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Gadolinium oxide (Gd2O3), which can be used as a T1-weighted magnetic resonance imaging (MRI) contrast agent, has attracted intense attention in recent years. In this paper, ligand-free monoclinic Gd2O3 nanocrystals of 7.1 nm in diameter are synthesized by a simple and green approach, namely microsecond laser ablation of a gadolinium (Gd) target in deionized water. These nanocrystals obtain high r1 relaxivity of 5.53 s(-1) mM(-1), and their low toxicity was demonstrated by the cell viability of S18 cells and apoptosis in RAW264.7 cells. In vitro and in vivo MR images show these particles to be good T1-weighted MRI contrast agents. Base on the experimental results and theoretical analysis, we suggest that the purity of the Gd2O3 contributes to its high r1 relaxivity value, while the low toxicity is due to its good crystallinity. These findings show that laser ablation in liquid (LAL) is a promising strategy to synthesize ligand-free monoclinic Gd2O3 nanocrystals for use as high efficient T1-weighted MRI contrast agents.

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The influences of ultrasonic output intensity, solution pH, H2O2 concentration and the addition of Fenton reagent on the degradation of 2,4-dinitrophenol (DNP) under ultrasonic irradiation were investigated. It was observed that the degradation of DNP fitted pseudo-first-order dynamics under our experimental conditions. Increasing the ultrasonic output intensity increased the degradation efficiency of DNP and low pH favored the ultrasonic degradation of DNP. The addition of H2O2 enhanced the ultrasonic degradation efficiency of DNP. The further addition of Cu2+, however, hindered the degradation of DNP. In contrast, sono-oxidation treatment in combination with FeSO4/H2O2 showed a synergistic effect for DNP degradation.

This paper presents a novel two-step approach that incorporates fuzzy c-means (FCMs) clustering and gradient vector flow (GVF) snake algorithm for lesions contour segmentation on breast magnetic resonance imaging (BMRI). Manual delineation of the lesions by expert MR radiologists was taken as a reference standard in evaluating the computerized segmentation approach. The proposed algorithm was also compared with the FCMs clustering based method. With a database of 60 mass-like lesions (22 benign and 38 malignant cases), the proposed method demonstrated sufficiently good segmentation performance. The morphological and texture features were extracted and used to classify the benign and malignant lesions based on the proposed computerized segmentation contour and radiologists' delineation, respectively. Features extracted by the computerized characterization method were employed to differentiate the lesions with an area under the receiver-operating characteristic curve (AUC) of 0.968, in comparison with an AUC of 0.914 based on the features extracted from radiologists' delineation. The proposed method in current study can assist radiologists to delineate and characterize BMRI lesion, such as quantifying morphological and texture features and improving the objectivity and efficiency of BMRI interpretation with a certain clinical value.