Santiago Coelho

Santiago Coelho
New York University | NYU · School of Medicine

PhD Biomedical Engineering

About

23
Publications
1,614
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178
Citations
Citations since 2017
23 Research Items
177 Citations
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Introduction
My main research area is Diffusion MRI (dMRI), where my focus is on biophysical tissue models for brain white matter. Currently, I am investigating the advantages of the use of novel diffusion MRI encoding and modeling schemes to improve the specificity of dMRI measurements to features of tissue microstructure.

Publications

Publications (23)
Article
Full-text available
Purpose Biophysical tissue models are increasingly used in the interpretation of diffusion MRI (dMRI) data, with the potential to provide specific biomarkers of brain microstructural changes. However, it has been shown recently that, in the general Standard Model, parameter estimation from dMRI data is ill‐conditioned even when very high b‐values a...
Article
Full-text available
Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra...
Preprint
Full-text available
Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra...
Conference Paper
Full-text available
In the machine-learning (ML) era, we are transitioning from max-likelihood parameter estimation to learning the mapping from measurements to model parameters. While such maps look smooth, there is danger of them becoming too smooth: At low SNR, ML estimates become the mean of the training set. Here we derive fit quality (MSE) as function of SNR, an...
Conference Paper
Full-text available
Robust parameter estimation of the Standard Model (SM) for diffusion in white matter has been elusive due to intrinsic model degeneracies and insufficient measurements. To design optimal scanner-specific protocols, we couple multidimensional protocol optimization for estimation of microstructural tissue properties in 15-minute acquisitions on clini...
Article
Full-text available
Background Tissue microarrays (TMAs), where each block (and thus section) contains multiple tissue cores from multiple blocks potentially allow more efficient use of tissue, reagents and time in neuropathology. New method The relationship between data from TMA cores and whole sections was investigated using ‘virtual’ TMA cores. This involved quant...
Chapter
Computational models of biophysical tissue properties have been widely used in diffusion MRI (dMRI) research to elucidate the link between microstructural properties and MR signal formation. For brain tissue, the research community has developed the so-called Standard Model (SM) that has been widely used. However, in clinically applicable acquisiti...
Preprint
Full-text available
Computational models of biophysical tissue properties have been widely used in diffusion MRI (dMRI) research to elucidate the link between microstructural properties and MR signal formation. For brain tissue, the research community has developed the so-called Standard Model (SM) that has been widely used. However, in clinically applicable acquisiti...
Conference Paper
Full-text available
Computational models of biophysical tissue properties have been widely used in diffusion MRI (dMRI) research to elucidate the link between microstructural properties and MR signal formation. For brain tissue, the research community has developed the so-called Standard Model (SM) that has been widely used. However, in clinically applicable acquisiti...
Article
Full-text available
Purpose Information on the brain microstructure can be probed by Diffusion Magnetic Resonance Imaging (dMRI). Neurite Orientation Dispersion and Density Imaging with Diffusivities Assessment (NODDIDA) is one of the simplest microstructural model proposed. However, the estimation of the NODDIDA parameters from clinically plausible dMRI acquisition i...
Article
Full-text available
White matter lesions represent a major risk factor for dementia in elderly people. Magnetic Resonance Imaging (MRI) studies have demonstrated cerebral blood flow reduction in age-related white matter lesions, indicating that vascular alterations are involved in developing white matter lesions. Hypoperfusion and changes in capillary morphology are g...
Article
Full-text available
This immunohistochemistry dataset contains the main structures in deep subcortical white matter (axons, astrocytes, and myelinated axons) in a representative cohort of an ageing population. A set of samples from 90 subjects of the Cognitive Function and Ageing Study (CFAS) were analysed, stratified into three groups of 30 subjects each, in relation...
Article
Full-text available
Deep subcortical lesions (DSCL) of the brain, are present in ~60% of the ageing population, and are linked to cognitive decline and depression. DSCL are associated with demyelination, blood brain barrier (BBB) dysfunction, and microgliosis. Microglia are the main immune cell of the brain. Under physiological conditions microglia have a ramified mor...
Data
Visiopharm vs MATLAB analysis. The ROI in each case was identified based on the extent of microglial activation (Iba-1 reactivity and/or CD68 reactivity). The ROI identified in a NAWM case using Iba-1 immunoreactivity in visiopharm (A). The circular ROI was depicted in the equivalent MATLAB processed image. The ROI identified from the central point...
Data
Visiopharm analysis. An example image uploaded into visiopharm (A) and the main region of interest (ROI) drawn onto the section (B, green box). The overall mean area of immunostaining was calculated across the 5 sub ROIs within the main ROI (C). (TIF)
Data
Comparison of CD68 and Iba-1 immunoreactivity. The extent of Iba-1 and CD68 immunoreactivity across areas of control, NAWM and DSCL (A). Iba-1 and CD68 microglia M-score across areas of control, NAWM and DSCL, the higher the M-score the more larger and rounder the cell (B). Error bars indicate standard deviation (SD) of the mean. NAWM: normal appea...
Data
Microglial double labelling. CD68+ (A, red fluorescent label) and Iba-1 detected with 3,3’-diaminobenzidine (DAB) visualised under light microscopy (B, brown). Double labelling confirms colocalisation of CD68 and Iba-1 double-labelled cells (C, green arrows) but also shows a distinct population of Iba-1- cells (B, black arrows) that are CD68+ (C, w...
Data
Semi-quantitative analysis of fibrinogen. The parenchyma fibrinogen immunoreactivity was graded as G0; absent parenchyma immunoreactivity, G1; perivascular immunoreactivity only, G2L or G2I (Less intense and Intense immunoreactivity); throughout the all parenchyma. Scale bar = 1mm (A). The pattern of fibrinogen immunoreactivity in the region of int...
Data
Minimal data set. Visiopharm and MATLAB generated data (MHCII, CD68, Iba-1, AQP4, Fibrinogen and PLP). (XLSX)
Preprint
Purpose: Biophysical tissue models are increasingly used in the interpretation of diffusion MRI (dMRI) data, with the potential to provide specific biomarkers of brain microstructural changes. However, the general Standard Model has recently shown that model parameter estimation from dMRI data is ill-posed unless very strong magnetic gradients are...
Article
Full-text available
This study aims to statistically describe histologically stained white matter brain sections to subsequently inform and validate diffusion MRI techniques. For the first time, we characterise volume fraction distributions of three of the main structures in deep subcortical white matter (axons, astrocytes, and myelinated axons) in a representative co...
Conference Paper
Biophysical tissue models are a solid tool for obtaining specific biomarkers with diffusion MRI. However, the assumptions they rely on are sometimes inaccurate and may lead to erroneous results. Some limitations of the Neurite Orientation Dispersion and Density Imaging (NODDI) model are tackled by NODDIDA (NODDI with Diffusivities Added), at the co...

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