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When studying water diffusion in biological systems, any specific signal attenuation curve may be reproduced by a broad range of mathematical functions. Our goals were to quantify the diffusion and T(2) relaxation properties of water in a simple biological system and to study the changes that occur in osmotically stressed cells. Human breast cancer cells were incubated in isotonic or hypotonic osmotic buffers. Diffusion-weighted and T(2)-weighted magnetic resonance images were acquired during sedimentation over 12 h. Diffusion-weighted imaging (DWI) data were analyzed with a biexponential fit, the Kärger model for exchange between two freely diffusing populations and the Price-modified Kärger model accounting for restricted diffusion in spherical geometry. We found that only the Price model provided an accurate quantitative description for water diffusion in both cell systems, independent of acquisition parameters, over the entire density range. Model-derived cell radii, intracellular volume fractions and transmembrane water exchange times were in good agreement with results calculated from light microscopy and with model-free exchange times. T(2) data indicated two populations in fast exchange, with volume fractions clearly different from DWI populations. Hypotonic stress led to higher slow apparent diffusion coefficient, longer T(2) and lower membrane permeability. The tortuosity in a hypotonic cell suspension complied with the Wang model for spherical geometry. Quantitative characterization of biological systems is obtainable by DWI, using appropriate modeling, accounting for water restriction and exchange between compartments.

An accurate computer-assisted method able to perform regional segmentation on 3D single modality images and measure its volume is designed using a mixture of unsupervised and supervised artificial neural networks. Firstly, an unsupervised artificial neural network is used to estimate representative textures that appear in the images. The region of interest of the resultant images is selected by means of a multi-layer perceptron after a training using a single sample slice, which contains a central portion of the 3D region of interest. The method was applied to magnetic resonance imaging data collected from an experimental acute inflammatory model (T(2) weighted) and from a clinical study of human Alzheimer's disease (T(1) weighted) to evaluate the proposed method. In the first case, a high correlation and parallelism was registered between the volumetric measurements, of the injured and healthy tissue, by the proposed method with respect to the manual measurements (r = 0.82 and p < 0.05) and to the histopathological studies (r = 0.87 and p < 0.05). The method was also applied to the clinical studies, and similar results were derived of the manual and semi-automatic volumetric measurement of both hippocampus and the corpus callosum (0.95 and 0.88).