2596

Effect of simultaneous multislice imaging and repetition time on biexponential liver intravoxel incoherent motion

Martin Loh¹, Christoph Stuprich¹, Tobit Führes¹, Thomas Benkert², Michael Uder¹, and Frederik Bernd Laun¹
¹Uniklinikum Erlangen, Erlangen, Germany, ²Siemens Healthineers AG, Erlangen, Germany

Synopsis

Keywords: IVIM, Diffusion/other diffusion imaging techniques

Motivation: Intravoxel incoherent motion (IVIM) measurements are often performed with various acquisition parameters among different studies. Previous studies showed differences in f during the application of simultaneous multislice (SMS).

Goal(s): The goal is to investigate the influence of SMS and the concomitant T₁-effects due to the reduction of TR.

Approach: Diffusion-weighted images of the liver were acquired for conventional and SMS excitation with long and short TR each. Results were verified with a biophysical model.

Results: The evaluation indicates no dependence of IVIM parameters on SMS. Yet, the reduction of TR appears to be relevant for small gaps between slices in the simulations.

Impact: This study indicates that while comparing different IVIM studies, TR and gaps between slices must be taken into account, as they may substantially alter the measured f. IVIM parameters for SMS excitation are well comparable with conventional excitation, though.

Introduction

Intravoxel incoherent motion (IVIM) describes the initial signal decay at small b-values due to perfusion in diffusion-weighted imaging (DWI).^1,2 The signal decay is often described as
$$S(b)/S(b=0)=(1-f)\exp{(-bD)}+f\exp{(-bD^*)}$$
with the measured signal $$$S(b)$$$, the perfusion fraction $$$f$$$, the diffusion coefficient $$$D$$$, and the pseudodiffusion coefficient $$$D^*$$$.
The simultaneous multislice imaging (SMS) technique³ significantly shortens scan time^4-7 by enabling the excitation of multiple slices. Recently, several studies employed SMS for IVIM imaging applications.^8-11 Some of them observed a significant decrease of $$$f$$$ from conventional to SMS accelerated acquisitions.^8,9,11 So far, it is ambiguous whether this effect is intrinsically caused by the SMS technique itself or due to T₁-effects. The mentioned studies made use of the scan time acceleration and drastically reduced TR for the SMS acquisition. With T1 times of blood between 1,300 ms¹² and 1,900 ms¹³, large reductions of TR may have a major impact on $$$f$$$.¹⁴
For this reason, the influence of both SMS and TR on biexponential IVIM parameters in the liver were investigated.

Methods

Diffusion-weighted images of the liver were acquired at 3T (MAGNETOM Prisma, Siemens Healthineers, Erlangen, Germany) of ten healthy volunteers. Image acquisition was performed with a short TR (1,300 ms, prefix s) for conventional slice excitation (sAF1) and SMS excitation with acceleration factor three (sAF3). Similarly, a long TR (4,500 ms, prefix l) was used for both modalities (lAF1 and lAF3). Nine 5 mm thick transversal slices were acquired at 19 b-values between 0 and 800 s/mm² and at three orthogonal diffusion directions each.
Regions of interest (ROI) were defined in the parenchyma of the liver on the $$$b=0$$$ images and transferred to the remaining diffusion-weighted images in MITK v2021.10 (Medical Imaging Interaction Toolkit).
The subsequent evaluation was performed in Python 3.9. Median values of the ROIs were computed over all voxels for each slice, b-value, and diffusion direction and were normalized to the median at $$$b=0$$$. These values were arithmetically averaged over diffusion directions and the same nominal b-values. The biexponential IVIM equation was fitted in a segmented manner^14-17 to the median signal at all b-values. Data points were weighted in the fit according to the number of averages.
Statistical analysis was performed with the Kruskal-Wallis test. In order to validate the measurements and compare it to the literature, a biophysical model was developed accounting for in- and outflow effects of blood magnetization and the resulting T₁-effects depending on the gap between slices, TR and blood flow velocities, similarly to a previous study.¹⁴In this way, $$$f$$$ can be modeled depending on the blood flow velocity $$$v_0$$$. Computations were performed with 5 mm gaps between slices and zero gaps between slices. T₁ of blood was set to 1,300 ms.¹²

Results

Figure 1 illustrates representative diffusion-weighted images for 130 s/mm² with respective segmentation and IVIM curves.
Figure 2 shows the boxplots of the biexponential IVIM parameters. No significant differences between the four modalities were observed. The p-values were 0.553 ($$$D$$$), 0.122 ($$$f$$$) and 0.856 ($$$D^*$$$).
Figure 3 shows the results from the biophysical simulations. For 5 mm gaps between slices (Figure 3A), $$$f$$$ remains constant for the given $$$v_0$$$ for TR = 4,500 ms, while it is monotonically increasing for TR = 1,300 ms with increasing $$$v_0$$$. From $$$v_0=2~\mathrm{mm/s}$$$ upwards, the measured values for $$$f$$$ are comparable for long and short TR. With no gaps in between slices (Figure 3B), both computed $$$f$$$ values remain constant over $$$v_0$$$, but with a noteworthy difference (28 % vs 24 %).

Discussion

Our study neither showed a dependency on SMS excitation nor on TR. This is in contradiction to previous studies.
In this work, we used much fewer slices and larger gaps between slices than commonly used in the literature. In this way, we reduced the effect of in- and outflowing blood magnetization.
However, pre-saturated blood magnetization flowing from neighboring slices may have a substantial influence at small slice gaps and small TR as confirmed by the biophysical model. Large enough slice gaps contain fully relaxed blood magnetization that can flow into the measured slice for appropriate $$$v_0$$$. In the case of no gaps between slices, a considerable amount of blood is already pre-saturated when being measured again.
This likely explains the mismatch between previous studies and the importance of considering TR and the distance between slices.

Conclusion

The biexponential IVIM parameters were not found to have any significant dependency on the slice excitation mode and on TR for the given measurement protocol. However, small distances between slices might considerably influence $$$f$$$ at small TR affirmed by biophysical computations.

Acknowledgements

Funding by the Deutsche Forschungsgemeinschaft is gratefully acknowledged (DFG project 446875476).

References

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Figures

Figure 1: (A) Representative diffusion-weighted images with segmentation (red) for short TR (sAF1, sAF3) and long TR (lAF1, lAF3) for b = 130 s/mm². (B) Respective IVIM data with biexponential fit curves and IVIM parameters.

Figure 2: Boxplots of D, f and D^* for sAF1, sAF3, lAF1 and lAF3. Each datapoint represents one slice. The median values are indicated by the red lines, while the mean values are presented by red crosses. Outliers are marked using red ‘+’ signs. The IQR is described by the boxes, while the whiskers show data within 1.5·IQR. Note the discontinuous axis for D* .

Figure 3: Prediction of f from the biophysical model. (A) f was computed for nine slices of slice thickness 5 mm and with 5 mm gaps for short and long TR (1,300 ms and 4,500 ms) for different blood flow velocities. The measured f at short TR is similar to f at long TR for velocities above 2 mm/s. (B) f was computed with equal parameters as in (A) except for zero gaps between the slices. This results in a noticeable difference in f between long and short TR for all given v₀.

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)

2596

DOI: https://doi.org/10.58530/2024/2596