No abstract

Given that the contrast of Zn-doped Fe3O4 nanoparticles (NPs) (Zn x Fe3–x O4) in magnetic resonance imaging (MRI) depends on their intrinsic chemical and physical properties such as doping content or size, the ability to finely control these characteristics is very important but at the same time very challenging. In this work, we introduce a novel doping mechanism and present how various desired MRI contrast levels can be precisely achieved by synthesizing Zn x Fe3–x O4 nanoparticles in a controlled and reproducible manner, exhibiting different Zn doping concentrations (Zn x Fe3–x O4, x = 0/0.1/0.2/0.3/0.4) and different dimensions (4/7/10 nm). The experimental results show that Zn x Fe3–x O4 NPs of a specific dimension form a system whose saturation magnetization and crystal structures can be easily tuned by adjusting their Zn doping contents. The proposed model thus enables the exact tuning of MRI contrast by controlling NP doping content and size. The utility of our study is not restricted to the case of the considered material, as it can be easily extrapolated and applied in the case of other divalent transition metal ion-doped magnetic NPs, to optimize their MRI contrast and eventually other relevant properties for further biomedical applications.

No abstract

The market of available contrast agents for clinical magnetic resonance imaging (MRI) has been dominated by gadolinium (Gd) chelates based T1 contrast agents for decades. However, there are growing concerns about their safety because they are retained in the body and are nephrotoxic, which necessitated a warning by the U.S. Food and Drug Administration against the use of such contrast agents. To ameliorate these problems, it is necessary to improve the MRI efficiency of such contrast agents to allow the administration of much reduced dosages. In this study, a ten‐gram‐scale facile method is developed to synthesize organogadolinium complex nanoparticles (i.e., reductive bovine serum albumin stabilized Gd‐salicylate nanoparticles, GdSalNPs‐rBSA) with high r1 value of 19.51 mm−1 s−1 and very low r2/r1 ratio of 1.21 (B0 = 1.5 T) for high‐contrast T1‐weighted MRI of tumors. The GdSalNPs‐rBSA nanoparticles possess more advantages including low synthesis cost (≈0.54 USD per g), long in vivo circulation time (t1/2 = 6.13 h), almost no Gd3+ release, and excellent biosafety. Moreover, the GdSalNPs‐rBSA nanoparticles demonstrate excellent in vivo MRI contrast enhancement (signal‐to‐noise ratio (ΔSNR) ≈ 220%) for tumor diagnosis.

Objective. The study aimed to investigate the predictive classification accuracy of computer semiautomatic segmentation algorithm for the histological grade of breast tumors through the magnetic resonance imaging (MRI) examination. Methods. Five dynamic contrast-enhanced (DCE) MRI regions of interest (ROIs) were captured using computer semiautomatic segmentation method, referring to the entire tumor area, tumor border area, proximal gland area, middle gland area, and distal gland area. According to the mutual information maximum protocol, the corresponding five ROIs were extracted from diffusion weighted imaging (DWI) combined with DCE-MRI images. To use the features in the nonoverlapping area of DWI image and DCE-MRI image as elements, a single-variable logistic regression model was established corresponding to element characteristics. After multiple training, the model was evaluated using the receiver operating characteristic (ROC) curve and area under curve (AUC). Results. This DCE-MRI combined with DWI was superior to DCE-MRI and DW in the prediction of tumor area features. To use DCE-MRI or DWI alone was less effective than DCE-MRI combined with DWI. The DWI combined DCE-MRI demonstrated good regional segmentation effects in the tumour area, with luminal A value being 0.767 and the area under curve (AUC) value being 0.758. After optimization, the AUC value of the tumor area was 0.929, indicating that classification effects can be enhanced by combining the two imaging methods, which complemented each other. Conclusions. The DWI combined DCE-MRI imaging has improved the early diagnosis effects of breast cancer by predicting the occurrence of breast cancer through the labeling of biomarkers.