Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x-q Space

Diffusion MRI requires sufficient coverage of the diffusion wavevector space, also known as the q-space, to adequately capture the pattern of water diffusion in various directions and scales. As a result, the acquisition time can be prohibitive for individuals who are unable to stay still in the scanner for an extensive period of time, such as infants. To address this problem, in this paper we harness non-local self-similar information in the x-q space of diffusion MRI data for q-space upsampling. Specifically, we first perform neighborhood matching to establish the relationships of signals in x-q space. The signal relationships are then used to regularize an ill-posed inverse problem related to the estimation of high angular resolution diffusion MRI data from its low-resolution counterpart. Our framework allows information from curved white matter structures to be used for effective regularization of the otherwise ill-posed problem. Extensive evaluations using synthetic and infant diffusion MRI data demonstrate the effectiveness of our method. Compared with the widely adopted interpolation methods using spherical radial basis functions and spherical harmonics, our method is able to produce high angular resolution diffusion MRI data with greater quality, both qualitatively and quantitatively.Comment: 15 pages, 12 figure

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

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