Introduction

The potential of MRI-guided catheter procedures was well-realized as early as the 1980s¹. Due to many restrictions of MRI hardware and software, its applications were very limited in the beginning. But now with the advent of powerful gradients, surface coil arrays, and parallel imaging, expansion of clinical interventional MRI-guided catheter procedures are on the horizon^2-5.
For example, angioplasty and stent implantation procedures can be carried out under an MRI environment instead of X-rays. For such procedures, it is important for the physician to see his actions on the screen with minimal delay. For example, during a stent release at the target area, any delay can result in its wrong deployment.
Understanding and optimizing the latency of the Real-Time (RT) sequences beforehand is therefore important for further development of these applications. Here, we introduce a new flexible method, allowing continuous, in-situ measurement of end-to-end latency.

Methods

The schematic diagram of our latency measurement setup is shown in Figure 1. It consists of two microcontroller boards (ESP32-CAM, distributed by Joy-IT, Neukirchen-Vluyn, Germany) with WiFi connectivity, camera modules, microsecond-precision timers, and a phantom. The phantom is built in a way that the motion of the sample is visible for both to direct visual perception and in MR images (Figure 2).
The measurements are performed on a Skyra 3T Scanner (Siemens Healthcare GmbH, Erlangen, Germany) using a radial bSSFP (TE/TR=1.71/3.42 ms, base resolution=200, 15 spokes) and a radial FLASH (TE/TR=1.3/2.02 ms, base resolution 128, 21/31/41 spokes) sequence.
Reconstruction by means of regularized non-linear inversion⁶ (NLINV) with 5 iterations on a Multi-GPU system⁷ allows displaying the images in real-time on the in-room screen (NordicNeuroLab).

One camera is mounted inside the bore to record the motion in the scanner. The other camera records the in-room screen showing the MRI video. Data from both cameras is streamed to a computer and saved along with precise timestamps. The resulting videos are processed by calculating the consecutive differences between two frames. A delay between the two videos is then determined using the cross-correlation of the norm of the difference images over a selected Region of Interest (ROI).
The correlation is determined on a window of 70 frames every time a motion happens, which can be detected from peaks in the processed signals. Using the timestamps from the internal timers, spurious variations in the frame rates of the cameras are recorded. As these might affect the estimated latency, frames from periods during which the frame rate varied were rejected.
In addition to this automatic method, we also perform manual identification of frames showing the same event. The latencies obtained from the two streams are corrected to include the offsets of the internal timers. This is done by recording a single event directly with both cameras before and after the MRI measurement.

Results

As a first result, we determined the offset between the timers of the two microcontrollers from the calibration measurement.
We found an offset of 200182.8 ms from the measurement before the MRI scan and 200184.0 ms from synchronisation after the scan. These measurements were separated by 44 minutes. The difference of 1.2 ms is well below the time between two frames, which varied between 34 and 40 ms. This shows sufficient accuracy of the hardware.

Figure 3 shows first results from our phantom experiment with a real-time radial FLASH sequence with 21 spokes and NLINV reconstruction on GPUs.
Motion of the test sample can be clearly seen as a peak in both signals. Over the course of one minute, 16 motion events were detected and processed, yielding a series of latency estimates.
Correcting the offset between the two timers, at our first motion we find a delay time of 331 ms and 347 ms using the automatic and manual methods, respectively.

A comparison of with manual delay estimation gives very similar latencies, confirming the automated latency estimation approach. This is shown in Figure 4, along with further results from experiments with different base resolutions and numbers of spokes.

Discussion

We present a simple and robust method to measure the end-to-end latency of RT MRI setup using two inexpensive optical cameras and a tube containing passive MRI markers.
The two cameras simultaneously record the live motion of the sample inside the phantom and on the MRI screen.
After correcting the time shift between the time stamps of the cameras, the latencies of real-time sequences are determined with an automated and a manual method. The accuracy of the method is influenced by the frame rate of the cameras. However by considering random motions over sufficient lengths of time, very precise measurements can be made.

To demonstrate the feasibility of our method, we analyzed some of our RT MRI sequences and determined differences in the latencies between different measurement protocols with different spatial and temporal resolution. In summary, the described setup provides reliable tool to estimate end-to-end latency that not only includes reconstruction time but also all other delays of the complete imaging pipeline.

Acknowledgements

Supported by the DZHK (German Centre for Cardiovascular Research).

References

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