Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes

In diffusion MRI (dMRI), a good sampling scheme is important for efficient acquisition and robust reconstruction. Diffusion weighted signal is normally acquired on single or multiple shells in q-space. Signal samples are typically distributed uniformly on different shells to make them invariant to the orientation of structures within tissue, or the laboratory coordinate frame. The Electrostatic Energy Minimization (EEM) method, originally proposed for single shell sampling scheme in dMRI, was recently generalized to multi-shell schemes, called Generalized EEM (GEEM). GEEM has been successfully used in the Human Connectome Project (HCP). However, EEM does not directly address the goal of optimal sampling, i.e., achieving large angular separation between sampling points. In this paper, we propose a more natural formulation, called Spherical Code (SC), to directly maximize the minimal angle between different samples in single or multiple shells. We consider not only continuous problems to design single or multiple shell sampling schemes, but also discrete problems to uniformly extract sub-sampled schemes from an existing single or multiple shell scheme, and to order samples in an existing scheme. We propose five algorithms to solve the above problems, including an incremental SC (ISC), a sophisticated greedy algorithm called Iterative Maximum Overlap Construction (IMOC), an 1-Opt greedy method, a Mixed Integer Linear Programming (MILP) method, and a Constrained Non-Linear Optimization (CNLO) method. To our knowledge, this is the first work to use the SC formulation for single or multiple shell sampling schemes in dMRI. Experimental results indicate that SC methods obtain larger angular separation and better rotational invariance than the state-of-the-art EEM and GEEM. The related codes and a tutorial have been released in DMRITool.Comment: Accepted by IEEE transactions on Medical Imaging. Codes have been released in dmritool https://diffusionmritool.github.io/tutorial_qspacesampling.htm

Novel Single and Multiple Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes

International audienceA good data sampling scheme is important for diffusion MRI acquisition and reconstruction. Diffusion Weighted Imaging (DWI) data is normally acquired on single or multiple shells in q-space. The samples in different shells are typically distributed uniformly, because they should be invariant to the orientation of structures within tissue, or the laboratory coordinate frame. The Electrostatic Energy Minimization (EEM) method, originally proposed for single shell sampling scheme in dMRI by Jones et al., was recently generalized to the multi-shell case, called generalized EEM (GEEM). GEEM has been successfully used in the Human Connectome Project (HCP). Recently, the Spherical Code (SC) concept was proposed to maximize the minimal angle between different samples in single or multiple shells, producing a larger angular separation and better rotational invariance than the GEEM method. In this paper, we propose two novel algorithms based on the SC concept: 1) an efficient incremental constructive method, called Iterative Maximum Overlap Construction (IMOC), to generate a sampling scheme on a discretized sphere; 2) a constrained non-linear optimization (CNLO) method to update a given initial scheme on the continuous sphere. Compared to existing incremental estimation methods, IMOC obtains schemes with much larger separation angles between samples, which are very close to the best known solutions in single shell case. Compared to the existing Riemannian gradient descent method, CNLO is more robust and stable. Experiments demonstrated that the two proposed methods provide larger separation angles and better rotational invariance than the state-of-the-art GEEM and methods based on the SC concept

Análisis del estudio de la migraña con resonancia magnética mediante medidas avanzadas y técnicas de inteligencia artificial

La resonancia magnética de difusión forma parte de las modalidades de imagen médica más útil tanto en el pronóstico como diagnóstico de las patologías neurológicas más complejas actualmente en el mundo de la medicina. Constituye también un elemento fundamental en el tratamiento de estas enfermedades, gracias a su capacidad de visualización de las fibras nerviosas que componen la sustancia blanca del cerebro. Sin embargo, uno de los principales desafíos que posee la resonancia magnética de difusión es que necesita una gran cantidad de datos para la creación de modelos complejos de procesado de imágenes que interpreten y representen la información microestructural cerebral necesaria para la caracterización de la sustancia blanca. Actualmente, el procesado de imágenes de difusión más empleado es el modelo tensorial, basado en un modelo Gaussiano que proporciona los descriptores más comunes para llevar a cabo estudios estadísticos en la comparación de grupos de interés en patologías neurológicas. En este Trabajo de Fin de Grado se hace una exhaustiva investigación sobre dos corrientes muy concretas con el objetivo de resolver el reto de conseguir un mayor número de datos de difusión reduciendo el tiempo de escáner y mejorando la práctica clínica: un modelo alternativo al tensorial denominado AMURA; y un método de inteligencia artificial basado en la interpolación espacial para la generación de nuevos datos. A través de estas dos vertientes, este trabajo busca mejorar la capacidad de encontrar diferencias significativas en enfermedades con diagnóstico y tratamiento con gran incertidumbre, como es la migraña. Para conseguir el reto se usa una base de datos de 100 pacientes, divididos en 50 crónicos y 50 episódicos. El conjunto de datos utilizado en este estudio consta de tres tipos diferentes: datos de 61 direcciones de gradiente; datos de 21 direcciones de gradiente; y datos de 61 direcciones de gradiente sintéticas gracias al método de inteligencia artificial mencionado. Sobre estos datos se realiza un análisis por ROIs en 48 regiones cerebrales, identificadas mediante un atlas específico. En este análisis se emplea un ANOVA sobre el cálculo realizado previamente con los descriptores DTI (FA, MD, AD) y AMURA (RTOP, RTAP, RTPP, APA, qMSD). Finalmente se evalúa, a través de dos grandes tablas de resultados, la capacidad de estas dos corrientes analizadas para aumentar los datos de difusión de forma eficiente y su competencia en términos estadísticos para discernir diferencias entre pacientes crónicos y episódicos, en comparación con el modelo tensorial.Grado en Ingeniería Biomédic

Improved Quantification of Connectivity in Human Brain Mapping

Diffusion magnetic resonance imaging (dMRI) is an advanced MRI methodology that can be used to probe the microstructure of biological tissue. dMRI can provide orientation information by modeling the process of water diffusion in white matter. This thesis presents contributions in three areas of diffusion imaging technology: diffusion reconstruction, quantification, and validation of derived metrics. It presents a novel reconstruction method by combining generalized q-sampling imaging, spherical harmonic basis functions and constrained spherical deconvolution methods to estimate the fiber orientation distribution function (ODF). This method provides improved spatial localization of brain nuclei and fiber tract separation. A novel diffusion anisotropy metric is presented that provides anatomically interpretable measurements of tracts that are robust in crossing areas of the brain. The metric, directional Axonal Volume (dAV) provides an estimate of directional water content of the tract based on the (ODF) and proton density map. dAV is a directionally sensitive metric and can separate anisotropic water content for each fiber population, providing a quantification in milliliters of water. A method is provided to map voxel-based dAV onto tracts that is not confounded by crossing areas and follows the tract morphology. This work introduces a novel textile based hollow fiber anisotropic phantom (TABIP) for validation of reconstruction and quantification methods. This provides a ground truth reference for axonal scale water tubular structures arranged in various anatomical configurations, crossing and mixing patterns. Analysis shows that: 1) the textile tracts are identifiable with scans used in human imaging and produced tracts and voxel metrics in the range of human tissue; 2) the current methods could resolve crossing at 90o and 45o but not 30o; 3) dAV/NODDI model closely matches (r=0.95) the number of fibers whereas conventional metrics poorly match (i.e., FA r=0.32). This work represents a new accurate quantification of axonal water content through diffusion imaging. dAV shows promise as a new anatomically interpretable metric of axonal connectivity that is not confounded by factors such as axon dispersion, crossing and local isotropic water content. This will provide better anatomical mapping of white matter and potentially improve the detection of axonal tract pathology