Publications by authors named "Yudu Li"

16 Publications

  • Page 1 of 1

Machine Learning-Enabled High-Resolution Dynamic Deuterium MR Spectroscopic Imaging.

IEEE Trans Med Imaging 2021 Jul 28;PP. Epub 2021 Jul 28.

Deuterium magnetic resonance spectroscopic imaging (DMRSI) has recently been recognized as a potentially powerful tool for noninvasive imaging of brain energy metabolism and tumor. However, the low sensitivity of DMRSI has significantly limited its utility for both research and clinical applications. This work presents a novel machine learning-based method to address this limitation. The proposed method synergistically integrates physics-based subspace modeling and data-driven deep learning for effective denoising, making high-resolution dynamic DMRSI possible. Specifically, a novel subspace model was used to represent the dynamic DMRSI signals; deep neural networks were trained to capture the low-dimensional manifolds of the spectral and temporal distributions of practical dynamic DMRSI data. The learned subspace and manifold structures were integrated via a regularization formulation to remove measurement noise. Theoretical analysis, computer simulations, and in vivo experiments have been conducted to demonstrate the denoising efficacy of the proposed method which enabled high-resolution imaging capability. The translational potential was demonstrated in tumor-bearing rats, where the Warburg effect associated with cancer metabolism and tumor heterogeneity were successfully captured. The new method may not only provide an effective tool to enhance the sensitivity of DMRSI for basic research and clinical applications but also provide a framework for denoising other spatiospectral data.
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http://dx.doi.org/10.1109/TMI.2021.3101149DOI Listing
July 2021

Improved estimation of myelin water fractions with learned parameter distributions.

Magn Reson Med 2021 Jul 3. Epub 2021 Jul 3.

Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Purpose: To improve estimation of myelin water fraction (MWF) in the brain from multi-echo gradient-echo imaging data.

Methods: A systematic sensitivity analysis was first conducted to characterize the conventional exponential models used for MWF estimation. A new estimation method was then proposed for improved estimation of MWF from practical gradient-echo imaging data. The proposed method uses an extended signal model that includes a finite impulse response filter to compensate for practical signal variations. This new model also enables the use of prelearned parameter distributions as well as low-rank signal structures to improve parameter estimation. The resulting parameter estimation problem was solved optimally in the Bayesian sense.

Results: Our sensitivity analysis results showed that the conventional exponential models were very sensitive to measurement noise and modeling errors. Our simulation and experimental results showed that our proposed method provided a substantial improvement in reliability, reproducibility, and robustness of MWF estimates over the conventional methods. Clinical results obtained from stroke patients indicated that the proposed method, with its improved capability, could reveal the loss of myelin in lesions, demonstrating its translational potentials.

Conclusion: This paper addressed the problem of robust MWF estimation from gradient-echo imaging data. A new method was proposed to provide improved MWF estimation in the presence of significant noise and modeling errors. The performance of the proposed method has been evaluated using both simulated and experimental data, showing significantly improved robustness over the existing methods. The proposed method may prove useful for quantitative myelin imaging in clinical applications.
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http://dx.doi.org/10.1002/mrm.28889DOI Listing
July 2021

High-resolution sodium imaging using anatomical and sparsity constraints for denoising and recovery of novel features.

Magn Reson Med 2021 08 25;86(2):625-636. Epub 2021 Mar 25.

Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Purpose: To develop and evaluate a novel method for reconstruction of high-quality sodium MR images from noisy, limited k-space data.

Theory And Methods: A novel reconstruction method was developed for reconstruction of high-quality sodium images from noisy, limited k-space data. This method is based on a novel image model that contains a motion-compensated generalized series model and a sparse model. The motion-compensated generalized series model enables effective use of anatomical information from a proton image for denoising and resolution enhancement of sodium data, whereas the sparse model enables high-resolution reconstruction of sodium-dependent novel features. The underlying model estimation problems were solved efficiently using convex optimization algorithms.

Results: The proposed method has been evaluated using both simulation and experimental data obtained from phantoms, healthy human volunteers, and tumor patients. Results showed a substantial improvement in spatial resolution and SNR over state-of-the-art reconstruction methods, including compressed sensing and anatomically constrained reconstruction methods. Quantitative tissue sodium concentration maps were obtained from both healthy volunteers and brain tumor patients. These tissue sodium concentration maps showed improved lesion fidelity and allowed accurate interrogation of small targets.

Conclusion: A new method has been developed to obtain high-resolution sodium images with good SNR at 3 T. The proposed method makes effective use of anatomical prior information for denoising, while using a sparse model synergistically to recover sodium-dependent novel features. Experimental results have been obtained to demonstrate the feasibility of achieving high-quality tissue sodium concentration maps and their potential for improved detection of spatially heterogeneous responses of tumor to treatment.
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http://dx.doi.org/10.1002/mrm.28767DOI Listing
August 2021

Fast high-resolution metabolic imaging of acute stroke with 3D magnetic resonance spectroscopy.

Brain 2020 12;143(11):3225-3233

High-Dimensional Neurology Group, Institute of Neurology, University College London, London, UK.

Impaired oxygen and cellular metabolism is a hallmark of ischaemic injury in acute stroke. Magnetic resonance spectroscopic imaging (MRSI) has long been recognized as a potentially powerful tool for non-invasive metabolic imaging. Nonetheless, long acquisition time, poor spatial resolution, and narrow coverage have limited its clinical application. Here we investigated the feasibility and potential clinical utility of rapid, high spatial resolution, near whole-brain 3D metabolic imaging based on a novel MRSI technology. In an 8-min scan, we simultaneously obtained 3D maps of N-acetylaspartate and lactate at a nominal spatial resolution of 2.0 × 3.0 × 3.0 mm3 with near whole-brain coverage from a cohort of 18 patients with acute ischaemic stroke. Serial structural and perfusion MRI was used to define detailed spatial maps of tissue-level outcomes against which high-resolution metabolic changes were evaluated. Within hypoperfused tissue, the lactate signal was higher in areas that ultimately infarcted compared with those that recovered (P < 0.0001). Both lactate (P < 0.0001) and N-acetylaspartate (P < 0.001) differed between infarcted and other regions. Within the areas of diffusion-weighted abnormality, lactate was lower where recovery was observed compared with elsewhere (P < 0.001). This feasibility study supports further investigation of fast high-resolution MRSI in acute stroke.
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http://dx.doi.org/10.1093/brain/awaa264DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719019PMC
December 2020

Accelerating T mapping of the brain by integrating deep learning priors with low-rank and sparse modeling.

Magn Reson Med 2021 03 29;85(3):1455-1467. Epub 2020 Sep 29.

Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Purpose: To accelerate T mapping with highly sparse sampling by integrating deep learning image priors with low-rank and sparse modeling.

Methods: The proposed method achieves high-speed T mapping by highly sparsely sampling (k, TE)-space. Image reconstruction from the undersampled data was done by exploiting the low-rank structure and sparsity in the T -weighted image sequence and image priors learned from training data. The image priors for a single TE were generated from the public Human Connectome Project data using a tissue-based deep learning method; the image priors were then transferred to other TEs using a generalized series-based method. With these image priors, the proposed reconstruction method used a low-rank model and a sparse model to capture subject-dependent novel features.

Results: The proposed method was evaluated using experimental data obtained from both healthy subjects and tumor patients using a turbo spin-echo sequence. High-quality T maps at the resolution of 0.9 × 0.9 × 3.0 mm were obtained successfully from highly undersampled data with an acceleration factor of 8. Compared with the existing compressed sensing-based methods, the proposed method produced significantly reduced reconstruction errors. Compared with the deep learning-based methods, the proposed method recovered novel features better.

Conclusion: This paper demonstrates the feasibility of learning T -weighted image priors for multiple TEs using tissue-based deep learning and generalized series-based learning. A new method was proposed to effectively integrate these image priors with low-rank and sparse modeling to reconstruct high-quality images from highly undersampled data. The proposed method will supplement other acquisition-based methods to achieve high-speed T mapping.
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http://dx.doi.org/10.1002/mrm.28526DOI Listing
March 2021

Simultaneous QSM and metabolic imaging of the brain using SPICE: Further improvements in data acquisition and processing.

Magn Reson Med 2021 02 18;85(2):970-977. Epub 2020 Aug 18.

Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois.

Purpose: To achieve high-resolution mapping of brain tissue susceptibility in simultaneous QSM and metabolic imaging.

Methods: Simultaneous QSM and metabolic imaging was first achieved using SPICE (spectroscopic imaging by exploiting spatiospectral correlation), but the QSM maps thus obtained were at relatively low-resolution (2.0 × 3.0 × 3.0 mm ). We overcome this limitation using an improved SPICE data acquisition method with the following novel features: 1) sampling (k, t)-space in dual densities, 2) sampling central k-space fully to achieve nominal spatial resolution of 3.0 × 3.0 × 3.0 mm for metabolic imaging, and 3) sampling outer k-space sparsely to achieve spatial resolution of 1.0 × 1.0 × 1.9 mm for QSM. To keep the scan time short, we acquired spatiospectral encodings in echo-planar spectroscopic imaging trajectories in central k-space but in CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) trajectories in outer k-space using blipped phase encodings. For data processing and image reconstruction, a union-of-subspaces model was used, effectively incorporating sensitivity encoding, spatial priors, and spectral priors of individual molecules.

Results: In vivo experiments were carried out to evaluate the feasibility and potential of the proposed method. In a 6-min scan, QSM maps at 1.0 × 1.0 × 1.9 mm resolution and metabolic maps at 3.0 × 3.0 × 3.0 mm nominal resolution were obtained simultaneously. Compared with the original method, the QSM maps obtained using the new method reveal fine-scale brain structures more clearly.

Conclusion: We demonstrated the feasibility of achieving high-resolution QSM simultaneously with metabolic imaging using a modified SPICE acquisition method. The improved capability of SPICE may further enhance its practical utility in brain mapping.
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http://dx.doi.org/10.1002/mrm.28459DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7722130PMC
February 2021

Accelerated J-resolved H-MRSI with limited and sparse sampling of ( -space.

Magn Reson Med 2021 01 29;85(1):30-41. Epub 2020 Jul 29.

Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Purpose: To accelerate the acquisition of J-resolved proton magnetic resonance spectroscopic imaging ( H-MRSI) data for high-resolution mapping of brain metabolites and neurotransmitters.

Methods: The proposed method used a subspace model to represent multidimensional spatiospectral functions, which significantly reduced the number of parameters to be determined from J-resolved H-MRSI data. A semi-LASER-based (Localization by Adiabatic SElective Refocusing) echo-planar spectroscopic imaging (EPSI) sequence was used for data acquisition. The proposed data acquisition scheme sampled -space in variable density, where t and t specify the J-coupling and chemical-shift encoding times, respectively. Selection of the J-coupling encoding times (or, echo time values) was based on a Cramer-Rao lower bound analysis, which were optimized for gamma-aminobutyric acid (GABA) detection. In image reconstruction, parameters of the subspace-based spatiospectral model were determined by solving a constrained optimization problem.

Results: Feasibility of the proposed method was evaluated using both simulated and experimental data from a spectroscopic phantom. The phantom experimental results showed that the proposed method, with a factor of 12 acceleration in data acquisition, could determine the distribution of J-coupled molecules with expected accuracy. In vivo study with healthy human subjects also showed that 3D maps of brain metabolites and neurotransmitters can be obtained with a nominal spatial resolution of 3.0 × 3.0 × 4.8 mm from J-resolved H-MRSI data acquired in 19.4 min.

Conclusions: This work demonstrated the feasibility of highly accelerated J-resolved H-MRSI using limited and sparse sampling of -space and subspace modeling. With further development, the proposed method may enable high-resolution mapping of brain metabolites and neurotransmitters in clinical applications.
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http://dx.doi.org/10.1002/mrm.28413DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7992196PMC
January 2021

High-Resolution Dynamic P-MR Spectroscopic Imaging for Mapping Mitochondrial Function.

IEEE Trans Biomed Eng 2020 10 28;67(10):2745-2753. Epub 2020 Jan 28.

Objective: To enable non-invasive dynamic metabolic mapping in rodent model studies of mitochondrial function using P-MR spectroscopic imaging (MRSI).

Methods: We developed a novel method for high-resolution dynamic P-MRSI. The method synergistically integrates physics-based models of spectral structures, biochemical modeling of molecular dynamics, and subspace learning to capture spatiospectral variations. Fast data acquisition was achieved using rapid spiral trajectories and sparse sampling of (k, t, T)-space; image reconstruction was accomplished using a low-rank tensor-based framework.

Results: The proposed method provided high-resolution dynamic metabolic mapping in rat hindlimb at spatial and temporal resolutions of 4[Formula: see text]2 mm and 1.28 s, respectively. This allowed for in vivo mapping of the time-constant of phosphocreatine resynthesis, a well established index of mitochondrial oxidative capacity. Multiple rounds of in vivo experiments were performed to demonstrate reproducibility, and in vitro experiments were used to validate the accuracy of the estimated metabolite maps.

Conclusions: A new model-based method is proposed to achieve high-resolution dynamic P-MRSI. The proposed method's ability to delineate metabolic heterogeneity was demonstrated in rat hindlimb.

Significance: Abnormal mitochondrial metabolism is a key cellular dysfunction in many prevalent diseases such as diabetes and heart disease; however, current understanding of mitochondrial function is mostly gained from studies on isolated mitochondria under nonphysiological conditions. The proposed method has the potential to open new avenues of research by allowing in vivo and longitudinal studies of mitochondrial dysfunction in disease development and progression.
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http://dx.doi.org/10.1109/TBME.2020.2969892DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384926PMC
October 2020

Improved Low-Rank Filtering of MR Spectroscopic Imaging Data With Pre-Learnt Subspace and Spatial Constraints.

IEEE Trans Biomed Eng 2020 08 23;67(8):2381-2388. Epub 2019 Dec 23.

Objective: To investigate the use of pre-learnt subspace and spatial constraints for denoising magnetic resonance spectroscopic imaging (MRSI) data.

Method: We exploit the partial separability or subspace structures of high-dimensional MRSI data for denoising. More specifically, we incorporate a subspace model with pre-learnt spectral basis into the low-rank approximation (LORA) method. Spectral basis is determined based on empirical prior distributions of the spectral parameters variations learnt from auxiliary training data; spatial priors are also incorporated as is done in LORA to further improve denoising performance.

Results: The effects of the explicit subspace and spatial constraints in reducing estimation bias and variance have been analyzed using Cramér-Rao Lower bound analysis, Monte-Carlo study, and experimental study.

Conclusion: The denoising effectiveness of LORA can be significantly improved by incorporating pre-learnt spectral basis and spatial priors into LORA.

Significance: This study provides an effective method for denoising MRSI data along with comprehensive analyses of its performance. The proposed method is expected to be useful for a wide range of studies using MRSI.
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http://dx.doi.org/10.1109/TBME.2019.2961698DOI Listing
August 2020

Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces.

Magn Reson Med 2020 02 4;83(2):377-390. Epub 2019 Sep 4.

Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, Illinois.

Purpose: To develop a subspace learning method for the recently proposed subspace-based MRSI approach known as SPICE, and achieve ultrafast H-MRSI of the brain.

Theory And Methods: A novel strategy is formulated to learn a low-dimensional subspace representation of MR spectra from specially acquired training data and use the learned subspace for general MRSI experiments. Specifically, the subspace learning problem is formulated as learning "empirical" distributions of molecule-specific spectral parameters (e.g., concentrations, lineshapes, and frequency shifts) by integrating physics-based model and the training data. The learned spectral parameters and quantum mechanical simulation basis can then be combined to construct acquisition-specific subspace for spatiospectral encoding and processing. High-resolution MRSI acquisitions combining ultrashort-TE/short-TR excitation, sparse sampling, and the elimination of water suppression have been performed to evaluate the feasibility of the proposed method.

Results: The accuracy of the learned subspace and the capability of the proposed method in producing high-resolution 3D H metabolite maps and high-quality spatially resolved spectra (with a nominal resolution of ∼2.4 × 2.4 × 3 mm in 5 minutes) were demonstrated using phantom and in vivo studies. By eliminating water suppression, we are also able to extract valuable information from the water signals for data processing ( map, frequency drift, and coil sensitivity) as well as for mapping tissue susceptibility and relaxation parameters.

Conclusions: The proposed method enables ultrafast H-MRSI of the brain using a learned subspace, eliminating the need of acquiring subject-dependent navigator data (known as ) in the original SPICE technique. It represents a new way to perform MRSI experiments and an important step toward practical applications of high-resolution MRSI.
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http://dx.doi.org/10.1002/mrm.27980DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6824949PMC
February 2020

Simultaneous metabolic and functional imaging of the brain using SPICE.

Magn Reson Med 2019 12 11;82(6):1993-2002. Epub 2019 Jul 11.

Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois.

Purpose: To enable simultaneous high-resolution mapping of brain function and metabolism.

Methods: An encoding scheme was designed for interleaved acquisition of functional MRI (fMRI) data in echo volume imaging trajectories and MR spectroscopic imaging (MRSI) data in echo-planar spectroscopic imaging trajectories. The scheme eliminates water and lipid suppression and utilizes free induction decay signals to encode both functional and metabolic information with ultrashort TE, short TR, and sparse sampling of -space. A subspace-based image reconstruction method was introduced for processing both the fMRI and MRSI data. The complementary information in the fMRI and MRSI data sets was also utilized to improve image reconstruction in the presence of intrascan head motion, field drift, and tissue susceptibility changes.

Results: In-vivo experimental results were obtained from healthy human subjects in resting-state fMRI/MRSI experiments. In these experiments, the proposed method was able to simultaneously acquire metabolic and functional information from the brain in high resolution. For scans of 6.5 minutes, we achieved 3.0 × 3.0 × 1.8 mm spatial resolution for fMRI, 1.9 × 2.5 × 3.0 mm nominal spatial resolution for MRSI, and 1.9 × 1.9 × 1.8 mm nominal spatial resolution for quantitative susceptibility maps.

Conclusion: This work demonstrates the feasibility of simultaneous high-resolution mapping of brain function and metabolism with improved spatial resolution and synergistic image reconstruction.
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http://dx.doi.org/10.1002/mrm.27865DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6717045PMC
December 2019

Simultaneous QSM and metabolic imaging of the brain using SPICE.

Magn Reson Med 2018 Jan 24;79(1):13-21. Epub 2017 Oct 24.

Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Purpose: To map brain metabolites and tissue magnetic susceptibility simultaneously using a single three-dimensional H-MRSI acquisition without water suppression.

Methods: The proposed technique builds on a subspace imaging method called spectroscopic imaging by exploiting spatiospectral correlation (SPICE), which enables ultrashort echo time (TE)/short pulse repetition time (TR) acquisitions for H-MRSI without water suppression. This data acquisition scheme simultaneously captures both the spectral information of brain metabolites and the phase information of the water signals that is directly related to tissue magnetic susceptibility variations. In extending this scheme for simultaneous QSM and metabolic imaging, we increase k-space coverage by using dual density sparse sampling and ramp sampling to achieve spatial resolution often required by QSM, while maintaining a reasonable signal-to-noise ratio (SNR) for the spatiospectral data used for metabolite mapping. In data processing, we obtain high-quality QSM from the unsuppressed water signals by taking advantage of the larger number of echoes acquired and any available anatomical priors; metabolite spatiospectral distributions are reconstructed using a union-of-subspaces model.

Results: In vivo experimental results demonstrate that the proposed method can produce susceptibility maps at a resolution higher than 1.8 × 1.8 × 2.4 mm along with metabolite spatiospectral distributions at a nominal spatial resolution of 2.4 × 2.4 × 2.4 mm from a single 7-min MRSI scan. The estimated susceptibility values are consistent with those obtained using the conventional QSM method with 3D multi-echo gradient echo acquisitions.

Conclusion: This article reports a new capability for simultaneous susceptibility mapping and metabolic imaging of the brain from a single H-MRSI scan, which has potential for a wide range of applications. Magn Reson Med 79:13-21, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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http://dx.doi.org/10.1002/mrm.26972DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5744903PMC
January 2018

Macromolecule mapping of the brain using ultrashort-TE acquisition and reference-based metabolite removal.

Magn Reson Med 2018 05 3;79(5):2460-2469. Epub 2017 Sep 3.

Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Purpose: To develop a practical method for mapping macromolecule distribution in the brain using ultrashort-TE MRSI data.

Methods: An FID-based chemical shift imaging acquisition without metabolite-nulling pulses was used to acquire ultrashort-TE MRSI data that capture the macromolecule signals with high signal-to-noise-ratio (SNR) efficiency. To remove the metabolite signals from the ultrashort-TE data, single voxel spectroscopy data were obtained to determine a set of high-quality metabolite reference spectra. These spectra were then incorporated into a generalized series (GS) model to represent general metabolite spatiospectral distributions. A time-segmented algorithm was developed to back-extrapolate the GS model-based metabolite distribution from truncated FIDs and remove it from the MRSI data. Numerical simulations and in vivo experiments have been performed to evaluate the proposed method.

Results: Simulation results demonstrate accurate metabolite signal extrapolation by the proposed method given a high-quality reference. For in vivo experiments, the proposed method is able to produce spatiospectral distributions of macromolecules in the brain with high SNR from data acquired in about 10 minutes. We further demonstrate that the high-dimensional macromolecule spatiospectral distribution resides in a low-dimensional subspace. This finding provides a new opportunity to use subspace models for quantification and accelerated macromolecule mapping. Robustness of the proposed method is also demonstrated using multiple data sets from the same and different subjects.

Conclusion: The proposed method is able to obtain macromolecule distributions in the brain from ultrashort-TE acquisitions. It can also be used for acquiring training data to determine a low-dimensional subspace to represent the macromolecule signals for subspace-based MRSI. Magn Reson Med 79:2460-2469, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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http://dx.doi.org/10.1002/mrm.26896DOI Listing
May 2018

A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging.

IEEE Trans Biomed Eng 2017 10 18;64(10):2486-2489. Epub 2017 Aug 18.

Objective: To provide a new approach to spectral quantification for magnetic resonance spectroscopic imaging (MRSI), incorporating both spatial and spectral priors.

Methods: A novel signal model is proposed, which represents the spectral distributions of each molecule as a subspace and the entire spectrum as a union of subspaces. Based on this model, the spectral quantification can be solved in two steps: 1) subspace estimation based on the empirical distributions of the spectral parameters estimated using spectral priors; and 2) parameter estimation for the union-of-subspaces model incorporating spatial priors.

Results: The proposed method has been evaluated using both simulated and experimental data, producing impressive results.

Conclusion: The proposed union-of-subspaces representation of spatiospectral functions provides an effective computational framework for solving the MRSI spectral quantification problem with spatiospectral constraints.

Significance: The proposed approach transforms how the MRSI spectral quantification problem is solved and enables efficient and effective use of spatiospectral priors to improve parameter estimation. The resulting algorithm is expected to be useful for a wide range of quantitative metabolic imaging studies using MRSI.
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http://dx.doi.org/10.1109/TBME.2017.2741922DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5646283PMC
October 2017

Death Penalty Disposition in China: What Matters?

Int J Offender Ther Comp Criminol 2018 Jan 7;62(1):253-273. Epub 2016 Apr 7.

2 University of Nevada, Las Vegas, USA.

In theory, sentencing decisions should be driven by legal factors, not extra-legal factors. However, some empirical research on the death penalty in the United States shows significant relationships between offender and victim characteristics and death sentence decisions. Despite the fact that China frequently imposes death sentences, few studies have examined these sanctions to see if similar correlations occur in China's capital cases. Using data from published court cases in China involving three violent crimes-homicide, robbery, and intentional assault-this study examines the net impact of offender's gender, race, and victim-offender relationship on death sentence decisions in China. Our overall multiple regression results indicate that, after controlling for other legal and extra-legal variables, an offender's gender, race, and victim-offender relationship did not produce similar results in China when compared with those in the United States. In contrast, it is the legal factors that played the most significant role in influencing the death penalty decisions. The article concludes with explanations and speculations on the unique social, cultural, and legal conditions in China that may have contributed to these correlations.
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http://dx.doi.org/10.1177/0306624X16642426DOI Listing
January 2018

Single trial EEG classification applied to a face recognition experiment using different feature extraction methods.

Annu Int Conf IEEE Eng Med Biol Soc 2015 ;2015:7246-9

Research on brain machine interface (BMI) has been developed very fast in recent years. Numerous feature extraction methods have successfully been applied to electroencephalogram (EEG) classification in various experiments. However, little effort has been spent on EEG based BMI systems regarding familiarity of human faces cognition. In this work, we have implemented and compared the classification performances of four common feature extraction methods, namely, common spatial pattern, principal component analysis, wavelet transform and interval features. High resolution EEG signals were collected from fifteen healthy subjects stimulated by equal number of familiar and novel faces. Principal component analysis outperforms other methods with average classification accuracy reaching 94.2% leading to possible real life applications. Our findings thereby may contribute to the BMI systems for face recognition.
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http://dx.doi.org/10.1109/EMBC.2015.7320064DOI Listing
September 2016
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