Advanced Nuclear Magnetic Resonance

Transparencias en inglés de la asignatura "Resonancia Magnética Nuclear Avanzada" (Advanced Nuclear Magnetic Resonance) (36643) que se imparte en el Máster de Química Médica como asignatura optativa de 3 créditos ECTS. En esta asignatura se completa el estudio iniciado en la asignatura de quinto curso de la licenciatura en Química "Determinación estructural" (7448) y en la del Grado de Química de tercer curso "Determinación estructural de los compuestos orgánicos" (26030) en lo referente a técnicas bidimensionales de resonancia magnética nuclear. Además se proporcionan los conocimientos necesarios para poder interpretar RMN de otros núcleos activos en RMN no estudiados hasta el momento como 19F, 31P, 2H, 28Si y 15N así como sus acoplamientos con los núcleos de 1H y 13C.

Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.