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Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and three-dimensional arterial spin labeling (3D-ASL) have been applied to brain tumors; however, the relationship between their parameters and the Ki-67 labeling index (Ki-67 LI) for the grading of gliomas have yet to be investigated. The aim of this study is to compare multiple parameters obtained from IVIM-DWI and 3D-ASL with the Ki-67 LI when grading gliomas. Fifty-two patients with pathologically confirmed gliomas had undergone magnetic resonance imaging (MRI), including IVIM-DWI and 3D-ASL imaging. Mann–Whitney U tests were conducted and receiver operating characteristic (ROC) curves were generated to determine parameters for distinguishing high-grade gliomas (HGGs) from low-grade gliomas (LGGs). These parameters included the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo diffusivity (D*), perfusion fraction (f), cerebral blood flow (CBF), and their relative values (rADC, rD, rD*, rf, and rCBF). Spearman correlation analysis was used to assess the correlations of the parameters of MRI with the Ki-67 LI. The rADC, rD, and rf were significantly lower in HGGs than in LGGs (P < 0.005 for all). The rD had a significantly greater area under the ROC curve than that of the other parameters in the differentiation of HGGs from LGGs (P < 0.05). Both the rD and rf were moderately negatively correlated with the Ki-67 LI. Both the rD and rf can be used for the quantitative prediction of the Ki-67 LI. Among the extracted parameters, the rD had the significantly greatest diagnostic efficacy.

Background: Single-shot echo planar imaging (SS-EPI) diffusion-weighted magnetic resonance imaging (DW-MRI) is the most-widely sequence in breast MRI. MRI artifacts and magnetic susceptibility are sometimes severe when this sequence is utilized at 3T. Purpose: To compare the imaging quality, ADC values between SS-EPI DWI sequence and two reduced field-of-view (rFOV) DWI sequences of breast cancer. Material and Methods: Twelve cases with breast cancer were scanned using SS-EPI DWI (FOV, 360 × 360 mm), rFOV DWI1 (FOV, 360 × 180 mm), and rFOV DWI2 (FOV, 280 × 140 mm), respectively. Image quality (scores 1 to 5) and ADC values of breast imaging were compared among three groups by different DWI sequences. SNR were compared between rFOV DWI1 and rFOV DWI2. Results: The imaging quality score of 12 cases was 5.00 in rFOV DWI1, 3.60 in SS-EPI DWI, and 3.75 in rFOV DWI2. The mean ADC value of 12 cases was 1.211 × 10⁻³mm²/s in SS-EPI DWI, 1.107 × 10^–3mm²/s in rFOV DWI1, and 1.038 × 10^–3mm²/s in rFOV DWI2. SNR of rFOV DWI1 images was much higher than that of rFOV DWI2. Conclusion: rFOV DWI1 is the optimal DWI sequence in our study. Comparing with SS EPI DWI, suitable rFOV DWI has an obvious advantage, which can present higher image resolution and less distortion. It may be helpful in the diagnosis of breast cancer.

Intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) has been applied to gliomas; however, the relationships between its parameters and isocitrate dehydrogenase 1 (IDH1) mutations in gliomas remain to be investigated. To investigate the value of IVIM in predicting IDH1 mutations in high-grade gliomas before surgery. A total of 72 patients with pathologically confirmed high-grade gliomas underwent conventional brain MRI and IVIM from September 2016 to May 2020. Among them, 22 cases were IDH1 mutant and 50 cases were IDH1 wild type. The independent samples t test (normal distribution and homogeneity of variance) or Mann–Whitney rank-sum test (non-normal distribution or heterogeneity of variance) was conducted, and receiver operating characteristic curves were generated to determine parameters for distinguishing IDH1-mutated from IDH1 wild-type high-grade gliomas. These parameters included apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusivity (D*), and perfusion fraction (f). ADC, D, and f were significantly higher in IDH1-mutated gliomas than in IDH1 wild-type gliomas (P < 0.05 for all). There was no significant difference in the D* value between the two groups (P = 0.053). The areas under the curve of ADC, D, and f in predicting IDH1 mutations in gliomas were 0.798, 0.810, and 0.823, respectively. The specificity of ADC was highest (80.0%), and the sensitivity of the f value was the highest (100.0%). IVIM can be used for the quantitative prediction of IDH1 mutations in high-grade gliomas before surgery, and the f value is the most effective.