Rapid Estimation of IVIM Pseudo-Diffusion Fraction with Correction of TE Dependence
Neil Peter Jerome1, Matthew R Orton1, Thorsten Feiweier2, Dow-Mu Koh3, Martin O Leach1, and David J Collins1

1CRUK Cancer Imaging Centre, Division of Radiotherapy & Imaging, Institute of Cancer Research, London, United Kingdom, 2Siemens Healthcare GmbH, Erlangen, Germany, 3Department of Radiology, Royal Marsden Hospital, London, United Kingdom

### Synopsis

The biexponential IVIM model of diffusion does not account for distinct T2 values for the two components, commonly interpreted as blood and tissue, leading to a TE dependence of the pseudo-diffusion volume fraction parameter f. In this volunteer study, the addition of a small number of DWI scans at different TEs allows for fitting of an extended T2-IVIM model, returning TE-independent estimations of liver f (18.26±7.3 % compared to 27.88±6.0 % from conventional IVIM fitting), and T2s of 77.6 ± 30.2 and 42.1 ± 6.8 ms for pseudo- and true diffusion compartments, respectively.

### Introduction

The two-compartment IVIM diffusion model proposed by Le Bihan (1) is commonly used for DWI studies in the body. In this model, compartments are taken to represent pseudo-diffusion and true diffusion, which may in turn represent vascular and tissue fractions. Standard diffusion-weighted imaging (DWI) protocols are acquired at a single (usually minimum) TE and incorrectly assume a single (apparent) T2, thus causing the observed pseudo-diffusion fraction f to be dependent on the TE chosen (2). Distinct transverse relaxation constants for these components (T2p and T2t for pseudo- and true diffusion compartments, respectively) modify the standard IVIM model (Eq. 1) for echo time (TE) dependency (Eq. 2), where f is pseudo-diffusion fraction, D and D* are true and pseudodiffusion coefficients, T2p and T2t are the transverse relaxations constants for pseudo- and true diffusion compartments, and ${S_{eff}} ={S_0}.\exp\left(\frac{-TE}{T_{2apparent}}\right)$:

$${S_{b}} ={S_{eff}}.\left[ f.\exp\left(-b.D^*\right) + \left(1-f\right).\exp\left(-b.D\right)\right]$$ Eq 1 (standard IVIM model)

$${S_{b,TE}} ={S_0}.\left[ f.\exp\left(\frac{-TE}{T_2p}\right).\exp\left(-b.D^*\right) + \left(1-f\right).\exp\left(\frac{-TE}{T_2t}\right).\exp\left(-b.D\right)\right]$$ Eq 2 (extended T2-IVIM model)

We present additional measurements at low b-values with increased TE as a method of deriving an estimate of f that is independent of TE, a parameter not commonly fixed in clinical MR studies. A b-value of 50mm-2s is sufficient to remove the pseudo-diffusion component in the liver (3), with the assumption that associated signal decay due to true diffusion is small (<5% for D of 1x10-3mm2s-1 in the liver). Full sampling of the b-value/TE space is challenging (4), however a clinical timeframe acquisition is able to give a TE-independent estimation of f that may be more accurate, and thus clinically useful and sensitive to modulation of pseudo-diffusion fraction, as well as providing native estimations of T2.

### Methods

Volunteers (n=6) underwent coronal free-breathing DWI of the abdomen using a MAGNETOM Avanto 1.5T scanner (Siemens Healthcare, Erlangen, Germany), acquired twice (24hr separation) using a prototype sequence with the following parameters: diffusion delays δ 16.0ms and Δ 20.2ms, 3-scan trace monopolar diffusion scheme, TR 4000ms, FOV 380x380mm2, 16x5mm slices, matrix 128x128 (interpolated to 256x256), bandwidth 1628 Hz/pixel, SPAIR fat suppression, 7/8 partial Fourier, iPAT factor 2, and 12 averages. Seven b-values (0,10,50,100,200,400,800 mm-2s) were acquired at (minimum) TE 62ms (total 15 minutes), with three additional b-values (0,10,50 mm-2s) acquired at TE 80 and 100ms (additional 10 minutes). A region of interest (ROI) was drawn for a single slice over the liver for each volunteer (Figure 1a); fitting of the standard IVIM and extended T2-IVIM model was performed for mean ROI signal in each unregistered image (b-value and signal average), and voxel-wise in one volunteer, using custom MATLAB routines. The repeat measures coefficient of variation was calculated for each parameter across the cohort using log-transformed values.

### Results

Representative ROI and voxel-wise f values for the two models are shown in Figure 1; parameters derived from the IVIM and T2-IVIM models are given in Table 1; the TE-independent f is substantially smaller (mean 35±15% decrease, p=0.002, paired t-test; Figure 2), while both D and D* from the T2-IVIM model are comparable to conventional IVIM values. The T2t value returned for the liver tissue compartment is consistent with literature (5), although the T2p returned (77.6±30.2ms) was substantially lower than literature (5) (290ms). In this study, the CoV for D and D* was small in both models (see Table 2), which for D is consistent with previous work but lower for D* than previously observed in tumours (6). In the T2-IVIM model, the CoVs for f and T2p were large (>20%), which indicates the inherent difficulty in separating these two parameters.

### Discussion

DWI studies commonly use a minimum TE dictated by gradient hardware and diffusion scheme. The dependence of the IVIM pseudo-diffusion parameter f on TE may limit its clinical utility; when using the standard IVIM model, failure to account for distinct T2 values for the two volume fractions may bias estimates of f and lead to misinterpretations of observed changes. An extended T2-IVIM model that includes T2 for each component can be used to derive TE-independent (TE = 0ms) estimations of IVIM parameters (4). Full sampling of TE/b space is limited by minimum TEs and SNRat larger TE/b combinations, but T2-IVIM parameters derived from the addition of a small number of TE/b combinations (minimum 2 b-values at 1 extra TE, additional ~20% scan time) to a standard IVIM protocol may provide useful estimates for TE-independent f, D, D*, and complementary information from T2p, and T2t (7), within clinical DWI examination times.

### Acknowledgements

CRUK and EPSRC support in association with MRC & Dept. of Health C1060/A10334, C1060/A16464 and NHS funding to the NIHR Biomedical Research Centre and the Clinical Research Facility in Imaging. Martin O Leach is a senior NIHR investigator. Neil P Jerome is funded by Imagine for Margo.

### References

1. Le Bihan D, et al. Radiology; 1998;168:497 2. Lemke A, et al. Magn. Reson. Med 64:1580–1585 (2010) 3. Jerome NP, et al. Proc. Intl. Soc. Mag. Reson. Med. 21 (2013), #2201 4. Orton MR, et al. Submitted to ISMRM 2016 5. Stansiz GJ, et al. Magn. Reson. Med 54:507-512 (2005) 6. Miyazaki K, et al. Eur. Rad 25:2641-50 (2015) 7. Cheng L, et al. Proc. Intl. Soc. Mag. Reson. Med. 23 (2015), #2878

### Figures

Figure 1: a) Example b100 image showing liver ROI, b) pseudo-diffusion fraction maps from (left) standard IVIM and (right) T2-IVIM model. c) Difference map of estimated f, and d) histogram of differences (voxel-wise fitting performed only for illustration; presented results are fitting of ROI mean values).

Figure 2: Ladder plot showing the estimated pseudo-diffusion fraction before and after removal of TE dependence using additional b/TE data (each point average from 2 scans). Grey lines are individual volunteers; black line is overall mean (n=6).

Table 1: Parameters for conventional IVIM and extended IVIM (with additional TEs) models using liver ROI in 6 healthy volunteers. The extended model estimates T2 values in pseudo- and true diffusion compartments (T2p and T2t, respectively)

Table 2: Coefficient of Variation (%) of parameters derived from IVIM and T2-IVIM modelling. In the T2-IVIM model, larger (>20%) CVs for the parameters T2p and f associated with the pseudo-diffusion compartment.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
2051