TREK: an integrated system architecture for intraoperative cone-beam CT-guided surgery
A system architecture has been developed for integration of intraoperative 3D imaging [viz., mobile C-arm cone-beam CT (CBCT)] with surgical navigation (e.g., trackers, endoscopy, and preoperative image and planning data). The goal of this paper is to describe the architecture and its handling of a broad variety of data sources in modular tool development for streamlined use of CBCT guidance in application-specific surgical scenarios.
The architecture builds on two proven open-source software packages, namely the cisst package (Johns Hopkins University, Baltimore, MD) and 3D Slicer (Brigham and Women’s Hospital, Boston, MA), and combines data sources common to image-guided procedures with intraoperative 3D imaging. Integration at the software component level is achieved through language bindings to a scripting language (Python) and an object-oriented approach to abstract and simplify the use of devices with varying characteristics. The platform aims to minimize offline data processing and to expose quantitative tools that analyze and communicate factors of geometric precision online. Modular tools are defined to accomplish specific surgical tasks, demonstrated in three clinical scenarios (temporal bone, skull base, and spine surgery) that involve a progressively increased level of complexity in toolset requirements.
The resulting architecture (referred to as “TREK”) hosts a collection of modules developed according to application-specific surgical tasks, emphasizing streamlined integration with intraoperative CBCT. These include multi-modality image display; 3D-3D rigid and deformable registration to bring preoperative image and planning data to the most up-to-date CBCT; 3D-2D registration of planning and image data to real-time fluoroscopy; infrared, electromagnetic, and video-based trackers used individually or in hybrid arrangements; augmented overlay of image and planning data in endoscopic or in-room video; and real-time “virtual fluoroscopy” computed from GPU-accelerated digitally reconstructed radiographs (DRRs). Application in three preclinical scenarios (temporal bone, skull base, and spine surgery) demonstrates the utility of the modular, task-specific approach in progressively complex tasks.
The design and development of a system architecture for image-guided surgery has been reported, demonstrating enhanced utilization of intraoperative CBCT in surgical applications with vastly different requirements. The system integrates C-arm CBCT with a broad variety of data sources in a modular fashion that streamlines the interface to application-specific tools, accommodates distinct workflow scenarios, and accelerates testing and translation of novel toolsets to clinical use. The modular architecture was shown to adapt to and satisfy the requirements of distinct surgical scenarios from a common code-base, leveraging software components arising from over a decade of effort within the imaging and computer-assisted interventions community.
KeywordsImage-guided surgery Cone-beam CT Intraoperative imaging Surgical navigation System architecture Open-source software
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- 8.Deguet A, Kumar R, Taylor R, Kazanzides P (2008) The cisst libraries for computer assisted intervention systems. MIDAS J Syst Archit Comput Assist IntervGoogle Scholar
- 9.Pieper S, Lorensen B, Schroeder W, Kikinis R (2006) The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community. In: Proceedings of IEEE International Symposium Biomedical Imaging, 6–9 April 2006. pp 698–701Google Scholar
- 10.Wolf I, Nolden M, Boettger T, Wegner I, Schoebinger M, Hastenteufel M, Heimann T, Meinzer H-P, Vetter M (2005) The MITK approach. Insight Journal—MICCAI Open-Source WorkshopGoogle Scholar
- 11.Rexilius J, Spindler W, Jomier J, Koenig M, Hahn H, Link F, Peitgen H-O (2005) A framework for algorithm evaluation and clinical application prototyping using ITK. Insight Journal—MICCAI Open-Source WorkshopGoogle Scholar
- 12.Schroeder W, Lorenson B (1996) Visualization toolkit: an object-oriented approach to 3-D graphics. Prentice Hall PTR, Englewood CliffsGoogle Scholar
- 13.Ibanez L, Schroeder W, Ng L, Cates J (2005) The ITK software guide. MIDAS J Syst Archit Comput Assist IntervGoogle Scholar
- 14.Tokuda J, Fischer GS, Papademetris X, Yaniv Z, Ibanez L, Cheng P, Liu H, Blevins J, Arata J, Golby AJ, Kapur T, Pieper S, Burdette EC, Fichtinger G, Tempany CM, Hata N (2009) OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robot 5(4): 423–434PubMedCrossRefGoogle Scholar
- 17.Schafer S, Nithananiathan S, Mirota DJ, Uneri A, Stayman JW, Zbijewski W, Schmidgunst C, Kleinszig G, Khanna AJ, Siewerdsen JH (2011) Mobile C-Arm cone-beam CT for guidance of spine surgery: image quality, radiation dose, and integration with interventional guidance. Med Phys (to appear)Google Scholar
- 23.Reaungamornrat S, Otake Y, Uneri A, Schafer S, Stayman JW, Zbijewski W, Mirota DJ, Yoo J, Nithiananthan S, Khanna AJ, Taylor RH, Siewerdsen JH (2011) Tracker-on-C: a novel tracker configuration for image-guided therapy using a mobile C-arm. In: Computer Assisted Radiology and Surgery, Berlin, Germany, 22–25 June 2011. CARS (to appear)Google Scholar
- 24.Bradski G, Kaehler A (2008) Learning OpenCV: computer vision with the OpenCV library. O’Reilly Media, SebastopolGoogle Scholar
- 25.Oguz I, Gerig G, Barre S, Styner M (2006) KWMeshVisu: a mesh visualization tool for shape analysis. Insight Journal—MICCAI Open-Source WorkshopGoogle Scholar
- 28.Mirota DJ, Uneri A, Schafer S, Nithiananthan S, Reh DD, Gallia GL, Taylor RH, Hager GD, Siewerdsen JH (2011) High-accuracy 3D image-based registration of endoscopic video to C-arm cone-beam CT for image-guided skull base surgery. In: Wong KH, Holmes Iii DR (eds) SPIE medical imaging: visualization, image-guided procedures, and modeling. SPIE, Lake Buena Vista, pp 79640J–79610Google Scholar
- 31.Daly MJ, Chan H, Prisman E, Vescan A, Nithiananthan S, Qiu J, Weersink R, Irish JC, Siewerdsen JH (2010) Fusion of intraoperative cone-beam CT and endoscopic video for image-guided procedures. In: Wong KH, Miga MI (eds) SPIE medical imaging: visualization, image-guided procedures, and modeling. SPIE, San Diego, pp 762503–762508Google Scholar
- 32.Mirota D, Wang H, Taylor R, Ishii M, Hager G (2009) Toward video-based navigation for endoscopic endonasal skull base surgery. In: Yang G-Z, Hawkes D, Rueckert D, Noble A, Taylor C (eds) Medical Image Computing and Computer Assisted Intervention Lecture notes in computer science, vol 5761. Springer, Berlin, Heidelberg, pp 91–99Google Scholar
- 33.Strobl KH, Hirzinger G (2006) Optimal hand-eye calibration. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 9–15 Oct. 2006, pp 4647–4653Google Scholar
- 35.Pluim J, Maintz J, Viergever M (2000) Image registration by maximization of combined mutual information and gradient information. In: Delp S, DiGoia A, Jaramaz B (eds) Medical Image Computing and Computer Assisted Intervention Lecture notes in computer science, vol 1935. Springer, Berlin, Heidelberg, pp 103–129Google Scholar
- 36.Hansen N (2006) The CMA evolution strategy: a comparing review. In: Lozano J, Larrañaga P, Inza I, Bengoetxea E (eds) Towards a new evolutionary computation, vol 192. Studies in fuzziness and soft computing. Springer, Berlin, Heidelberg, pp 75–102Google Scholar
- 38.Simpson AL, Ma B, Ellis RE, Stewart AJ, Miga MI (2011) Uncertainty propagation and analysis of image-guided surgery. In: Wong KH, Holmes Iii DR (eds) SPIE medical imaging: visualization, image-guided procedures, and modeling. SPIE, Lake Buena Vista, Florida, USA, pp 79640H–79647Google Scholar
- 39.Hamming NM, Daly MJ, Irish JC, Siewerdsen JH (2008) Effect of fiducial configuration on target registration error in intraoperative cone-beam CT guidance of head and neck surgery. In: Proceedings of the IEEE Engineering in Medicine and Biology Society, 20–25 Aug. 2008, pp 3643–3648Google Scholar