of the samples on the central axial plane, which have B(1) field magnitude within +/-20% of the average B(1) field magnitude, are about 57% for the birdcage, 72% for the TEM, and 59% for the microstrip coil. Average SAR levels are 11.4% and 42.9% higher in the birdcage than those in the TEM and microstrip coils, respectively. The average relative SNR on the central axial plane for the shielded birdcage, TEM, and microstrip coils are 1, 1.07, and 1.48, respectively. CONCLUSION: The birdcage coil has the best unloaded B(1) field homogeneity, and the TEM coil has the best loaded B(1) field homogeneity and the lowest radiation loss; while the microstrip coil is better in SAR and SNR at 7T than the birdcage and TEM coils.

Electromagnetic Fields↗

PubMed2005

Convergence behavior of iterative SENSE reconstruction with non-Cartesian trajectories.

In non-Cartesian SENSE reconstruction based on the conjugate gradient (CG) iteration method, the iteration very often exhibits a "semi-convergence" behavior, which can be characterized as initial convergence toward the exact solution and later divergence. This phenomenon causes difficulties in automatic implementation of this reconstruction strategy. In this study, the convergence behavior of the iterative SENSE reconstruction is analyzed based on the mathematical principle of the CG method. It is revealed that the semi-convergence behavior is caused by the ill-conditioning of the underlying generalized encoding matrix (GEM) and the intrinsic regularization effect of CG iteration. From the perspective of regularization, each iteration vector is a regularized solution and the number of iterations plays the role of the regularization parameter. Therefore, the iteration count controls the compromise between the SNR and the residual aliasing artifact. Based on this theory, suggestions with respect to the stopping rule for well-behaved reconstructions are provided. Simulated radial imaging and in vivo spiral imaging are performed to demonstrate the theoretical analysis on the semi-convergence phenomenon and the stopping criterion. The dependence of convergence behavior on the undersampling rate and the noise level in samples is also qualitatively investigated.

Algorithms↗

PubMed2005

Tailored utilization of acquired k-space points for GRAPPA reconstruction.

The generalized auto-calibrating partially parallel acquisition (GRAPPA) is an auto-calibrating parallel imaging technique which incorporates multiple blocks of data to derive the missing signals. In the original GRAPPA reconstruction algorithm only the data points in phase encoding direction are incorporated to reconstruct missing points in k-space. It has been recognized that this scheme can be extended so that data points in readout direction are also utilized and the points are selected based on a k-space locality criterion. In this study, an automatic subset selection strategy is proposed which can provide a tailored selection of source points for reconstruction. This novel approach extracts a subset of signal points corresponding to the most linearly independent base vectors in the coefficient matrix of fit, effectively preventing incorporating redundant signals which only bring noise into reconstruction with little contribution to the exactness of fit. Also, subset selection in this way has a regularization effect since the vectors corresponding to the smallest singular values are eliminated and consequently the condition of the reconstruction is improved. Phantom and in vivo MRI experiments demonstrate that this subset selection strategy can effectively improve SNR and reduce residual artifacts for GRAPPA reconstruction.

Algorithms↗