Mami Iima^{1,2}, Tomomi Nobashi^{1}, Hirohiko Imai^{3}, Sho Koyasu^{1}, Akira Yamamoto^{1}, Yuji Nakamoto^{1}, Masako Kataoka^{1}, Tetsuya Matsuda^{3}, and Kaori Togashi^{1}

^{1}Department of Diagnostic Imaging and Nuclear Medicine, Graduate Schoolof Medicine, Kyoto University, Kyoto, Japan, ^{2}The Hakubi Center for Advancer Research, Kyoto University, Kyoto, Japan, ^{3}Department of Systems Science, Graduate Schoolof of Informatics, Kyoto University, Kyoto, Japan

### Synopsis

**The relationship between diffusion time and
diffusion parameters in a human hepatocellular carcinoma xenograft mouse model
was investigated at 7T. There was an increase in K values and decrease in ADCo
values in 27.6ms compared to 9.6 ms. Accordiginly a remarkable difference in a
composite index (sADC) was also found. The investigation of the water
diffusion behavior at different diffusion times may provide valuable
information on the contribution of the different compartments or tissue
components to the overall diffusion MRI.**### Introduction

Diffusion
MRI is undergoing a striking revival in the application to cancer, and many publications
have revealed the usefulness of DWI image in the diagnosis and treatment
monitoring of cancer (1), including hepatocellular carcinoma (HCC) (2).
Furthermore, recent improvements in MRI scanners have allowed to scan with
shorter diffusion times, an interesting approach to disentangle the
contribution of different tissue components to the overall diffusion MRI signal
(3). Diffusion hindrance is supposed to increase with the diffusion time, as
more water molecules hit obstacles, such as cell membranes, the density of
which increases in cancer tissues. Thus, our aim was to investigate the
relationship between diffusion time and diffusion parameters obtained from 7T
MRI using

a HCC xenograft mouse model.

### Materials and Methods

Human HCC
cell line HepG2 cells (1x10^{6}) were injected to the hind limbs of 4 Balb/c
nu/nu mice. They developed tumors in 6 weeks with the diameter of
8.8-12.4 mm, and they were imaged on a 7T MRI scanner (Bruker, Germany) using a
1H quadrature transmit/receive volume coil. The SE-EPI acquisition parameters
were set as follows; Resolution 250 x 250μm², matrix size 100 x 100,
field of view 25 x 25mm² , slice thickness 1.5mm, TE=46.9ms, TR=2500ms, 8 averages, 4 segments.
DWI MRI images were acquired using 2 different diffusion times (diffusion
gradient duration(δ): 7.2ms, and diffusion
gradient separation(Δ): 12ms and 30ms, resulting in
the effective diffusion time: 9.6 and 27.6ms) and 19 b values (from 7 to 4105 sec/mm²).
The acquisition time for each b value was 80 seconds, and the total acquisition
time was 50 min 40 sec. Data analysis was performed using a code developed in Matlab (Mathworks, Natick, MA). ROIs were drawn according to
the contrast patterns observed on anatomical and DWI images. Diffusion
parameters were retrieved for each ROI.
Signals
acquired for each diffusion time at b>500 s/mm² to remove IVIM effects was
fitted using non-Gaussian diffusion kurtosis model (4):

S(b)=[S0²{ exp [-bADCo+(bADCo)²K/6]}²+NCF]1/2 [1]

where NCF (noise correction factor) a
parameter which characterizes the “intrinsic” non-Gaussian noise contribution within
the images (4).

A composite, synthetic ADC was also calculated
as:

sADC = ln [S(Lb)/S(Hb)]/(Hb-Lb) [2]

where Lb is “low key b value”, Hb is “high
key b value” optimized to get the highest overall sensitivity to ADCo and K (5).
For this study the Low and High key b values were 438 and 2584s/mm²,
respectively.

### Results

Overall, tumors were highly
heterogeneous (Fig.1) All Examples of the plots of the diffusion signal
attenuation at two different diffusion times are shown in Fig. 2.The curvature,
indicating that diffusion is not Gaussian regardless of diffusion times,
clearly increases with the diffusion time, suggesting increased diffusion
hindrance. An increase of K (from 0.44 to 0.78,

*p*value=0.07) and a decrease of ADC (from 0.75 to 0.56x10

^{-3}mm

^{2}/s,

*p*value=0.16) was observed when the
diffusion time increased, a general trend also found in the rat brain cortex
(3). Furthermore, the difference of sADC values was strinking, decreasing from
0.58 to 0.44x10

^{-3}mm

^{2}/s (

*p*value<0.01) when the diffusion time increased from 9.6ms to
27.6ms (Fig.3).

### Discussion

The decrease of ADCo and the
increase of K values with the diffusion time are well in agreement with the
hypothesis that diffusion hindrance increases with the diffusion time, as more
molecules hit boundaries, such as cell membranes. In other words,

those results
confirm that the deviation of water diffusion from a Gaussian distribution
increases with the diffusion time in the range [9-30ms]. Interestingly, those
ADCo and K patterns

were shifted to longer diffusion times, compared to a
previous report obtained in the rat brain cortex (3), suggesting a high
sensitivity of the relationship of the diffusion parameters with the diffusion
time to the tissue types. This non-Gaussian behavior was very well picked-up
using a composite diffusion index,

sADC, which is intrinsically sensitive to
variations of both ADCo and K (5). The

sADC is simple to calculate, requiring
signal acquired at only 2 b values, and easy to implement in a clinical
setting. Our study also points out the importance of the diffusion time when
reporting diffusion parameter values when diffusion is not Gaussian, in order
to make

results comparable across studies.

### Conclusion

The investigation of the effects of the diffusion time on non-Gaussian
diffusion parameters might provide useful information on tissue types.
Non-Gaussian diffusion can be easily characterized using a composite index,
such as the

sADC. Although our preliminary study requires further validation with
a larger cohort and other tumor types, results underline the importance of providing the diffusion times when
reporting ADCo and K values.

### Acknowledgements

This work was supported
by Hakubi Project of Kyoto University and JSPS KAKENHI Grant.### References

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(2) Miller FH et al. Utility of diffusion-weighted MRI in distinguishing benign and malignant hepatic lesions. J Magn Reson Imaging. 2010;32:138-47.

(3) Pyatigorskaya N et al. Relationship between the diffusion time and the diffusion MRI signal observed at 17.2 Tesla in the healthy rat brain cortex. Magn Reson Med. 2014;72:492-500.

(4) Iima M et al. Quantitative Non-Gaussian Diffusion and Intravoxel Incoherent Motion Magnetic Resonance Imaging: Differentiation of Malignant and Benign Breast Lesions. Investigative Radiology. 2015;50:205-11.

(5) Iima M et al. Clinical Intravoxel Incoherent Motion and Diffusion MR Imaging: Past, Present and Future. Radiology (in press, Dec 2015)