Improved Metal Artifact Reduction via Image Quality Metric Optimization

Michael Manhart, Marios Psychogios, Nadine Amelung, Michael Knauth, Christopher Rohkohl

DOI:10.12059/Fully3D.2017-11-3202038

Published in:Fully3D 2017 Proceedings

Pages:233-236

Keywords:
Cone-beam CT images acquired with C-arm systems are frequently disturbed by metal artifacts. Recently, correction algorithms based on the normalized metal artifact reduction (NMAR) algorithm were introduced for commercial C-arm systems enabling restoration of most part of soft tissue contrast. The NMAR algorithm replaces attenuation values of rays passing through metal by interpolation in projection domain. Since interpolation is imperfect and can cause inconsistencies, residual streak and shadow artifacts are often visible even after NMAR reconstruction. We introduce a novel iterative post-processing step for the NMAR algorithm which modifies the interpolated attenuation values by optimizing an image quality metric in the reconstructed volume to reduce residual artifacts. The novel approach was able to reduce residual artifacts on all 14 evaluated clinical neuroradiology datasets with only moderate increase of computation time (average +12 s).
Michael Manhart
Siemens Healthcare GmbH, Germany
Marios Psychogios
Universitätsmedizin Göttingen, Germany
Nadine Amelung
Universitätsmedizin Göttingen, Germany
Michael Knauth
Universitätsmedizin Göttingen, Germany
Christopher Rohkohl
Siemens Healthcare GmbH, Germany
  1. J. Leyhe, I. Tsogkas, A. Hesse, D. Behme, K. Schregel, I. Papageorgiou, J. Liman, M. Knauth, and M. Psychogios, “Latest generation of flat detector CT as a peri-interventional diagnostic tool: a comparative study with multidetector CT,” J Neurointerv Surg, 2016, published Online First: 20 December 2016. doi: 10.1136/neurintsurg-2016-012866.
  2. E. Meyer, R. Raupach, M. Lell, B. Schmidt, and M. Kachelrieß, “Normalized metal artifact reduction (NMAR) in computed tomography,” Medical Physics, vol. 37, no. 10, pp. 5482–5493, 2010.
  3. A. Mennecke, S. Svergun, B. Scholz, K. Royalty, A. D¨orfler, and T. Struffert, “Evaluation of a metal artifact reduction algorithm applied to post-interventional flat detector CT in comparison to pre-treatment CT in patients with acute subarachnoid haemorrhage,” European Radiology, vol. 27, no. 1, pp. 88–96, 2016.
  4. M.-N. Psychogios, B. Scholz, C. Rohkohl, Y. Kyriakou, A. Mohr, P. Schramm, D. Wachter, K. Wasser, and M. Knauth, “Impact of a new metal artefact reduction algorithm in the noninvasive follow-up of intracranial clips, coils, and stents with flat-panel angiographic CTA: initial results,” Neuroradiology, vol. 55, no. 7, pp. 813–818, 2013.
  5. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D: Nonlinear Phenomena, vol. 60, no. 1, pp. 259–268, 1992.
  6. G. L. Zeng, Medical Image Reconstruction: A Conceptual Tutorial, 1st ed. Berlin, Germany: Springer, 2010.
  7. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge University Press, 2004.
  8. P. T. Lauzier, J. Tang, and G.-H. Chen, “Prior image constrained compressed sensing: Implementation and performance evaluation,” Medical Physics, vol. 39, no. 1, pp. 66–80, 2012.
  9. B. Keck, H. Hofmann, H. Scherl, M. Kowarschik, and J. Hornegger, “GPU-accelerated SART reconstruction using the CUDA programming environment,” in SPIE Medical Imaging. Lake Buena Vista, FL, USA: International Society for Optics and Photonics, February 2009, pp. 72 582B1–72 582B9.