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Joint deformable liver registration and bias field correction for MR-guided HDR brachytherapy

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

In interstitial high-dose rate brachytherapy, liver cancer is treated by internal radiation, requiring percutaneous placement of applicators within or close to the tumor. To maximize utility, the optimal applicator configuration is pre-planned on magnetic resonance images. The pre-planned configuration is then implemented via a magnetic resonance-guided intervention. Mapping the pre-planning information onto interventional data would reduce the radiologist’s cognitive load during the intervention and could possibly minimize discrepancies between optimally pre-planned and actually placed applicators.

Methods

We propose a fast and robust two-step registration framework suitable for interventional settings: first, we utilize a multi-resolution rigid registration to correct for differences in patient positioning (rotation and translation). Second, we employ a novel iterative approach alternating between bias field correction and Markov random field deformable registration in a multi-resolution framework to compensate for non-rigid movements of the liver, the tumors and the organs at risk. In contrast to existing pre-correction methods, our multi-resolution scheme can recover bias field artifacts of different extents at marginal computational costs.

Results

We compared our approach to deformable registration via B-splines, demons and the SyN method on 22 registration tasks from eleven patients. Results showed that our approach is more accurate than the contenders for liver as well as for tumor tissues. We yield average liver volume overlaps of 94.0 ± 2.7% and average surface-to-surface distances of 2.02 ± 0.87 mm and 3.55 ± 2.19 mm for liver and tumor tissue, respectively. The reported distances are close to (or even below) the slice spacing (2.5 – 3.0 mm) of our data. Our approach is also the fastest, taking 35.8 ± 12.8 s per task.

Conclusion

The presented approach is sufficiently accurate to map information available from brachytherapy pre-planning onto interventional data. It is also reasonably fast, providing a starting point for computer-aidance during intervention.

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Notes

  1. http://www.gridcut.com/ (last accessed April 15, 2016).

  2. The normalization by \(\left| P \right| \) in Eq. 5 simplifies the weighting of terms \(D_s\) and \(V_{s,t}\) in Eq. 2.

  3. The normalization by \(\left| N \right| \) in Eq. 6 simplifies the weighting of terms \(D_s\) and \(V_{s,t}\) in Eq. 2.

  4. http://www.itk.org/ (last accessed November 15, 2016).

  5. http://elastix.isi.uu.nl/ (last accessed November 15, 2016).

  6. http://www.slicer.org/ (last accessed November 15, 2016).

  7. http://stnava.github.io/ANTs/ (last accessed November 15, 2016).

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Acknowledgements

This research was funded in part by the German Research Foundation (WY 169/1-1).

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Correspondence to Marko Rak or Tim König.

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Conflict of interest

M. Rak, T. König, K. D. Tönnies, M. Walke, J. Ricke and C. Wybranski declare that they have no conflict of interest with any financial organization regarding the material discussed in the manuscript.

Ethical standard

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Declaration of Helsinki 1975, as revised in 2008 (5). Informed consent was obtained from all patients for being included in the study.

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Rak, M., König, T., Tönnies, K.D. et al. Joint deformable liver registration and bias field correction for MR-guided HDR brachytherapy. Int J CARS 12, 2169–2180 (2017). https://doi.org/10.1007/s11548-017-1633-2

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  • DOI: https://doi.org/10.1007/s11548-017-1633-2

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