Myrte Wennen^{1,2}, Manon Moll^{3}, Tim Marcus^{2}, Joost Kuijer^{2}, Leo Heunks^{1}, Christina Lavini^{2}, Gustav Strijkers^{2}, Aart Nederveen^{2}, and Oliver Gurney-Champion^{2}

^{1}Intensive Care, Amsterdam UMC, Amsterdam, Netherlands, ^{2}Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands, ^{3}University of Twente, Enschede, Netherlands

Although fat suppression can substantially improve image quality, the pulses corrupt the steady-state in a spoiled GRE sequence used for DCE and T1-relaxometry. Consequently, the conventional signal equations for quantifying tissue properties (T1, Ktrans, etc) do not hold. We have now included the effect of fat-suppression pulses in the signal equation that forms the basis for T1-relaxometry and DCE modelling. We have validated our equation with T1-mapping in a phantom and show its performance in healthy subjects and patients. Our equation enables accurate T1-mapping and pharmakinetic modelling for sequences that use fat suppression, including GRASP.

Fat suppression (FS) in VFA T1-mapping and DCE-MRI would be beneficial, as the bright signal from fat can obscure the region of interest and prevent problems due to water-fat shift. In particular, radial acquisitions suffer from substantial streaking artefacts without FS.

However, FS-pulses disrupt the steady-state magnetization that is the basis for VFA T1-relaxometry and DCE modelling, and hence FS is barely applied in DCE [4, 5]. In fact, the QIBA guidelines advice against FS as converted contrast concentrations would be incorrect due to a wrong model for baseline T1 estimates as well as for converting the signal to concentration, both required for DCE [6]. If we can account for these FS pre-pulses in the T1-relaxometry and DCE modelling, we would enable FS and allow for pharmacokinetic modelling analysis in the presence of fat.

Therefore, we included the effect of a FS-pulse into the spoiled gradient echo (GRE) signal equation, used for relaxometry and DCE modelling.

Without FS pre-pulse, the signal equation for GRE sequences is [7]:

$$$S = M_0\sin{(FA)}\frac{1-\mathrm{e}^{{\frac{-TR}{T1}}}}{1-\mathrm{e}^{{\frac{-TR}{T1}}}\cos{(FA)}}$$$[Eq.1]

with $$$FA$$$ flip angle, $$$S$$$ signal, $$$M_0$$$ magnetization in z-direction, and $$$TR$$$ repetition time. When GRE is interleaved with spectral FS pre-pulses, the steady-state of water is interrupted with a longer TR during which additional T1-relaxation takes place. We assume that magnetization right before the FS-pulse ($$$M_{startFS}$$$) is equal to the magnetization after ($$$M_{endFS}$$$), except for T1-relaxation during the pulse:

$$$M_{endFS}=M_{startFS}+(M_0-M_{startFS})(1-E_{1,FS})$$$[Eq.2]

with $$$E_{1,FS}=\mathrm{e}^{-\frac{TR_{FS}}{T1}}$$$ where $$$TR_{FS}$$$ is the duration of the FS-pulse. Furthermore, the magnetization after a train of GRE pulses after the end of the FS-pulse is:

$$$M_k=M_{endFS}(E_1\cos(FA))^{k-1}+M_0(1-E_1)\frac{(1-E_1)cos(FA)^{k-1}}{1-E_1\cos(FA)}$$$[Eq.3]

with $$$E_{1}=\mathrm{e}^{-\frac{TR}{T1}}$$$ and $$$n$$$ the number of excitations after FS. In particular, with $$$k$$$ excitations between FS-pulses, we find $$$M_{startFS}=M(k)$$$. By combining this with Eq. 2 and 3, the signal equation that takes into account FS is derived initially at $$$M_{endFS}$$$ . Then, with Eq. 3 again, the signal at $$$n$$$ excitations after FS is found:

$$$S=M_0\sin(FA)\left[\left[\left[\frac{(1-E_{1,FS})(E_1\cos(FA))^{k-1}+(1-E_1)\frac{1-(E_1\cos(FA))^{k-1}}{1-E_1\cos(FA)}}{(1-E_{1,FS}(E_1\cos(FA))^{k-1})}\right]E_{1,FS}+(1-E_{1,FS})\right](E_1\cos(FA))^{n-1}+(1-E_1)\frac{1-(E_1\cos(FA))^{n-1}}{1-E_1\cos(FA)}\right]$$$[Eq.4]

with $$$k$$$ the number of excitations between the FS-pulses. Assuming signal contrast is predominantly defined when the center of k-space is acquired, we can take $$$n=$$$ excitations until center of k-space to obtain the signal equation (Figure 1). Determining $$$n$$$ depends on acquisition parameters, including the k-space filling order, the partial-Fourier factor, start-up echoes and the shot-length.

Although these equations hold T1-mapping and for converting to contrast concentration in DCE, they are easiest validated using T1-mapping. Hence we focus on T1-relaxometry for this abstract.

Five healthy volunteers, three ICU patients, and the NISTI system phantom (CaliberMRI, T1 reference values 10–1879ms) were imaged on a 1.5T MAGNETOM Sola (Siemens Healthineers) using a 32-channel body coil. T1-maps were obtained with a fat-saturated VFA GRASP method and conventional Modified Look-Locker (MOLLI), Table 1. For the phantom, a B1 map was acquired using the dual FA method [8] to correct the FAs for B1 inhomogeneities. T1 fitting for the MOLLI method was performed, after non-rigid motion correction, using qMRLAB in MATLAB 2021a [9]. For the VFA-GRASP method, an in-house fitting pipeline was developed in MATLAB 2021a based on Eq. 1 and 4. Fitted values were compared between results from the MOLLI and VFA GRASP methods versus the manufacturer-provided reference T1 values from the phantom using linear regression ($$$\alpha = 0.05$$$). In human subjects, results from different tissues were compared using Bland-Altman analysis (liver, spleen, and skeletal muscle).

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Table 1: Overview of acquisition parameters. VIBE = Volumetric Interpolated Breath-hold Examination

Figure 1: Simulated signal for two T1-values (500 and 1500ms) showing the difference between the signal equation with correction for FS-pulses (continuous lines, Eq. 4) and without correction (dashed lines, Eq. 1), assuming M_0=1.

Figure 2: Measured T1 values in the phantom versus reference values. Linear regression equations: 1) VFA with correction T1_meas = 0.95 * T1_ref + 28 (R2 = 0.998) 2) VFA without correction T1_meas = 0.64 * T1_ref + 31 (R2 = 0.997) 3) MOLLI T1_meas = 0.90 * T1_ref + 9 (R2 = 0.998).

Figure 3: Example of T1-maps using the MOLLI method (left), the
corrected VFA method (middle) and in the phantom (right). Two ROIs (liver and
muscle tissue, light blue) are indicated that were used for comparison with the
MOLLI method. The ROI in the spleen was only used within the VFA methods,
because the spleen was not in the FOV of the MOLLI acquisition.

Figure 4: Bland-Altman plots using the corrected and uncorrected VFA
method (left), the uncorrected VFA method and MOLLI method (middle) and the
corrected VFA method and MOLLI method (right). Note the improved agreement
between the VFA and MOLLI method in right vs. middle plot.

DOI: https://doi.org/10.58530/2022/2693