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Accuracy of real-time quantitative flow imaging in small diameters using compressed sensing at 7T: a phantom study
Johannes Töger1, Mads Andersen2, and Karin Markenroth Bloch3

1Department of Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden, 2Philips Healthcare, Copenhagen, Denmark, 3Lund University Bioimaging Center, Lund University, Lund, Sweden

Synopsis

Recent studies have shown that cerebrospinal fluid (CSF) flow is strongly affected by respiration, which may potentially be used for future diagnosis and treatment follow-up in diseases such as normal pressure hydrocephalus and congenital malformations. However, quantitative measurements of respiratory effects on CSF flow in small diameters are not currently available. Therefore, this abstract shows a phantom validation of flow measurement using a radial golden-angle real-time flow sequence, reconstructed using compressed sensing. Results show that mean velocities can be quantified with a small underestimation, suggesting that the protocol is promising for future study of respiratory effects on CSF flow.

Background

Cerebrospinal fluid (CSF) flow is critical to the health of the brain and plays a central role in autoregulation of cerebral blood flow, immunological protection and mechanical cushioning. The cerebral aqueduct (Aq), with a diameter of 2-3 mm, is a central pathway for CSF circulation. Cardiac-gated aqueduct CSF flow has previously been investigated in diseases such as congenital malformations1 and hydrocephalus.2 However, recent results have shown that respiration is the main driving force of CSF circulation. Accurate real-time measurement of aqueduct CSF flow may therefore provide better understanding of several diseases.

Real-time quantification of aqueduct CSF flow presents a significant technical challenge, due to the small diameter and rapid cardiac pulsations. Therefore, the aim of this study was to investigate the performance of a real-time flow imaging protocol based on golden angle radial acquisition and compressed sensing reconstruction in a phantom setup.

Methods

Phantom setup

A pump was used to generate pulsatile flow in two plastic tubes, with diameters 3 and 4 mm (Figure 1). The tubes were submerged in a water container (1.5 liters) with approximately one tablespoon of table salt added to improve B1 homogeneity. The pump was run at 32, 46 and 58 beats per minute (bpm).

Imaging protocol

Flow imaging was performed on a Philips Achieva 7T MRI system (Philips Healthcare, Best, The Netherlands) using a dual-transmit 32-channel-receive head coil (Nova Medical, Wilmington, MA, USA). Data was acquired during 60 seconds using a 2D radial gradient-echo sequence with a golden angle increment (111.246°). Each spoke was acquired twice with opposite polarity of the flow encoding gradient (VENC=10 cm/s). The field of view was 240×240 mm, spatial resolution 0.6×0.6×5 mm (matrix size 400×400), TR/TE/flip = 10.5/5.1 ms /4°, and bandwidth 208 Hz/pixel.

For reference, a conventional 2D flow sequence was used, gated to the pump with a temporal resolution of 20 frames per cycle (Figure 1). Field of view was 210x210 mm, spatial resolution 0.3×0.3×3 mm, TR/TE/flip = 11/3.6 ms /7°, and bandwith 421 Hz/pixel.

Reconstruction

Radial raw data, cropped to 20 seconds to speed up reconstruction, was corrected for gradient delays and incoherent phases at the center of k-space. The data was then binned into timeframes with 5, 8 or 13 spokes per frame, corresponding to temporal resolutions of 105, 168 and 273 ms and acceleration factors of R=126, R=79 and R=48, respectively. Coil compression to 8 virtual channels was performed. Compressed sensing reconstruction was performed using the Berkeley Advanced Reconstruction Toolbox3 (BART) v0.4.03 using the Parallel Imaging and Compressed Sensing (PICS) module for the problem

$$ \min_x \left\| F_r S x - d \right\|_2^2 + \lambda \left\| T x\right\|_1. \qquad \textrm{(Eqn. 1)}$$

The first term describes data consistency, and the second term the temporal total variation sparsity constraint. The weighting parameter λ was varied in nine steps: 1.00×10-4, 1.78×10-4, 3.16×10-4, 5.62×10-4, 1.00×10-3, 1.78×10-3, 3.16×10-3, 5.62×10-3, and 1.00×10-2. The number of iterations was set to 500 to ensure convergence.

Reconstructions were also performed without the temporal sparsity constraint using a conjugate gradient SENSE (CG-SENSE) method4, also with 5, 8 and 13 spokes per frame.

Data analysis

Phase background was corrected by the average velocity in a ROI surrounding each vessel (Figure 2). Data was analyzed with respect to a) average velocity and b) velocity range. For the gated 2D flow data, average velocity was computed over the single reconstructed flow period. In the real-time data, average velocity was calculated over the full 20-second reconstructed dataset. Velocity range was computed as the difference between the 5th and 95th percentiles of all data points for both gated and real-time data.

Results

Figure 3 shows selected flow curves from the real-time sequence. Figure 4 shows results for mean velocity. For the 32 bpm pump program, mean velocity is slightly overestimated, but slightly underestimated for 46 and 58 bpm. Reconstructions without temporal sparsity constraint showed a strong underestimation of mean velocity. Figure 5 shows results for the velocity range. A small underestimation is seen for 32 bpm, with larger underestimations for 46 and 58 bpm. Mean velocities changed only weakly with the λ value, but velocity range values tended to decrease for increased λ.

Conclusions

The radial golden-angle flow-encoded radial sequence reconstructed using compressed sensing can be used to accurately quantify flow in a small tube with diameter 4 mm at a pulsation frequency of 32 bpm. However, smaller tubes and higher frequencies leads to underestimation of peak velocities (velocity range). With further development, the proposed method shows promise for quantification of respiratory effects on cerebrospinal fluid flow.

Acknowledgements

Image reconstructions were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the Lund University Center for Scientific and Technical Computing (LUNARC) under projects SNIC-2018/6-32 and LU-2018/2-40. Lund University Bioimaging Center (LBIC), Lund University is gratefully acknowledged for providing experimental resources.

References

  1. Wang, C. S. et al. Analysis of cerebrospinal fluid flow dynamics and morphology in Chiari I malformation with cine phase-contrast magnetic resonance imaging. Acta Neurochir. (Wien). 156, 707–713 (2014).
  2. Scollato, A., Gallina, P., Di Lorenzo, N. & Bahl, G. Is Aqueductal Stroke Volume, Measured with Cine Phase-contrast Magnetic Resonance Imaging Scans Useful in Predicting Outcome of Shunt Surgery in Suspected Normal Pressure Hydrocephalus? Neurosurgery 63, E1209 (2008).
  3. BART Toolbox for Computational Magnetic Resonance Imaging. doi:10.5281/zenodo.817472
  4. Pruessmann, K. P., Weiger, M., Börnert, P. & Boesiger, P. Advances in sensitivity encoding with arbitrary k-space trajectories. Magn. Reson. Med. 46, 638–51 (2001).

Figures

Figure 1: Phantom setup. Panel a) shows how a pulsatile pump was used to drive periodic flow through a flow phantom placed in the head transmit/receive coil of the 7T MRI system. Arrows indicate the flow direction. Panel b) shows the image slice through the flow phantom, with the 3 mm and 4 mm tubes running through the water container. Panel c) shows Flow curves for the three different pump programs for the 3 mm tube, acquired using gated 2D flow, and panel d) flow curves for the 4 mm tube.

Figure 2: Regions of interest (ROI:s) for flow analysis (blue) and phase background correction (red).

Figure 3: Selected flow curves from the real-time sequence. Results are shown here for the 4 mm tube, with 5 radial spokes per reconstructed frame (temporal resolution 105 ms). The left column (a,c,e) shows image reconstruction without the temporal total variation constraint, corresponding to a conjugate gradient SENSE (CG-SENSE) reconstruction. The right column (b, d, f) shows the compressed sensing reconstruction of the same raw data, with a moderate-strength temporal total variation constraint enabled (1.00×10-3), with clearly improved data quality.

Figure 4: Mean velocity results. The left column shows results for the 3 mm tube, and the right column for the 4 mm tube. The rows represent pump settings of 32, 46 and 58 bpm, respectively. Note how the CG-SENSE reconstruction underestimates the mean velocity in all configurations, except for the 4 mm tube at 58 bpm (f). For each case, nine different CS reconstructions with differing settings of λ (light green to dark green) are shown, in addition to a non-constrained CG-SENSE reconstruction (outlined bar).

Figure 5: Velocity range results. The velocity range was computed as the difference between the 5th and 95th percentile of all flow values during the 20-second reconstructed time window. The left column shows results for the 3 mm tube, and the right column for the 4 mm tube. The rows represent pump settings of 32, 46 and 58 bpm, respectively. For each case, nine different CS reconstructions with differing settings of λ (light green to dark green) are shown, in addition to a non-constrained CG-SENSE reconstruction (outlined bar).

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