The framework and AnalytiCally Represented Oxygen-17 BrAin Tumor (ACROBAT) phantom for optimization of CMRO2 quantification protocols in dynamic 17O-MRI.
Dmitry Kurzhunov1, Robert Borowiak1,2, Axel Krafft1,2, and Michael Bock1

1University Medical Center Freiburg, Dept. of Radiology - Medical Physics, Freiburg, Germany, 2German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany

Synopsis

Direct dynamic 17O-MRI allows quantification of the cerebral metabolic rate of oxygen consumption (CMRO2). The influence of acquisition parameters on the precision of CMRO2 quantification needs to be investigated for routine application, but the costly and rare 17O gas prohibits extensive imaging studies. Thus, in this work a flexible, Fourier domain-based simulation framework is presented and analytical tumor and numerical 17O MRI brain phantoms are utilized based on experimental 17O relaxation times and signal-to-noise ratios. Precision of CMRO2 quantification is evaluated and optimal acquisition parameters are given.

Introduction

$$$\hspace{1cm}$$$In healthy brain tissue, oxidative metabolization of glucose fulfills most of cellular energy demand, whereas in tumor tissue the energy production is usually shifted towards less efficient anaerobic glycolysis[1]. To quantify energy metabolism, an imaging method would be desirable to map the local cerebral metabolic rate of oxygen consumption (CMRO2).

$$$\hspace{1cm}$$$CMRO2 can be quantified with 15O-PET[2,3] or with direct 17O-MRI[4-8], where the 17O signal change is detected after the administration of 17O-enriched gas. Recently, 17O-MRI results in humans at 3T were presented[7,8]; however, acquisition parameters need to be optimized for clinical routine application, but the costly and rare 17O2 gas allows only for limited imaging studies. Thus, in this work a flexible simulation framework and two phantoms: analytical tumor and numerical, are presented to analyze the influence of various protocol parameters on CMRO2 quantification by simulation.

Material and Methods

$$$\hspace{1cm}$$$17O-MR signal in k-space was either analytically expressed or numerically estimated to simulate a dynamic 17O-MRI experiment, which consists of 4 phases: baseline, inhalation of 17O-enriched gas, re-breathing and wash-out[6-8]. Signal intensity in different brain regions was simulated separately: the modulation of the 17O signal was calculated on the k-space sampling time scale (i.e., the TR). Expected signal changes over time for the different tissues ρi(t) were retrieved and calculated according to CMRO2 15O-PET studies[2,3]. After reconstruction of the dynamic datasets, input CMRO2 values were compared to the simulation results from fitting.

$$$\hspace{1cm}$$$At first, simulations were performed on an AnalytiCally Represented Oxygen-17 BrAin Tumor (ACROBAT) with the following substructures (Fig.1e): white (WM) and gray brain matter (GM), cerebral spinal fluid (CSF), and the tumor regions contrast-enhancing rim (CE), necrotic tumor center (NE) and perifocal edema (PE).

$$$\hspace{1cm}$$$Making use of linearity of the FT, the Fourier domain signal Sk of a ACROBAT phantom was calculated as a combination of multiple 3D objects, that have an analytic representation in k-space (ellipsoids[9] and spheres):

$$Sk(\rho(t),G,\textbf{k})=\sum_{i}^{N}Sk_i(\rho_i(t),G_i,\textbf{k})\cdot{}e^{\frac{-(TE+\widetilde{t})}{T_2^*}}+A_N\cdot(randn+j\cdot{}randn).$$

Gi: shape of ith object, $$$\widetilde{t}$$$ indicates the sampling time for a certain position in k-space. The last term represents Gaussian noise, which was added to the complex k-space signal Sk using experimental SNR values (amplitude $$$A_N\sim{1/}\sqrt{BW}$$$,BW-readout bandwidth). Measured at 3T MR system in human brain T2*=2ms decay was assumed[4]. K-space simulation was done for a radial image acquisition with density adapted sampling using parameters of a 17O-MRI experiment at 3T[7,8].

$$$\hspace{1cm}$$$Secondly, a numerical 17O-MRI phantom was created from the segmented 1H MPRAGE data, where 17O concentrations were assigned to each brain tissue: GM, WM and CSF (Fig.1d). An operator A, representing dynamic 17O-MR measurement (sampling k-space trajectory), was than applied to calculate k-space signals. Finally, the framework for signal dynamics described above was applied.

Results and Discussion

$$$\hspace{1cm}$$$Figure 2 shows CMRO2 differences in GM, WM and CE as a function of spatial resolution and BW. For WM small deviations of 2% are seen at Δx≥8mm and at all BWs, since the simulated WM region is large (Fig.1e). For Δx<8mm CMRO2 is underestimated due to nonlinear signal behavior at low SNR. If only 8 or less out of 10 simulation runs for the parameter pair were successful, then they are not plotted,otherwise blue/black error bar is shown for 9/10 successful runs. As seen in Fig.2, BW≤250Hz/px and Δx≥8mm are required for reproducible CMRO2 quantification in CE tumor region. Results for different temporal and spatial re­solution (Fig.3) are more heterogeneous: CMRO2 values change smoothly for a temporal re­solution 75≤Δt≤240sec, whereas they vary strongly otherwise. Thus, intermediate parameter settings seem unfavorable. For CMRO2 quantification in the CE region 150≤BW≤250Hz/px, and Δx>7mm are required.

$$$\hspace{1cm}$$$Results of the numerical phantom (Fig.4) show that GM CMRO2 (1.12±0.11µmol/gtissue/min) is always underestimated by about 30%, but does neither depend on BW nor on spatial resolution, whereas for WM CMRO2 values are overestimated. The best values are found at BW=150Hz/px and 7≤Δx≤9mm, and for BW=250Hz/px and Δx≥7mm, with the mean CMRO2=0.74±0.03 µmol/gtissue/min and 20% overestimation.

$$$\hspace{1cm}$$$Calculated underestimation of the CMRO2 values is different in two presented phantoms since the ACROBAT phantom has simpler structure than the numerical phantom. However, the regions of most precise CMRO2 values are similar: 150≤BW≤250Hz/px and 8≤Δx≤9mm for brain and tumor regions.

Conclusions

$$$\hspace{1cm}$$$A framework with an analytical tumor ACROBAT and numerical phantoms are presented for the simulation of the dynamic 17O-MRI experiments which can be used for flexible and fast evaluation of optimal acquisition schemes. Based on the proposed framework, other phan­toms are currently implemented to investigate the effect of different object sizes on the precision of CMRO2 quantification. The influence of the MR system,T1 relaxation time and other data ac­quisition scheme can also be included in the framework.

Acknowledgements

Support from NUKEM Isotopes Imaging GmbH is gratefully acknowledged

References

[1] O. Warburg (1956) Science 123, [2] M. Ito et al. (1982) Neuroradiology 23, [3] K.L. Leenders et al. (1982) Brain 113, [4] R. Borowiak et al. (2014) MAGMA 27, [5] I.C. Atkinson et al. (2010) Neuroimage 66, [6] S.H. Hoffmann et al. (2011) MRM 66, [7] D. Kurzhunov et al. (2015) Proc. of 23rd annual meeting, ISMRM, [8] R. Borowiak et al. (2015) Proc. of 23rd annual meeting, ISMRM, [9] C.G. Koay et al. (2007) MRM 58.

Figures

Figure 1: T1-/T2-weighted 1H images (a/b), 17O-MR image (c), numerical 17O-MRI (d) and AnalytiCally Represented Oxygen-17 BrAin Tumor (ACROBAT) phantoms (e). Color-scale represents H217O concentration at natural abundance before 17O-enriched O2 gas inhalation. Both phantoms consist of white/gray matter (WM/GM) and cerebrospinal fluid (CSF) regions. ACROBAT additionally contains three tumor regions.

Figure 2: Deviation of the simulated CMRO2 values from the input CMRO2 values in gray/white matter (WM/GM) and contrast enhancing rim (CE) tumor region of the ACROBAT phantom (Fig.1e) in the dependence of spatial resolution and readout bandwidth in the presence of fast relaxation. Color scale represents deviation in %.

Figure 3: Deviation of the simulated CMRO2 values from the input CMRO2 values in gray/white matter (WM/GM) and contrast enhancing rim (CE) tumor region of the ACROBAT phantom (Fig.1e) in the dependence of spatial and temporal resolution in the presence of fast relaxation. Color scale represents deviation in %.

Figure 4: Deviation of the simulated CMRO2 values from the input CMRO2 values in white matter (WM) and gray matter (GM) of the numerical 17O-MRI phantom (Fig.1d) in the dependence of spatial resolution and readout bandwidth in the presence of fast relaxation. Color scale represents deviation in %.



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