The purpose of the current study was to evaluate the performance of a continuous‐time random‐walk (CTRW) diffusion model for differentiating malignant and benign breast lesions and to consider the potential association between CTRW parameters and the Ki‐67 expression. Sixty‐four patients (46.2 ± 11.4 years) with breast lesions (29 malignant and 35 benign) were evaluated with the CTRW model, intravoxel incoherent motion model, and diffusion‐weighted imaging. Echo planar diffusion‐weighted imaging was conducted using 13 b‐values (0‐3000 s/mm2). Three CTRW model parameters, including an anomalous diffusion coefficient Dm, and two parameters related to temporal and spatial diffusion heterogeneity, α and β, respectively, were obtained, and had MRI b‐values of 0–3000 s/mm2. Receiver operating characteristic (ROC) analysis was conducted to determine the sensitivity, specificity, and diagnostic accuracy of CTRW parameters for differentiating malignant from benign breast lesions. In malignant breast lesions, the CTRW parameters Dm, α, and β were significantly lower than the corresponding parameters of benign breast lesions. In the malignant breast lesion group, the CTRW parameter Dm was significantly lower in high Ki‐67 expression than in low Ki‐67 expression. In ROC analysis, the combination of CTRW parameters (Dm, α, β) demonstrated the highest area under the curve value (0.985) and diagnostic accuracy (94.23%) in differentiating malignant and benign breast lesions. The CTRW model effectively differentiated malignant from benign breast lesions. The CTRW diffusion model offers a new way for noninvasive assessment of breast malignancy and better understanding of the proliferation of malignant lesions.

Background Regular monitoring of static lacunar infarction (SLI) lesions plays an important role in preventing disease development and managing prognosis. Magnetic resonance imaging is one method used to monitor SLI lesions. Purpose To evaluate the image quality of the T2 fluid-attenuated inversion recovery (T2-FLAIR) sequence using artificial intelligence-assisted compressed sensing (ACS) in detecting SLI lesions and assess its clinical applicability. Methods A total of 42 patients were prospectively enrolled and scanned by T2-FLAIR. Two independent readers reviewed the images acquired with accelerated modes 1D (acceleration factor 2) and ACS (acceleration factors 2, 3, and 4). The overall image quality and lesion image quality were analyzed, as were signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and number of lesions between groups. Results The subjective assessment of overall brain image quality and lesion image quality was consistent between the two readers. The lesion display quality and the overall image quality were better with the traditional 1D acceleration method than with the ACS accelerated method. There was no significant difference in the SNR of the lacunar infarction in the images between the groups. The CNR of the images with the 1D acceleration mode was significantly lower than that of images with the ACS acceleration mode. Images with the 1D, ACS2, and ACS3 acceleration modes showed no significant differences in terms of detecting lesions but scan time can be reduced by 40% (1D vs. ACS3). Conclusion ACS acceleration mode can greatly reduce the scan time. In addition, the images have good SNR, high CNR, and strong SLI lesion detection ability.

Magnetic resonance imaging (MRI) is an important diagnostic method for the breast cancer. However, for conventional MRI techniques such as diffusion-weighted imaging (DWI), it ignores the non-Gaussian behaviors of the water diffusion caused by the restriction of tissue microstructure, thus the conventional method is insensitive to microstructural and heterogeneity changes in breast tumor. This study explores the feasibility of applying a continuous-time random-walk (CTRW) non-Gaussian diffusion model to the differentiation and assessment of malignant and benign breast tumors. The correlations between CTRW parameters and breast tumor immunohistochemical features such as oestrogen receptor (ER), progesterone receptor (PR) and Ki67 expression status are studied. The CTRW model has potential in future applications of breast tumor malignancy and therapeutic efficacy evaluation.