Clusters of iron oxide nanoparticles encapsulated in a pH-responsive hydrogel are synthesized and studied for their ability to alter the T 2 -relaxivity of protons. Encapsulation of the clusters with the hydrophilic coating is shown to enhance the transverse relaxation rate by up to 85% compared to clusters with no coating. With the use of pH-sensitive hydrogel, difficulties inherent in comparing particle samples are eliminated and a clear increase in relaxivity as the coating swells is demonstrated. Agreement with Monte Carlo simulations indicates that the lower diffusivity of water inside the coating and near the particle surface leads to the enhancement. This demonstration of a surface-active particle structure opens new possibilities in using similar structures for nanoparticle-based diagnostics using magnetic resonance imaging.

In this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated with the structural and biochemical changes of tissue collagen networks. Based on these extracted quantitative parameters, multi-group classification of SHG images was performed. With combined FOS and GLCM texture values, we achieved reliable classification of SHG collagen images acquired from atherosclerosis arteries with >90% accuracy, sensitivity and specificity. The proposed methodology can be applied to a wide range of conditions involving collagen re-modeling, such as in skin disorders, different types of fibrosis and muscular-skeletal diseases affecting ligaments and cartilage.