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  • Open Access

Visualization of dynamic active devices via adaptive undersampled projection imaging

  • Ashvin K George1,
  • John Andrew Derbyshire1,
  • Christina E Saikus1,
  • Ozgur Kocaturk1,
  • Robert J Lederman1 and
  • Anthony Z Faranesh1
Journal of Cardiovascular Magnetic Resonance201012(Suppl 1):P249

https://doi.org/10.1186/1532-429X-12-S1-P249

Published: 21 January 2010

Keywords

Projection ImageActive DeviceDevice MotionAcceptable CompromiseUnobstructed View

Introduction

Accurate knowledge of the location of an interventional device is crucial to the success of MR-guided interventions. This paper extends previous work [1, 2] in visualizing an active device at a single time-instant, to the case of moving devices, by updating the dynamic device trajectory from vastly reduced projection data.

Purpose

To seamlessly visualize dynamic active devices in a multi-slice real-time display by reducing data requirements.

Methods

Knowledge of the device trajectory at a particular time-instant is used to adapt the orientation of the two projection images from which the device trajectory at the next time-instant will be estimated. They are chosen to have independent and unobstructed views of the device, a small FOV in the phase-encoding direction, and allow for some device motion between the current time-instant and the next.

As shown in Figure 1 the two projection images are related to the main plane of the device trajectory ("i") by a rotation of +r ("ii") and -r ("iii") degrees about the readout axis (dashed line). Choosing r = 67 gives an acceptable compromise between a small FOV and an unobstructed view. We also allow for the wrapping of the projection image in certain cases and partial-Fourier reconstructions of the projection for further data reductions.
Figure 1

Figure 1

To compute the trajectory at a time-instant we evaluate local 2D slices, which are perpendicular to the device trajectory at the previous time-instant, in the volume image formed from the product of the two projection images. We update the previous device trajectory by the location of the centroids of these local 2D slices [2] and further refine the estimate by extending or cropping the ends of the device based on the re-projection of the 3D point onto each 2D projection image [2].

Results

The method was successfully tested on an active device in an Aortic phantom. Rotational motion of the device was simulated from full 3D k-space data (as in [3]). The data is reduced by a factor of 9 (44 total k-space lines ~150 ms). The two projection images with the estimated trajectory overlaid are displayed in Figure 2. The repetition along the phase-encoding direction reflects the undersampling.
Figure 2

Figure 2

Conclusion

Adapting the orientation and size of the projection images to the current estimate of the device trajectory allows for greater reductions in data requirements than previously achieved [3].

Authors’ Affiliations

(1)
NIH, Bethesda, USA

References

  1. George : to appear MRM.Google Scholar
  2. George , et al: ISMRM. 2009Google Scholar
  3. Schirra , et al: ISMRM. 2009Google Scholar

Copyright

© George et al; licensee BioMed Central Ltd. 2010

This article is published under license to BioMed Central Ltd.

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