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Protocol Optimization for Functional 17O-MRI of Donor Kidneys at 3T
Yanis Taege1, Johannes Fischer1, Ali Çağlar Özen1,2, Hao Song1, Christian Schuch3, Rianne Schutter4, Cyril Moers4, Ronald JH Borra5, and Michael Bock1

1Department of Radiology, Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, 2German Cancer Consortium Partner Site Freiburg, German Cancer Research Center (DKFZ), Heidelberg, Germany, 3NUKEM Isotopes GmbH, Alzenau, Germany, 4Department of Surgery – Organ Donation and Transplantation, University Medical Center Groningen, Groningen, Netherlands, 5Medical Imaging Center, University Medical Center Groningen, Groningen, Netherlands

Synopsis

Direct 17O-MRI is able to measure the dynamics of renal metabolism in a porcine kidney in an organ transplantation setup at 3T. To obtain stable SNRs above 20 over time while maintaining a spatial resolution below 8 mm, we investigated the influence of nominal spatial resolution, bandwidth and acquisition time window of a UTE-sequence with a golden-angle acquisition pattern on SNR. Signal increase of up to 25% per liter of 17O-gas was observed in a pilot experiment.

Introduction

Most organ transplants are renal transplants1–3, but even though biomarkers exist to diagnose complications after kidney transplantation4, survival rates decrease drastically with the time after transplantation5,6. One reason for post-transplantation complications is the insufficient functional characterization of the transplanted kidney. It would thus be advantageous to measure renal function in vitro to quantify the suitability of a resected kidney before implantation. Besides perfusion and renal filtration, an important renal function parameter is tissue oxygenation.

A direct method to assess the metabolic rate of oxygen consumption is dynamic 17O-MRI, which has been extensively used for metabolic measurements in the brain7–14. In this work, we propose and optimize a 17O-MR measurement protocol to spatially assess renal metabolic rates of oxygen consumption in donor organs before transplantation.

Materials and Methods

To perform robust mapping of the renal metabolic rate of oxygen consumption (RMRO2) with 17O-MRI, the acquisition parameters need to be optimized to achieve a nominal spatial resolution of $$$\Delta{x}=6\text{mm}$$$, a minimal temporal resolution of $$$\Delta{t}=2$$$ min while maintaining a $$$\text{SNR}>20$$$.

Protocol Optimization

17O-MRI protocol optimization was performed at a clinical 3T$$$~$$$MR$$$~$$$system$$$~$$$(Prisma FIT; SIEMENS, Erlangen, Germany) with a custom-built Tx/Rx$$$~$$$17O-head coil. For image acquisition a radial UTE sequence with golden-angle (GA) projection acquisition pattern16 was used. The acquired spokes were divided using a sliding window reconstruction technique such that each image covers a specified reconstruction time window $$$\Delta{t_w}$$$. Kaiser-Bessel-regridding17 of k-space data and Hanning-filtering was subsequently applied in each frame. SNR was optimized as a function of $$$\Delta{x}$$$, BW and $$$\Delta{t_w}$$$, and other imaging parameters (Tab.$$$~$$$1) were taken from previous CMRO2 experiments in human brain7.

We numerically investigated the influence of the readout BW on the full-width-half-maximum (FWHM) of the point-spread-function by simulating the $$$T_2^*$$$-decay$$$~$$$($$$T_2^*=1.8\text{ms}$$$)18 during the acquisition with the given parameters and resulting gradient shapes. The influence of different combinations of BW and $$$\Delta{x}$$$ was experimentally evaluated on a homogeneous phantom with slightly larger dimensions than a kidney19 ($$$\text{vol}=450\text{mL}$$$) filled with 0.9%$$$~$$$NaCl. To scale the SNR dependency with the acquisition time, a non-linear fit $$$SNR=A\cdot\sqrt{(\Delta t_w)}$$$ was applied20.

To demonstrate that the optimized protocol is suitable for dynamic 17O-MRI, images of a oxygenated porcine kidney were acquired at a clinical 3T MR system (Prisma; SIEMENS, Erlangen, Germany) with a custom-built Tx/Rx$$$~$$$17O-loop$$$~$$$coil using our optimized parameters (Tab.$$$~$$$1).

Results

All combinations of BW and $$$\Delta{x}$$$ of the SNR dependency on $$$\Delta{t_w}$$$ showed the expected dependency on the square-root of time (Fig.$$$~$$$1, $$$R^2>0.9$$$), and $$$A$$$ decreased with BW and increased with $$$\Delta{x}$$$ (Fig.$$$~$$$2a). The simulation of the PSF (Fig. 2b) revealed a decrease of FWHM-factors with increasing bandwidth, approaching a limit of 2.2. For the lowest possible BW of 200 Hz/px in the desired parameter combinations, a FWHM-factor of 2.75 was found, while factors of below 2.5 were accomplished at BWs below 300 Hz/px. Figure$$$~$$$3 shows the anatomical 1H-MPRAGE image obtained with the body coil next to the interpolated 17O-magnitude signal averaged over the entire acquisition time. The mean kidney 17O-signal dynamically increased during 17O-oxygenation by up to 20% and remained relatively stable during the remaining time (Fig.$$$~$$$4).

Discussion

In the desired parameter range ($$$\text{SNR}>20,\Delta{t}\leq120$$$), a spatial resolution of 6$$$~$$$mm could be achieved while maintaining a BW below 400$$$~$$$Hz/px. Although a BW of 200$$$~$$$Hz/px at $$$~$$$6mm resolution would result in the highest SNR values for the lowest possible nominal resolution, the associated blurring (FWHM-factor$$$~$$$2.75) would deteriorate the spatial accuracy of the measurement. Thus, $$$\Delta{x}$$$ and BW were set to 6$$$~$$$mm and 280$$$~$$$Hz/pixel (Tab.$$$~$$$1), respectively, such that images could be reconstructed with an acquisition time down to 90s while maintaining the FWHM-factor in the range of 2.5. In the pilot experiment dynamic 17O-signals could be acquired over a time course of 72$$$~$$$minutes from a porcine kidney with SNR values above 20 in the ROI. The signal increase in the area of the kidney was similar those of previous CMRO2 experiments in brain, which were in the range of 10-35%7–9,14.

Conclusions

Parameter optimization of functional 17O-MRI in transplants as small as kidneys at 3T is a challenging compromise between low bandwidths and nominal resolution. Nevertheless we were able to perform a proof-of-concept experiment with the concluded parameters, which shows the possibility of dynamic 17O-MRI with SNRs above 20 and a temporal resolution down to 90$$$~$$$s. Due to direct oxygenation of the organ, the efficiency of used 17O2 of 25%/L is higher than those of previous CMRO­­2 experiments of 2.5-10%/L.

Acknowledgements

Support from NUKEM Isotopes Imaging GmbH is gratefully acknowledged.

References

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14. Hoffmann, S. H., Radbruch, A., Bock, M., Semmler, W. & Nagel, A. M. Direct 17O MRI with partial volume correction: first experiences in a glioblastoma patient. Magn. Reson. Mater. Phys. Biol. Med. 27, 579–587 (2014).

16. Chan, R. W., Ramsay, E. A., Cunningham, C. H. & Plewes, D. B. Temporal stability of adaptive 3D radial MRI using multidimensional golden means. Magn. Reson. Med. 61, 354–363 (2009).

17. Jackson, J. I., Meyer, C. H., Nishimura, D. G. & Macovski, A. Selection of a convolution function for Fourier inversion using gridding (computerised tomography application). IEEE Trans. Med. Imaging 10, 473–478 (1991).

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Figures

Table 1: Sequence parameter overview of the optimization process and the experiment. BW denotes the bandwidth, $$$\Delta{x}$$$ the nominal spatial resolution, $$$T_{RF}$$$ the length of the RF pulse, $$$\Delta{t_w}$$$ the length of the acquisition time window, $$$\Delta{t}$$$ the temporal resolution, $$$N_\text{Spokes}$$$ the total number of spokes acquired per measurement, BR the base resolution and OS the oversampling factor.

Figure 1: SNR dependency on reconstruction time window $$$\Delta{t_w}$$$ for different bandwidths BW and nominal resolutions $$$\Delta{x}$$$. Undesired values of SNR below 20 are denoted by the shaded gray area. Since the acquisition time is proportional to the number of spokes, the relation $$$SNR=A\cdot\sqrt{\Delta{t_w}}$$$, was fitted ($$$R^2>0.9$$$), with $$$A$$$ being the proportionality factor of the SNR in $$$\sqrt{\text{s}}$$$. An overview over values of $$$A$$$ is given in Figure 2a.

Figure 2: Overview of the results of the protocol optimization. (A) Overview over the dependency of the SNR proportionality factor $$$A$$$ (Fig. 1) on nominal resolution and bandwidth. (B) Simulation results showing the FWHM dependency of the point-spread-function (PSF) on bandwidth (BW).

Figure 3: Anatomical, isotropic 1H-MPRAGE image of the perfused porcine kidney (left) and the corresponding interpolated 17O-magnitude data (right), which were averaged over the entire scan time. Both images are intrinsically registered since 1H-data were acquired with the body coil while the 17O-coil was inserted.

Figure 4: Results of the dynamic 17O-perfusion experiment in a porcine kidney. Shown are the mean values of the kidney’s 17O-signal, normalized to the mean of the first 10 minutes of the scan. Error bars denote the standard error of the mean over the 2min of acquisition time. The green, shaded area marks the time period in which 0.8L of 70%-enriched 17O2-gas (NUKEM Isotopes, Alzenau, Germany) were administered.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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