RESUMEN
Corpus callosum (CC) is the main tract connecting the hemispheres, but the clinical significance of CC atrophy is poorly understood. The aim of this work was to investigate clinical and functional correlates of CC atrophy in subjects with age-related white matter changes (ARWMC). In 569 elderly subjects with ARWMC from the Leukoaraiosis And DISability (LADIS) study, the CC was segmented on the normalised mid-sagittal magnetic resonance imaging (MRI) slice and subdivided into five regions. Correlations between the CC areas and subjective memory complaints, mini mental state examination (MMSE) score, history of depression, geriatric depression scale (GDS) score, subjective gait difficulty, history of falls, walking speed, and total score on the short physical performance battery (SPPB) were analyzed. Significant correlations between CC atrophy and MMSE, SPPB, and walking speed were identified, and the CC areas were smaller in subjects with subjective gait difficulty. The correlations remained significant after correction for ARWMC grade. In conclusion, CC atrophy was independently associated with impaired global cognitive and motor function in subjects with ARWMC.
Asunto(s)
Envejecimiento/patología , Cuerpo Calloso/patología , Cuerpo Calloso/fisiopatología , Factores de Edad , Anciano , Anciano de 80 o más Años , Atrofia , Trastornos del Conocimiento/etiología , Trastornos del Conocimiento/patología , Estudios Transversales , Depresión/etiología , Personas con Discapacidad , Femenino , Marcha/fisiología , Humanos , Leucoaraiosis/patología , Estudios Longitudinales , Imagen por Resonancia Magnética/métodos , Masculino , Persona de Mediana Edad , Pruebas Neuropsicológicas , Desempeño Psicomotor/fisiología , Factores Sexuales , Tomógrafos Computarizados por Rayos XRESUMEN
This paper presents a novel method for registration of single and multi-slice cardiac perfusion MRI. Utilising off-line computer intensive analyses of variance and clustering in an annotated training set, the presented method is capable of providing registration without any manual interaction in less than a second per frame. Changes in image intensity during the bolus passage are modelled by a slice-coupled active appearance model, which is augmented with a cluster analysis of the training set. Landmark correspondences are optimised using the MDL framework due to Davies et al. Image search is verified and stabilised using perfusion specific prior models of pose and shape estimated from training data. Qualitative and quantitative validation of the method is carried out using 2000 clinical quality, short-axis, perfusion MR slice images, acquired from 10 freely breathing patients with acute myocardial infarction. Despite evident perfusion deficits and varying image quality in the limited training set, a leave-one-out cross-validation of the method showed a mean point to curve distance of 1.25+/-0.36 pixels for the left and right ventricle combined. We conclude that this learning-based method holds great promise for the automation of cardiac perfusion investigations, due to its accuracy, robustness and generalisation ability.