Synopsis

We developed an intra-cardiac MRI (ICMRI) catheter to monitor heating during MRI-guided electro-physiological ablative procedures. ICMRI includes an imaging coil that expands within the cardiac chambers and a tetrahedral-shaped array of MR-tracking coils, intended to compensate for cardiac motion during the imaging process. In this study, we used real swine cardiac MR-tracking data, which may contain varying levels of positional uncertainty (noise), to develop algorithms that filter this noise, and thus do not distort the motional (translational/rotational) estimates, required for delivering non-blurred high-resolution intra-cardiac images. Our results show that submillimiter-error motion reconstruction is feasible under realistic levels of noise.

Purpose

Methods

Noisy MRT-based positional-tracking within the heart of a swine. We previously³ measured LV cardiac motion at 40msec increments (during breath-holds) over multiple cardiac cycles by inserting 1-mm diameter catheters with 3 MRT coils into the Left Anterior Descending and Right Coronary Artery vessels in multiple swine. We used data from 2 MRT coils to manufacture an artificial 4-coil tetrahedron array, representing motion of the ICMRI catheter, and followed displacement and rotations over 30 cardiac cycles [Fig.2]. The noise in each MRT coil was estimated to be ~1.2mm using a high-pass filter. Simulations with increasing (0-4mm) uniform distributed noise were used to study the performance of the reconstruction algorithm under varying levels of noise [Fig.3].

Application of filters to improve rotational and translational estimation. We made use of a learning time-period (200 MTR points,~40sec) to measure the dimensions of the extended tetrahedron and the noise level of each coil, so that we could adaptively select rotational and translational noise thresholds. For a system with $$$n$$$ coils, our adaptive algorithm provided a set of $$$\binom{n}{3}$$$ rotational estimations and $$$n$$$ translational estimations, which were then filtered to reduce the noise effect on the reconstruction. Various filtering strategies (mean, outlier rejection and 1σ filter) were analyzed so that the optimal filter, for a given level of noise, was automatically chosen after the learning period.

Filter different cardiac phases according to the reconstruction performance. After the optimal rotational and translational estimations were calculated [Fig.4], we compared the MRT input data with the reconstructed position in real time. Time points with a discrepancy in position larger than 1mm could be disregarded, entailing skipping imaging of cardiac-cycle-segments with potentially large artifacts [Fig.5].

Results

The algorithm was able to reconstruct positions with sub-millimeter accuracy for noise levels up to ~2mm. The ratio of reconstruction error to noise remained below 45% up to ~3mm noise levels. Further increments in noise levels make the error increase in a polynomic fashion.

Using the real (~1.2mm noise) cardiac swine data, we found, in <33% of the cardiac cycle, reconstructed position estimations with more than 1 mm discrepancy from the MRT data.

Discussion and Conclusion

MRT-based motional estimation inside the heart can be improved dramatically by application of noise filtering. Such filters reduced errors in translation estimation and in rotational estimation by 30-40% for noise levels up to 3 mm. We observed that when all coils had the same level of noise (σ), averaging the estimators (mean filter) was a better strategy than rejecting outliers (1σ filter). If one coil was particularly noisy (>2σ), rejecting its data was the most advantageous choice.

Residual errors in the reconstruction remained mainly in the systolic region of the cardiac cycle. Thus, the diastolic phase was a more resilient area, well suited for image acquisition with reduced motion artifacts. The algorithm was robust to errors up to 1mm if the noise is under ~2mm. The larger the noise, the more data samples that needed to be rejected to avoid motion artifacts, increasing the scan time. For a realistic scenario, less than 33% of the cardiac cycle needed to be excluded, so image acquisition could be performed over most of the cardiac cycle.

Acknowledgements

References

1. Schmidt EJ, Qin L, Santos J, Michaud G, Kwong R, Stevenson W.G, Dumoulin CL. Intra-Cardiac MRI Catheter for EP Ablation Monitoring: Preliminary Studies. The International Society for Magnetic Resonance in Medicine 19th Scientific Meeting & Exhibition, 2011 May, Montréal, Québec, Canada.

2. Qin L, Schmidt EJ, Tse ZT, Santos J, Hoge WS, Tempany-Afdhal C, Butts-Pauly K, Dumoulin CL. Prospective motion correction using tracking coils Magn Reson Med. 2013; 69: 749–759.

3. Schmidt EJ, Yoneyama R, Dumoulin CL, Darrow RD, Klein E, Hayase M. Detailed 3D Coronary Motion Tracking in swine models using MR Tracking catheters. J. Magnetic Resonance Imaging 2009; 29:86-98.