A5004577779- Advanced Neuroimaging Techniques and Applications
- Advanced MRI Techniques and Applications
- MRI in cancer diagnosis
- Functional Brain Connectivity Studies
- Bone and Joint Diseases
- Sparse and Compressive Sensing Techniques
- NMR spectroscopy and applications
- Geometric Analysis and Curvature Flows
- Fetal and Pediatric Neurological Disorders
- Transcranial Magnetic Stimulation Studies
- Neural dynamics and brain function
- Morphological variations and asymmetry
- Point processes and geometric inequalities
- Tensor decomposition and applications
- Geometry and complex manifolds
- Advanced Differential Geometry Research
- Medical Imaging Techniques and Applications
- Control Systems and Identification
- Statistical and numerical algorithms
- Topological and Geometric Data Analysis
- Statistical Methods and Inference
- Integrated Circuits and Semiconductor Failure Analysis
- Neurological Disorders and Treatments
- Noncommutative and Quantum Gravity Theories
- Numerical methods in inverse problems
Brigham and Women's Hospital
2016-2025
Harvard University
2016-2025
Chongqing University
2024-2025
Second People's Hospital of NanTong
2025
Liaoning Planning and Design Institute of Post and Telecommunication
2024
Beijing Jiaotong University
2024
University of California, Los Angeles
2024
Chestnut Hill College
2024
Somerville Hospital
2023
Massachusetts General Hospital
2021
Abstract Neuroimaging with MRI has been a frequent component of studies individuals at clinical high risk (CHR) for developing psychosis, goals understanding potential brain regions and systems impacted in the CHR state identifying prognostic or predictive biomarkers that can enhance our ability to forecast outcomes. To date, most involving are likely not sufficiently powered generate robust generalizable neuroimaging results. Here, we describe prospective, advanced, modern protocol was...
Diffusion MRI is being used increasingly in studies of the brain and other parts body for its ability to provide quantitative measures that are sensitive changes tissue microstructure. However, inter-scanner inter-protocol differences known induce significant measurement variability, which turn jeopardises obtain ‘truly measures’ challenges reliable combination different datasets. Combining datasets from scanners and/or acquired at time points could dramatically increase statistical power...
Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability aggregate dMRI derived measures. Computational algorithms that harmonize minimize such critical reliably combine datasets acquired from different scanners and/or protocols, thus improving statistical power sensitivity studies. Different computational approaches have been proposed MRI or remove...
The ensemble average diffusion propagator (EAP) obtained from MRI (dMRI) data captures important structural properties of the underlying tissue. As such, it is imperative to derive an accurate estimate EAP acquired data. In this work, we propose a novel method for estimating by representing signal as linear combination directional radial basis functions scattered in q-space. particular, focus on special case anisotropic Gaussian and analytical expressions orientation distribution function...
Abstract Purpose To reduce the inter‐scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor‐neutral dMRI pulse sequence using open‐source vendor‐agnostic Pulseq platform. Methods We implemented standard EPI based in . tested it on two clinical (Siemens Prisma and GE Premier), systematically evaluating comparing within‐ across vendors, both vendor‐provided sequences. Assessments covered phantom three human subjects, error (SE) Lin's...
To develop an accelerated, robust, and accurate diffusion MRI acquisition reconstruction technique for submillimeter whole human brain in vivo scan on a clinical scanner.We extend the ultra-high resolution technique, gSlider, by allowing undersampling q-space radiofrequency (RF)-encoding space, thereby dramatically reducing total time of conventional gSlider. The novel method, termed gSlider-SR, compensates lack acquired information exploiting redundancy dMRI data using basis spherical...
We first introduce a class of divergence measures between power spectral density matrices. These are derived by comparing the suitability different models in context optimal prediction. Distances "infinitesimally close" spectra quadratic, and hence, they induce differential-geometric structure. study corresponding Riemannian metrics and, for particular case, provide explicit formulae geodesics geodesic distances. The close connection geometry Fisher-Rao metric is noted.
We present a particular formulation of optimal transport for matrix-valued density functions. Our aim is to devise geometry which suitable comparing power spectral densities multivariable time series. More specifically, the value at given frequency, in matricial case encodes as well directionality, thought proxy "matrix-valued mass density." Optimal aims establishing natural metric space such takes into account differences between across frequencies misalignment corresponding principle axes....
Elucidating developmental trajectories of white matter (WM) microstructure is critically important for understanding normal development and regional vulnerabilities in several brain disorders. Diffusion Weighted Imaging (DWI) currently the method choice in-vivo assessment. A majority neonatal studies use standard Tensor (DTI) model although more advanced models such as Neurite Orientation Dispersion Density (NODDI) Gaussian Mixture Model (GMM) have been used adult population. In this study,...
Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially patients with brain tumors, tracing tracts that are affected by vasogenic edema, which increases water content the tissue and lowers diffusion anisotropy. One strategy improving tracking use method more sensitive than traditional single-tensor streamline tractography. We performed experiments assess...
The rate of water exchange across cell membranes is a parameter biological interest and can be measured by diffusion magnetic resonance imaging (dMRI). In this work, we investigate stochastic model for the diffusion-and-exchange molecules. This provides general solution temporal evolution dMRI signal using any type gradient waveform, thereby generalizing expressions Kärger model. Moreover, also derive nth order cumulant expansion accounting exchange, which has not been explored in earlier...
Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting Laplace transform to recover joint distribution of relaxation-diffusion. However, as is well-known, this problem notoriously ill-posed and numerically unstable. In work, we address issue by directly computing moments transverse relaxation rate diffusivity, which be robustly estimated. To zoom into different parts distribution, further...
Abstract Diffusion magnetic resonance imaging (dMRI) allows to estimate brain tissue microstructure as well the connectivity of white matter (known tractography). Accurate estimation model parameters (by solving inverse problem) is thus very important infer underlying biophysical properties and fiber orientations. Although there has been extensive research on this topic with a myriad dMRI models, most models use standard nonlinear optimization techniques only provide an without any...
Parcellation of anatomically segregated cortical and subcortical brain regions is required in diffusion MRI (dMRI) analysis for region-specific quantification better anatomical specificity tractography. Most current dMRI parcellation approaches compute the from (T1- or T2-weighted) data, using tools such as FreeSurfer CAT12, then register it to space. However, registration challenging due image distortions low resolution often resulting mislabeling derived parcellation. Furthermore, these...