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IVIM values in healthy brain
Steren Chabert1, Jorge Verdu1,2, Gamaliel Huerta1, Cristian Montalba3, Pablo Cox4, Rodrigo Riveros4,5, Sergio Uribe3,6, Rodrigo Salas1, and Alejandro Veloz1,7

1Biomedical Engineering Department, Universidad de Valparaiso, Valparaiso, Chile, 2Universidad Politécnica de Valencia, Valencia, Spain, 3Center for Biomedical Imaging, Pontificia Universidad Católica de Chile, Santiago, Chile, 4Servicio de Imagenología, Hospital Carlos van Buren, Valparaiso, Chile, 5Facultad de Medicina, Universidad de Valparaiso, Valparaiso, Chile, 6Radiology Department, Pontificia Universidad Católica de Chile, Santiago, Chile, 7Informatics Department, Universidad Técnica Federico Santa María, Valparaíso, Chile

### Synopsis

Even though there is much interest in brain IVIM imaging, it is difficult to get a clear view from literature on which values to expect. Our purpose is to obtain healthy brain D, D* and f, to add findings and get closer to reference values. Two distributions of 16 b-values were used to acquire data on 10 volunteers, at 1.5T: one commonly found in literature and the other considered as optimal. Values obtained from the “optimal distribution” were significantly different in all cases but D in white matter. This study emphasizes the dependence of IVIM results on the acquisition scheme applied.

### Introduction

It has become easier to obtain good quality diffusion images due to the recent improvements in MR equipment. As a consequence, there has been a renewed interest towards brain IVIM images and their relation with perfusion-related information. Various studies emphasize the relevance of these images in clinical applications, such as tumor differentiation1, grading gliomas2, stroke3 among other applications. Nevertheless, a revision of current literature does not give a clear view of what values to expect in healthy tissues, as summarized in table 1. The goal of this study is to collect a set of cerebral IVIM values in healthy subjects to provide additional findings to consolidate reference values, to be obtained in a context as close as commonly found in clinical set-ups.

### Materials and Methods

This study was approved by the institutional Ethical Committee and included ten healthy subjects (7 males, 24.7 ± 6.8 y.o.). Images were acquired on a 1.5T Philips scanner. Conventional PGSE-EPI sequence was used, with TR/TE of 4000/110ms, acceleration factor of 2, FOV 230 mm, matrix size 1282. Two b-value distributions were used, one commonly found in literature {0; 10; 20; 30; 40; 50; 60; 70; 80; 90; 100; 150; 200; 400; 800; 1000} s.mm-2 and the other one, “optimal” according to Lemke et al.4 {0; 40; 50; 60; 150; 160; 170; 190; 200; 260; 440; 560; 600; 700; 980; 1000} s.mm-2. Images were processed in MATLAB. We adjusted the perfusion fraction f, pseudo-diffusion coefficient D* and diffusion coefficient D according to equation 1, where S stands for the signal magnitude of diffusion-weighted image and S0 stands for the signal magnitude without diffusion weighting.

$$S = S_0 \left [fe^{-bD^*} + (1-f)e^{-bD} \right ]$$

IVIM parameters were fitted using trust-region-reflective algorithm in two steps. Four ROIs were positioned in GM and four ROI in WM in each volunteer’s images. Analysis was then undertaken over signal average over each ROI. We considered as outlier the ROI whose values were superior to the population mean plus three standard deviations. Heteroscedastic, two-tails t-test was applied to check difference between populations.

### Results

Quality of fit was confirmed in all cases. Even though a slightly higher number of outliers were detected in literature distribution than in optimal distribution (4 vs 1), no significant difference in quality of fit was found between optimal and literature distribution (p=0.058 in case of GM and p = 0.950 in case of WM).

Averaged values for perfusion fraction, pseudo-diffusion and diffusion coefficients are summarized in table 2, and visualized as box-plot in figure 1. All values obtained with the optimal distribution were significantly different from the ones obtained with literature distribution, except for D in white matter. f values are higher with the optimal distribution and D* are lower with the optimal distribution. f and D are different (p<0.01) in WM compared to GM, in both cases of optimal and literature distributions. D* variations were such that no difference is found between GM and WM in both literature and optimal distribution schemes.

Standard deviations of IVIM parameters adjusted using data obtained with the optimal b-value distribution are lower than the ones obtained with the literature distribution. There is more variance observed in analysis in GM. Of all parameters (f, D* and D), D* is the least stable.

### Discussion and conclusion

Discussion has been active about the impact of the methodology used to estimate IVIM parameters: different fitting methods have been used, using one or two-steps for parameters fitting, constraining fit or not, about a possible dependence over b-value threshold to be used when adjusting parameters by parts, showing among other point the influence of SNR over robustness of evaluation5-7.

This work offers new elements to answer two of the current questions in IVIM imaging: which values are to be expected and how to set up the acquisition in a clinical setting. This study shows the dependence of IVIM parameters estimation to b-value distribution scheme. Our recommendation would be to choose an optimal b-value distribution such as the one proposed by Lemke et al.4, as the parameters variance is lower. Much is to be explored still in IVIM acquisition, to develop its full potential interest as biomarkers in different pathologies.

### Acknowledgements

Grant support: CONICYT - PIA - Anillo ACT1416; PMI UVA 1402

### References

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### Figures

Table 1: IVIM values from literature in healthy subjects

Table 2: IVIM values adjusted in White and Gray Matter, using the two b-values distributions. Values are given in average ± standard deviation (with standard deviation expressed in % of the mean within parenthesis). Last row in each case indicates the p-value obtained from t-test between results obtained from data from the two distributions. ** indicates significant difference between results obtained using data from literature distribution or from optimal distribution, alfa 1%. N value corresponds to the number of ROI included, after elimination of outliers.

Figure 1. Box plots over ROI in all volunteers, “opt” indicates results obtained with optimal b-value distribution and “lit” indicates results obtained with literature b-value distribution. Top row: f values (%), middle row: D* values (x 10-3 mm2/s), bottom row: D values (x 10-3 mm2/s). Left column: Grey Matter; Right column: White Matter.

Proc. Intl. Soc. Mag. Reson. Med. 26 (2018)
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