A5073128847- Advanced MRI Techniques and Applications
- Machine Learning in Materials Science
- Liver Disease Diagnosis and Treatment
- Atomic and Subatomic Physics Research
- Sparse and Compressive Sensing Techniques
- Medical Imaging Techniques and Applications
- Metabolomics and Mass Spectrometry Studies
- Bayesian Methods and Mixture Models
- Probabilistic and Robust Engineering Design
- Cardiac Imaging and Diagnostics
- Gaussian Processes and Bayesian Inference
- Model Reduction and Neural Networks
- MRI in cancer diagnosis
- Medical Image Segmentation Techniques
- Advanced X-ray Imaging Techniques
- Catalysis and Oxidation Reactions
- Mathematical Analysis and Transform Methods
- Control Systems and Identification
- Image and Signal Denoising Methods
- Computational Drug Discovery Methods
- NMR spectroscopy and applications
- Numerical methods in inverse problems
- Speech and Audio Processing
- Point processes and geometric inequalities
- Protein Structure and Dynamics
Pontificia Universidad Católica de Chile
2011-2024
Millennium Science Initiative
2019-2023
Stanford University
2013-2016
In an increasing number of applications, it is interest to recover approximately low-rank data matrix from noisy observations. This paper develops unbiased risk estimate---holding in a Gaussian model---for any spectral estimator obeying some mild regularity assumptions. particular, we give estimate formula for singular value thresholding (SVT), popular estimation strategy which applies soft-thresholding rule the values Among other things, our formulas offer principled and automated way...
In compressed sensing, one takes samples of an N -dimensional vector using matrix A , obtaining undersampled measurements . For random matrices with independent standard Gaussian entries, it is known that, when k -sparse, there a precisely determined phase transition: for certain region in the ( )-phase diagram, convex optimization typically finds sparsest solution, whereas outside that region, fails. It has been shown empirically same property—with transition location—holds wide range...
Purpose Quantitative T 1 , 2 *, and fat fraction (FF) maps are promising imaging biomarkers for the assessment of liver disease, however these usually acquired in sequential scans. Here we propose an extended MR fingerprinting (MRF) framework enabling simultaneous FF mapping from a single ~14 s breath‐hold scan. Methods A gradient echo (GRE) MRF sequence with nine readouts per TR, low flip angles (5‐15°), varying magnetisation preparation golden angle radial trajectory is at 1.5T to encode...
Resolving the details of an object from coarse-scale measurements is a classical problem in applied mathematics. This usually formulated as extrapolating Fourier transform bounded region to entire space, that is, terms performing extrapolation frequency. ill-posed unless one assumes has some additional structure. When compactly supported, then it well-known its can be extended space. However, also this severely ill-conditioned. In work, we assume known belong collection supported functions...
Estimation of cardiovascular model parameters from electronic health records (EHRs) poses a significant challenge primarily due to lack identifiability. Structural non-identifiability arises when manifold in the space is mapped common output, while practical can result limited data, misspecification or noise corruption. To address resulting ill-posed inverse problem, optimization-based Bayesian inference approaches typically use regularization, thereby limiting possibility discovering...
Purpose To decompose the 3D wall shear stress (WSS) vector field into its axial (WSS A ) and circumferential C components using a Laplacian finite element approach. Methods We validated our method with in silico experiments involving different geometries modified Poiseuille flow. computed maps of WSS, WSS , 4D flow MRI data obtained from 10 volunteers patients bicuspid aortic valve (BAV). compared centerline method. The mean value, standard deviation, root mean‐squared error, Wilcoxon signed...
We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by single or few molecular dynamics simulations (MD).
Abstract Nonalcoholic fatty liver disease (NAFLD) is the leading cause of chronic worldwide. Liver biopsy remains gold standard for diagnosis and staging disease. There a clinical need noninvasive diagnostic tools risk stratification, follow‐up, monitoring treatment response that are currently lacking, as well preclinical models recapitulate etiology human condition. We have characterized progression NAFLD in eNOS −/− mice fed high fat diet (HFD) using Dixon‐based magnetic resonance imaging...
Abstract The classic paradigm for MRI requires a homogeneous B 0 field in combination with linear encoding gradients. Distortions are produced when the is not homogeneous, and several postprocessing techniques have been developed to correct them. Field homogeneity difficult achieve, particularly short‐bore magnets higher fields. Nonlinear magnetic components can also arise from concomitant fields, low‐field imaging, or intentionally used nonlinear encoding. In any of these situations,...
Structured illumination microscopies achieve optical sectioning via differential modulation of in-focus and out-of-focus contributions to an image. Multiple wide-field camera images are analyzed recreate section. The requirement for multiple frames per image entails a loss temporal resolution compared conventional imaging. Here we describe computational structured imaging scheme, compressed Hadamard imaging, which achieves simultaneously high spatial 3D samples with low-rank dynamics (e.g....
Advances in solid-state technology have enabled the development of silicon photomultiplier sensor arrays capable sensing individual photons. Combined with high-frequency time-to-digital converters (TDCs), this opens up prospect sensors recording high accuracy both time and location each detected photon. Such a capability could lead to significant improvements imaging accuracy, especially for applications operating low photon fluxes such as light detection ranging positron-emission...
Molecular dynamics (MD) simulation of complex chemistry typically involves thousands atoms propagating over millions time steps, generating a wealth data. Traditionally these data are used to calculate some aggregate properties the system and then discarded, but we propose that can be reused study related chemical systems. Using approximate kinetic models methods from statistical learning, hydrocarbon chemistries under extreme thermodynamic conditions. We discover single MD contain...
Large-scale nonlinear dynamical systems, such as models of atmospheric hydrodynamics, chemical reaction networks, and electronic circuits, often involve thousands or more interacting components. In order to identify key components in the complex system well accelerate simulations, model reduction is desirable. this work, we develop a new data-driven method utilizing ℓ1-regularization for which involves minimal parameterization has polynomial-time complexity, allowing it easily handle...
Non-alcoholic fatty liver disease (NAFLD) is the most common in world and it becoming one of frequent cause transplantation. Unfortunately, only available method that can reliably determine stage this biopsy, however, invasive risky for patients. The purpose study to investigate changes intracellular composition acids during progression NAFLD a mouse model fed with Western diet, aim identify non-invasive biomarkers based 1H-MRS. Our results showed acid as progresses from simple steatosis...
Level set-based algorithms have been extensively used for medical image segmentation. Despite their relative success, standard level set segmentations tend to fail when images are severely corrupted or in poorly defined regions. This problem has tackled incorporating shape prior knowledge, i.e. restricting the evolving curve a distribution of shapes pre-defined during training process. Such restriction needs incorporate invariance translation, rotations and scaling. The common approach this...
Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 21 January 2021Accepted: 11 August 2021Published online: November 2021Keywordscircular seriation, circular Robinson dissimilarities, PQ-trees, Robinsonian matrices, circular-arc hypergraphs, embeddings graphs, generative modelAMS Subject Headings68R01, 05C85, 05C50, 05C25, 65C20Publication DataISSN (online): 2577-0187Publisher: Society for Industrial and Applied...
Nuclear magnetic resonance (NMR) spectroscopy is routinely used to study the properties of matter. Therefore, different materials can be classified according their NMR spectra. However, spectra cannot observed directly, and only signal, which a sum complex exponentials, directly observable in practice. A popular approach recover spectrum perform harmonic retrieval, i.e., reconstruct exactly from signal. even when this fails, might still accurately. In work, we model as an atomic measure...