Accelerating dual cardiac phase images using undersampled radial phase encoding trajectories

A three-dimensional dual-cardiac-phase (3D-DCP) scan has been proposed to acquire two data sets of the whole heart and great vessels during the end-diastolic and end-systolic cardiac phases in a single free-breathing scan. This method has shown accurate assessment of cardiac anatomy and function but is limited by long acquisition times. This work proposes to accelerate the acquisition and reconstruction of 3D-DCP scans by exploiting redundant information of the outer k-space regions of both cardiac phases. This is achieved using a modified radial-phase-encoding trajectory and gridding reconstruction with uniform coil combination. The end-diastolic acquisition trajectory was angularly shifted with respect to the end-systolic phase. Initially, a fully-sampled 3D-DCP scan was acquired to determine the optimal percentage of the outer k-space data that can be combined between cardiac phases. Thereafter, prospectively undersampled data were reconstructed based on this percentage. As gold standard images, the undersampled data were also reconstructed using iterative SENSE. To validate the method, image quality assessments and a cardiac volume analysis were performed. The proposed method was tested in thirteen healthy volunteers (mean age, 30 years). Prospectively undersampled data (R = 4) reconstructed with 50% combination led high quality images. There were no significant differences in the image quality and in the cardiac volume analysis between our method and iterative SENSE. In addition, the proposed approach reduced the reconstruction time from 40 min to 1 min. In conclusion, the proposed method obtains 3D-DCP scans with an image quality comparable to those reconstructed with iterative SENSE, and within a clinically acceptable reconstruction time.</p

Phyllotaxis transition over the lifespan of a palm tree using Magnetic Resonance Imaging (MRI) and Terrestrial Laser Scanning (TLS) : the case of Jubaea chilensis

Background: Jubaea chilensis (Molina) Baillon, is a uniquely large palm species endemic to Chile. It is under threatened status despite its use as an ornamental species throughout the world. This research seeks to identify the phyllotaxis of the species based on an original combination of non-destructive data acquisition technologies, namely Magnetic Resonance Imaging (MRI) in saplings and young individuals and Terrestrial Laser Scanning (TLS) in standing specimens, and a novel analysis methodology. Results: Two phyllotaxis parameters, parastichy pairs and divergence angle, were determined by analyzing specimens at different developmental stages. Spiral phyllotaxis patterns of J. chilensis progressed in complexity from parastichy pairs (3,2) and (3,5) in juvenile specimens and (5,3), (8,5) and (8,13) for adult specimens. Divergence angle was invariable and averaged 136.9°, close to the golden angle. Phyllotactic pattern changes associated with establishment phase, the adult vegetative and the adult reproductive phases were observed. Both technologies, MRI and TLS proved to be adequate for the proposed analysis. Conclusions: Understanding phyllotactic transitions may assist identification of developmental stages of wild J. chilensis specimens. The proposed methodology may also be useful for the study of other palm species.publishedVersionPeer reviewe

Análisis cuantitativo de variables hemodinámicas de la aorta obtenidas de 4D flow Quantitative analysis of hemodynamic variables of the aorta by 4D flow MRI

Objetivo: Los parámetros hemodinámicos son de gran utilidad para realizar un adecuado diagnóstico. Sin embargo, debido a la gran cantidad de variables que pueden obtenerse, el análisis global de todas ellas puede ser complejo. Para facilitar esta tarea, nosotros proponemos crear un modelo que permita clasificar distintas variables hemodinámicas entre las pertenecientes a un individuo sano o a uno patológico. Para ello, usaremos técnicas de minería de datos que permitan identificar y encontrar relaciones entre distintos parámetros hemodinámicos de la aorta obtenidos a través de flujo multidimensional (4D flow) por resonancia magnética. Método: Una secuencia 4D flow de todo el corazón y los grandes vasos fue adquirida utilizando resonancia magnética en 19 voluntarios sanos y 2 pacientes (uno con una coartación aórtica y otro con una coartación aórtica reparada). Retrospectivamente, los datos fueron reformateados a lo largo de la aorta, originándose 3 cortes en los voluntarios y 30 cortes en cada paciente. En cada corte la aorta fue segmentada y distintos parámetros fueron cuantificados: área, velocidad máxima, velocidad mínima, flujo y volumen, calculándose en los cuatro últimos su valor máximo, promedio, desviación estándar, curtosis, sesgo, proporción de tiempo en alcanzar el valor máximo, entre otros. Teniendo un total de 26 variables por cada corte. Se aplicó la técnica de árboles de decisión tipo CART (por sus siglas en inglés) para clasificar los datos. Para validar el modelo, 2 cortes extras fueron generados por cada voluntario y 20 cortes por cada paciente. Resultados: La técnica CART, mediante la utilización de sólo 7 variables, puede clasificar las imágenes de los voluntarios y pacientes con una tasa de error del 14,1%, una sensibilidad de 82,5% y una especificidad de 89.4%. Conclusiones: 4D flow provee una gran cantidad de datos hemodinámicos que son difíciles de analizar. En este trabajo demostramos que al utilizar minería de datos se pueden clasificar imágenes a partir de parámetros hemodinámicos relevantes y sus relaciones para apoyar el diagnóstico de alteraciones cardiovasculares.Objective: Hemodynamic parameters are critical to perform a proper diagnosis. However, due to the large number of variables that can be obtained, overall analysis may represent a complex task. To facilitate this, we propose to create a model for classifying different hemodynamic variables between those belonging to a healthy individual and to a pathological patient. For this purpose, we employed data mining techniques to identify relationships among various aortic hemodynamic parameters obtained through multi-dimensional (4D flow) MR imaging. Method: A 4D flow sequence of whole heart and great vessels was acquired using MRI in 19 healthy volunteers and 2 patients (one with aortic coarctation and one with repaired coarctation of the aorta). Retrospectively, data were reformatted along the aorta; three MRI acquisitions were performed for volunteers and 30 sequences for each patient. In each slice the aorta was segmented and various parameters were quantified: area, maximum velocity, minimum velocity, flow and volumen, with following values being calculated for last four parameters: maximum, average, standard deviation, kurtosis, skewness, proportion of time to reach the maximum value, among others. A total of 26 variables for each acquisition were obtained. In order to classify data, the CART Technique (Classification and Regression Trees) was applied. To validate the model, two extra projections were generated per each volunteer and 20 slice per each patient. Results: By using only 7 variables, the CART Technique allows discrimination between images performed either on volunteers or patients with an error rate of 14.1%, a sensitivity of 82.5%, and a specificity of 89.4%. Conclusions: 4D flow MR imaging provides a wealth of hemodynamic data that can be difficult to analyze. In this paper we demonstrate that by using data mining techniques it is possible to classify images from relevant hemodynamic parameters and their relationships in order to support the diagnosis of cardiovascular disorders

Medición de presiones relativas en aorta torácica y arteria pulmonar de voluntarios sanos y pacientes con Tetralogía de Fallot reparada utilizando la secuencia 4D Flow de resonancia magnética cardíaca Relative pressure measurement in thoracic aorta and pulmonary artery in healthy volunteers and patients with Tetralogy of Fallot repaired by 4D Flow cardiovascular MRI

Objetivo. Utilizar 4D Flow y las ecuaciones de Navier-Stokes para obtener mapas de presiones relativas (PR) en la Aorta y Arteria Pulmonar (AP) de voluntarios y pacientes con Tetralogía de Fallot reparada (TOFr). Métodos. En 10 voluntarios y 6 pacientes con TOFr se adquirió la secuencia 4D flow del corazón y sus principales vasos. La raíz de la Aorta Ascendente se utilizó como referencia para calcular las PR a esta zona en cinco puntos distintos. Además, se midió la PR de la AP derecha e izquierda respecto a la AP. Resultados. Los pacientes con TOFr tuvieron diferencias de PR entre los valores máximos y mínimos más grandes que los voluntarios en la AP (p<0,05). Adicionalmente, las PR de la aorta tuvieron una excelente correlación con datos publicados utilizando 4D flow y mediante cateterización. Conclusiones. 4D Flow podría constituir una nueva herramienta diagnóstica, no invasiva, ni operador dependiente, en el manejo de patologías CV.<br>Objective. To validate the utility of 4D Blood Flow and Navier-Stokes equations to create relative pressure (RP) maps in the aorta and pulmonary artery (PA) in healthy volunteers and patients with repaired tetralogy of Fallot (TOF). Methods. A 4D flow sequence of whole heart and its major vessels was acquired in 10 healthy volunteers and 6 patients with repaired TOF. The root of the ascending aorta was used as the reference point to calculate RP along five different points of this area. In addition, relative pressure of both right and left PA was measured as correlated to absolute pressure. Results. Patients with repaired TOF showed greater pulmonary artery (PA) relative pressure differences between maximum and minimum values when compared to volunteers (p <0.05). Additionally, aortic relative pressures had an excellent correlation with published data, whether using 4D flow or by catheterization. Conclusions. 4D Flow MRI may represent a new non-invasive and non operator-dependent diagnostic tool in CV disease management

Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data

Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients. To this end, we acquired magnetic resonance imaging data from relapsing-remitting multiple sclerosis patients and healthy subjects. Fractional anisotropy maps, structural and functional connectivity were extracted from the scans. Each of them were used as separate input features to construct support vector machine classifiers. A fourth input feature was created by combining structural and functional connectivity. Patients were divided in two groups according to their degree of disability and, together with the control group, three group pairs were formed for comparison. Twelve separate classifiers were built from the combination of these four input features and three group pairs. The classifiers were able to distinguish between patients and healthy subjects, reaching accuracy levels as high as 89% ± 2%. In contrast, the performance was noticeably lower when comparing the two groups of patients with different levels of disability, reaching levels below 63% ± 5%. The brain regions that contributed the most to the classification were the right occipital, left frontal orbital, medial frontal cortices and lingual gyrus. The developed classifiers based on MRI data were able to distinguish multiple sclerosis patients and healthy subjects reliably. Moreover, the resulting classification models identified brain regions, and functional and structural connections relevant for better understanding of the disease. Keywords: Resting state, fMRI, DTI, SVM, Multiple sclerosis, Classificatio