Abstract
Fiducial localization in volumetric images is a common task performed by image-guided navigation and augmented reality systems. These systems often rely on fiducials for image-space to physical-space registration, or as easily identifiable structures for registration validation purposes. Automated methods for fiducial localization in volumetric images are available. Unfortunately, these methods are not generalizable as they explicitly utilize strong a priori knowledge, such as fiducial intensity values in CT, or known spatial configurations as part of the algorithm. Thus, manual localization has remained the most general approach, readily applicable across fiducial types and imaging modalities. The main drawbacks of manual localization are the variability and accuracy errors associated with visual localization. We describe a semi-automatic fiducial localization approach that combines the strengths of the human operator and an underlying computational system. The operator identifies the rough location of the fiducial, and the computational system accurately localizes it via intensity based registration, using the mutual information similarity measure. This approach is generic, implicitly accommodating for all fiducial types and imaging modalities. The framework was evaluated using five fiducial types and three imaging modalities. We obtained a maximal localization accuracy error of 0.35 mm, with a maximal precision variability of 0.5 mm.
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References
Chen, D., Tan, J., Chaudhary, V., Sethi, I.K.: Automatic fiducial localization in brain images. International Journal of Computer Assisted Radiology and Surgery 1(1 suppl.), 45–47 (2006)
Cleary, K., Peters, T.M.: Image-guided interventions: technology review and clinical applications. Annu. Rev. Biomed. Eng. 12, 119–142 (2010)
Dang, H., Otake, Y., Schafer, S., Stayman, J.W., Kleinszig, G., Siewerdsen, J.H.: Robust methods for automatic image-to-world registration in cone-beam CT interventional guidance. Med. Phys. 39(10), 6484–6498 (2012)
Fallavollita, P., Aghaloo, Z.K., Burdette, E.C., Song, D.Y., Abolmaesumi, P., Fichtinger, G.: Registration between ultrasound and fluoroscopy or CT in prostate brachytherapy. Med. Phys. 37(6), 2749–2760 (2010)
Fattori, G., Riboldi, M., Desplanques, M., Tagaste, B., Pella, A., Orecchia, R., Baroni, G.: Automated fiducial localization in CT images based on surface processing and geometrical prior knowledge for radiotherapy applications. IEEE Trans. Biomed. Eng. 59(8), 2191–2199 (2012)
Gu, L., Peters, T.: 3D automatic fiducial marker localization approach for frameless stereotactic neuro-surgery navigation. In: Yang, G.Z., Jiang, T.-Z. (eds.) MIAR 2004. LNCS, vol. 3150, pp. 329–336. Springer, Heidelberg (2004)
Horn, B.K.P.: Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America A 4(4), 629–642 (1987)
Ji, S., Roberts, D.W., Hartov, A., Paulsen, K.D.: Intraoperative patient registration using volumetric true 3D ultrasound without fiducials. Med. Phys. 39(12), 7540–7552 (2012)
Liu, W., Ding, H., Han, H., Xue, Q., Sun, Z., Wang, G.: The study of fiducial localization error of image in point-based registration. In: International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5088–5091 (2009)
Mattes, D., Haynor, D.R., Vesselle, H., Lewellen, T.K., Eubank, W.: PET-CT image registration in the chest using free-form deformations. IEEE Trans. Med. Imag. 22(1), 120–128 (2003)
Maurer, J. C.R., Fitzpatrick, J.M., Wang, M.Y., Galloway Jr., R.L., Maciunas, R.J., Allen, G.S.: Registration of head volume images using implantable fiducial markers. IEEE Trans. Med. Imag. 16(4), 447–462 (1997)
Nicolau, S., Garcia, A., Pennec, X., Soler, L., Ayache, N.: An augmented reality system to guide radio-frequency tumour ablation. Comput. Animat. Virtual Worlds 16(1), 1–10 (2005)
Shamir, R.R., Joskowicz, L., Spektor, S., Shoshan, Y.: Localization and registration accuracy in image guided neurosurgery: a clinical study. International Journal of Computer Assisted Radiology and Surgery 4(1), 45–52 (2009)
Tan, J., Chen, D., Chaudhary, V., Sethi, I.: A template based technique for automatic detection of fiducial markers in 3D brain images. International Journal of Computer Assisted Radiology and Surgery 1, 47–49 (2006)
Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. International Journal of Computer Vision 24(2), 137–154 (1997)
Wang, M., Song, Z.: Automatic localization of the center of fiducial markers in 3D CT/MRI images for image-guided neurosurgery. Pattern Recognition Letters 30(4), 414–420 (2009)
Wang, M.Y., Maurer, J.C.R., Fitzpatrick, J.M., Maciunas, R.J.: An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head. IEEE Trans. Biomed. Eng. 43(6), 627–637 (1996)
Woerdeman, P.A., Willems, P.W., Noordmans, H.J., van der Sprenke, J.W.B.: The effect of repetitive manual fiducial localization on target localization in image space. Neurosurgery 60(2 suppl. 1), ONS-100–ONS-103 (2007)
Yaniv, Z.: Localizing spherical fiducials in c-arm based cone-beam CT. Med. Phys. 36(11), 4957–4966 (2009)
Yaniv, Z., Cleary, K.: Image-guided procedures: A review. Tech. Rep. CAIMR TR-2006-3, Image Science and Information Systems Center, Georgetown University (April 2006)
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Nagy, D.Á., Haidegger, T., Yaniv, Z. (2014). A Framework for Semi-automatic Fiducial Localization in Volumetric Images. In: Linte, C.A., Yaniv, Z., Fallavollita, P., Abolmaesumi, P., Holmes, D.R. (eds) Augmented Environments for Computer-Assisted Interventions. AE-CAI 2014. Lecture Notes in Computer Science, vol 8678. Springer, Cham. https://doi.org/10.1007/978-3-319-10437-9_15
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DOI: https://doi.org/10.1007/978-3-319-10437-9_15
Publisher Name: Springer, Cham
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