Academic literature on the topic 'Water/fat imaging'

Magnetic resonance imaging (MRI) is an important medical imaging technique for visualizing soft tissue structures in the body. It has the advantages of being noninvasive and, unlike x-ray, does not rely on ionizing radiation for imaging. In traditional hydrogen-based MRI, the strongest measured signals are generated from the hydrogen nuclei contained in water and fat molecules.Reliable and uniform water fat separation can be used to improve medical diagnosis. In many applications the water component is the primary signal of interest, while the fat component represents a signal which can obscure the underlying pathology or other features of interest. In other applications the fat signal is the signal of interest. There currently exist many techniques for water fat separation. Dixon reconstruction techniques take multiple images acquired at select echo times with specific phase properties. Linear combinations of these images produce separate water and fat images. In MR imaging, images with high signal-to-noise ratio (SNR), that can be generated in a short time, are desired. Balanced steady-state free precession (bSSFP) MRI is a technique capable of producing images with high SNR in a short imaging time but suffers from signal voids or banding artifacts due to magnetic field inhomogeneity and susceptibly variations. These signal voids degrade image quality. Several methods have been developed to remove these banding effects. The simplest methods combine images across multiple bSSFP image acquisitions. This thesis describes a technique in water fat separation I developed which combines the advantages of bSSFP with Dixon reconstruction in order to produce robust water fat decomposition with high SNR in a short imaging time, while simultaneously reducing banding artifacts which traditionally degrade image quality. This algorithm utilizes four phased-cycled bSSFP acquisitions at specific echo times. Phase sensitive post-processing and a field map are used to prepare the data and reduce the effects of field inhomogeneities. Dixon reconstruction is then used to generate separate water and fat images.

L'obésité est associée à une augmentation de la morbidité et de la mortalité liée à de nombreuses maladies, y compris le diabète de type 2, l'hypertension et des pathologies hépatiques menant à une surcharge lipidique d’origine non alcoolique. Récemment, l’imagerie par résonance magnétique (IRM) est devenue la méthode de choix pour la quantification non invasive de la graisse. Dans cette thèse, les méthodes d'IRM ont été étudiées sur un scanner préclinique de 4.7T in vitro (fantômes MR) et in vivo (souris). Deux algorithmes de quantifications de la graisse -la méthode de Dixon et l’algorithme IDEAL- ont été considérés. Les performances de l'algorithme IDEAL ont été analysées en fonction de propriétés des tissus (T2*, fraction de graisse et modèle spectral de la graisse), de paramètres d'acquisition IRM (temps d’écho, nombre d'échos) et de paramètres expérimentaux (SNR et carte de champ). Sur les fantômes, l'approche standard single-T2* IDEAL a montré certaines limites qui pourraient être surmontées en optimisant le nombre d'échos. Une nouvelle méthode, pour déterminer les valeurs de vérité terrain pour T2* de l'eau et pour T2* de la graisse, a été proposée. Pour les mesures in vivo, différentes analyses ont été effectuées en utilisant l'algorithme IDEAL sur le foie et les muscles. L'analyse statistique sur les mesures de ROI a montré que le choix optimal du nombre d'échos est égal à trois pour la quantification de la graisse et six ou plus pour la quantification du T2*. Les valeurs de la fraction de graisse, calculées avec l'algorithme IDEAL, étaient statistiquement comparables aux valeurs obtenues avec la méthode de Dixon. Enfin, un procédé pour générer des signaux de référence mimant les systèmes eau-graisse (Fat Virtual Phantom MRI), sans l'aide d'objets physiques, a été proposé. Ces fantômes virtuels, qui présentent des caractéristiques de bruit réalistes, représentent une alternative intéressante aux fantômes physiques pour fournir un signal de référence dans les mesures IRM<br>Obesity is associated with increased morbidity and mortality linked to many diseases, including type 2 diabetes, hypertension and disease nonalcoholic fatty liver. Recently, 1H magnetic resonance imaging (MRI) has emerged as the method of choice for non-invasive fat quantification. In this thesis, MRI methodologies were investigated for in vitro (MR phantoms) and in vivo (mice) measurements on a 4.7T preclinical scanner. Two algorithms of fat quantifications – the Dixon’s method and IDEAL algorithm – were considered. The performances of the IDEAL algorithm were analyzed as a function of tissue properties (T2*, fat fraction and fat spectral model), MRI acquisition parameters (echo times, number of echoes) and experimental parameters (SNR and field map). In phantoms, the standard approach of single-T2* IDEAL showed some limitations that could be overcome by optimizing the number of echoes. A novel method to determine the ground truth values of T2* of water and T2* of fat was here proposed. For in vivo measurements, different analyses were performed using the IDEAL algorithm in liver and muscle. Statistical analysis on ROI measurements showed that the optimal choice of the number of echoes was equal to three for fat quantification and six or more for T2* quantification. The fat fraction values, calculated with IDEAL algorithm, were statistically similar to the values obtained with Dixon’s method. Finally, a method for generating reference signals mimicking fat-water systems (Fat Virtual Phantom MRI), without using physical objects, was proposed. These virtual phantoms, which display realistic noise characteristics, represent an attractive alternative to physical phantoms for providing a reference signal in MRI measurements

A major health concern for subjects with diabetes is weaker bones and increased fracture risk. Current clinical assessment of the bone strength is performed by measuring Bone Mineral Density (BMD), where low BMD-values are associated with an increased risk of fracture. However, subjects with Type 2 Diabetes (T2D) have been shown to have normal or higher BMD-levels compared to healthy controls, which does not reflect the recognized bone fragility among diabetics. Thus, there is need for more research about diabetes-related bone fragility to find other factors of impaired bone health. One potential biomarker that has recently been studied is Bone Marrow Fat (BMF). The data in this project consisted of whole-body water-fat Magnetic Resonance Imaging (MRI) volumes from the UK Biobank Imaging study (UKBB). Each subject in this data has a water volume and a fat volume, allowing for a quantitative assessment of water and fat content in the body. To analyze and perform quantitative measurements of the bones specifically, a Deep Learning (DL) model was trained, validated, and tested for performing fully automated and objective skeleton segmentation, where six different bones were segmented: spine, femur, pelvis, scapula, clavicle and humerus. The model was trained and validated on 120 subjects with 6-fold cross-validation and tested on eight subjects. All ground-truth segmentations of the training and test data were generated using two semi-automatic pipelines. The model was evaluated for each bone separately as well as the overall skeleton segmentation and achieved varying accuracy, performing better on larger bones than on smaller ones. The final trained model was applied on a larger dataset of 9562 subjects (16% type 2 diabetics) and the BMF, as well as bone marrow volume (BMV) and cortical bone volume (CBV), were measured in the segmented bones of each subject. The results of the quantified biomarkers were compared between T2D and healthy subjects. The comparison revealed possible differences between healthy and diabetic subjects, suggesting a potential for new findings related to diabetes and associated bone fragility.