Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

BACKGROUND

The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery.

METHODS

Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student's t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs.

RESULTS

DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03).

CONCLUSION

Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.

Differentiation of high-grade from low-grade diffuse gliomas using diffusion-weighted imaging: a comparative study of mono-, bi-, and stretched-exponential diffusion models

Purpose

Diffusion-weighted imaging (DWI) plays an important role in the preoperative assessment of gliomas; however, the diagnostic performance of histogram-derived parameters from mono-, bi-, and stretched-exponential DWI models in the grading of gliomas has not been fully investigated. Therefore, we compared these models’ ability to differentiate between high-grade and low-grade gliomas.

Methods

This retrospective study included 22 patients with diffuse gliomas (age, 23–74 years; 12 males; 11 high-grade and 11 low-grade gliomas) who underwent preoperative 3 T-magnetic resonance imaging from October 2014 to August 2019. The apparent diffusion coefficient was calculated from the mono-exponential model. Using 13 b-values, the true-diffusion coefficient, pseudo-diffusion coefficient, and perfusion fraction were obtained from the bi-exponential model, and the distributed-diffusion coefficient and heterogeneity index were obtained from the stretched-exponential model. Region-of-interests were drawn on each imaging parameter map for subsequent histogram analyses.

Results

The skewness of the apparent diffusion, true-diffusion, and distributed-diffusion coefficients was significantly higher in high-grade than in low-grade gliomas (0.67 ± 0.67 vs. − 0.18 ± 0.63, 0.68 ± 0.74 vs. − 0.08 ± 0.66, 0.63 ± 0.72 vs. − 0.15 ± 0.73; P = 0.0066, 0.0192, and 0.0128, respectively). The 10th percentile of the heterogeneity index was significantly lower (0.77 ± 0.08 vs. 0.88 ± 0.04; P = 0.0004), and the 90th percentile of the perfusion fraction was significantly higher (12.64 ± 3.44 vs. 7.14 ± 1.70%: P < 0.0001), in high-grade than in low-grade gliomas. The combination of the 10th percentile of the true-diffusion coefficient and 90th percentile of the perfusion fraction showed the best area under the receiver operating characteristic curve (0.96).

Conclusion

The bi-exponential model exhibited the best diagnostic performance for differentiating high-grade from low-grade gliomas.

Electronic supplementary material

The online version of this article (10.1007/s00234-020-02456-2) contains supplementary material, which is available to authorized users.

Optimising sampling patterns for bi-exponentially decaying signals

A recently reported method, based on the Cramér-Rao Lower Bound theory, for optimising sampling patterns for a wide range of nuclear magnetic resonance (NMR) experiments is applied to the problem of optimising sampling patterns for bi-exponentially decaying signals. Sampling patterns are optimised by minimizing the percentage error in estimating the most difficult to estimate parameter of the bi-exponential model, termed the objective function. The predictions of the method are demonstrated in application to pulsed field gradient NMR data recorded for the two-component diffusion of a binary mixture of methane/ethane in a zeolite. It is shown that the proposed method identifies an optimal sampling pattern with the predicted objective function being within 10% of that calculated from the experiment dataset. The method is used to advise on the number of sampled points and the noise level needed to resolve two-component systems characterised by a range of ratios of populations and diffusion coefficients. It is subsequently illustrated how the method can be used to reduce the experiment acquisition time while still being able to resolve a given two-component system.

The Kärger vs bi-exponential model: Theoretical insights and experimental validations

We revise three common models accounting for water exchange in pulsed-gradient spin-echo measurements: a bi-exponential model with time-dependent water fractions, the Kärger model, and a modified Kärger model designed for restricted diffusion, e.g. inside cells. The three models are compared and applied to experimental data from yeast cell suspensions. The Kärger model and the modified Kärger model yield very close results and accurately fit the data. The bi-exponential model, although less rigorous, has a natural physical interpretation and suggests a new experimental modality to estimate the water exchange time.

Intravoxel incoherent motion diffusion weighted MRI of cervical cancer - Correlated with tumor differentiation and perfusion

OBJECTIVES

To investigate the value of parameters derived from IVIM model in grading of uterine cervical cancer and the relationship between perfusion parameters derived from IVIM and that from DCE-MRI.

METHODS

Parameters of DWI (ADC, D, f, D*) and semi-quantitative parameters of DCE-MRI (Slop, Maxslop, CER, Washout, AUC90) were assessed in 24 female with cervical cancers. Except for ROIs encompassed all of the area of tumors in axial plane (A_all), ROIs on tumor edge (A_peri) and tumor center (A_central) were drawn. All of the parameters were compared among three pathology grades. Perfusion parameters derived from IVIM were correlated with that from DCE-MRI.

RESULTS

For G1, G2 and G3 tumors, on tumor edge ADC=(1.03±0.11), (1.05±0.10), (0.90±0.05)×10(-3)mm(2)/s, D=(0.80±0.11), (0.78±0.07), (0.69±0.06)×10(-3)mm(2)/s, and f=(0.19±0.03), (0.22±0.02), (0.24±0.03). The differences among groups were significant (P<0.05). On tumor center, ADC=(0.90±0.10), (0.85±0.03), (0.80±0.07)×10(-3)mm(2)/s with significant differences (P=0.027). The other parameter, D and f of tumor center, as well as D* of all tumor areas, were of no statistic significance. Most of the DCE-MRI parameters negatively correlated with tumor volume. Although the correlation between f and slop was statistic significant, R=0.277 meant a negligible correlation. f had week correlation with Maxslop, CER and AUC90 (R=0.361, 0.400 and 0.405; P<0.001). D* showed no statistic significant correlation with all of the DCE parameters.

CONCLUSION

IVIM model could possibly be used to evaluate tumor differentiation and perfusion, providing an alternative for DCE-MRI.

Comparison of mono-exponential and bi-exponential models of diffusion-weighted MRI in diagnosis of small VX2 hepatic tumors in rabbits

AIM: To investigate the value of mono-exponential model with single b-factor and bi-exponential model with extended b-factor range of diffusion-weighted imaging (DWI) in diagnosis of small VX2 hepatic tumors ( ≤ 3 cm) in rabbits.

METHODS: On the 7^th day and 14^th day after tumor implantation, 50 New Zealand white rabbits with VX2 hepatic tumors underwent DWI based on single b-factor (b values of 0 and 800 s/mm²) and multi-b-factor (b values of 0, 20, 50, 100, 200, 400, 600, 800 and 1200 s/mm²). Apparent diffusion coefficient (ADC), slow ADC, fast ADC and fraction fast ADC (f_fast) were measured in the rim of tumor (TR) and the normal region (NR) and compared between the two groups. The best thresholds of ADC, slow ADC, fast ADC and f_fast were calculated by the receiver operating characteristic curve (ROC).

RESULTS: There were significant differences between TR and NR in ADC values on days 7 and 14 (P < 0.05). The values of slow ADC of TR were superior than those of NR on both days 7 and 14. Fast ADC and f_fast of NR were higher than those of TR on the 14^th day (P < 0.05). Expect for slow ADC, no statistical difference was observed in ADC, Fast-ADC or f_fast of TR between the 7^th day and 14^th day. Slow ADC offered the highest sensitivity and specificity compared to ADC, slow-ADC, fast-ADC and f_fast for differentiating between TR and NR on both days 7 and 14.

CONCLUSION: The parameter values of mono-exponential model and bi-exponential model, especially slow-ADC, reflect perfusion of microcirculation and diffusion of water molecules, thus having value for the diagnosis of small hepatic tumors.