A probabilistic framework for freehand 3D ultrasound reconstruction applied to catheter ablation guidance in the left atrium
- 130 Downloads
The catheter ablation procedure is a minimally invasive surgery used to treat atrial fibrillation. Difficulty visualizing the catheter inside the left atrium anatomy has led to lengthy procedure times and limited success rates. In this paper, we present a set of algorithms for reconstructing 3D ultrasound data of the left atrium in real-time, with an emphasis on automatic tissue classification for improved clarity surrounding regions of interest.
Using an intracardiac echo (ICE) ultrasound catheter, we collect 2D-ICE images of a left atrium phantom from multiple configurations and iteratively compound the acquired data into a 3D-ICE volume. We introduce two new methods for compounding overlapping US data—occupancy-likelihood and response-grid compounding—which automatically classify voxels as “occupied” or “clear,” and mitigate reconstruction artifacts caused by signal dropout. Finally, we use the results of an ICE-to-CT registration algorithm to devise a response-likelihood weighting scheme, which assigns weights to US signals based on the likelihood that they correspond to tissue-reflections.
Our algorithms successfully reconstruct a 3D-ICE volume of the left atrium with voxels classified as “occupied” or “clear,” even within difficult-to-image regions like the pulmonary vein openings. We are robust to dropout artifact that plagues a subset of the 2D-ICE images, and our weighting scheme assists in filtering out spurious data attributed to ghost-signals from multi-path reflections. By automatically classifying tissue, our algorithm precludes the need for thresholding, a process that is difficult to automate without subjective input. Our hope is to use this result towards developing 3D ultrasound segmentation algorithms in the future.
Keywords3D ultrasound Occupancy grid mapping Catheter ablation Left atrium Image guided surgery
Unable to display preview. Download preview PDF.
- 1.Manzke R, Zagorchev L, d’Avila, A, Thiagalingam A, Reddy VY, Chan RC (2007) Rotational X-ray angiography: a method for intra-operative volume imaging of the left-atrium and pulmonary veins for atrial fibrillation ablation guidance. In: Proc. SPIE: medical imaging 2007: visualization and image-guided procedures, vol 6509, San Diego, pp 65,090T-1–65,090T-9Google Scholar
- 2.Thiagalingam A, Manzke R, d’Avila A, Ho I, Locke AH, Ruskin JN, Chan RC, Reddy VY (2008) Intraprocedural volume imaging of the left atrium and pulmonary veins with rotational X-ray angiography: Implications for catheter ablation of atrial fibrillation. J Cardiovasc Electrophysiol 19(3): 293–300PubMedCrossRefGoogle Scholar
- 3.Manzke R, Reddy V, Dalal S, Hanekamp A, Rasche V, Chan R (2006) Intra-operative volume imaging of the left atrium and pulmonary veins with rotational X-ray angiography. In: Medical image computing and computer-assisted intervention, Copenhagen, pp 604–611Google Scholar
- 6.Novotny P, Stoll J, Dupont P, Howe R (2007) Real-time visual servoing of a robot using three-dimensional ultrasound. In: IEEE international conference on robotics and automation, Rome, pp 2655–2660Google Scholar
- 10.Knackstedt C, Franke A, Mischke K, Zarse M, Gramley F, Schimpf T, Plisiene J, Muehlenbruch G, Spuentrup E, Ernst S, Willems S, Kirchhof P, Schauerte P (2006) Semi-automated 3-dimensional intracardiac echocardiography: development and initial clinical experience of a new system to guide ablation procedures. Heart Rhythm 3(12): 1453–1459PubMedCrossRefGoogle Scholar
- 14.Elfes A (1989) Occupancy grids: A probabilistic framework for robot perception and navigation. Ph.D. thesis, Department of Computer and Electrical Engineering, Carnegie Mellon UniversityGoogle Scholar
- 15.Thrun S (2003) Robotic mapping: a survey. In: Exploring artificial intelligence in the new millennium. Morgan Kaufmann Publishers Inc, San Francisco, pp 1–35Google Scholar
- 16.Koolwal AB, Barbagli F, Carlson CR, Liang DH (2008) An incremental method for registering electroanatomic mapping data to surface mesh models of the left atrium. In: Medical image computing and computer-assisted intervention—MICCAI 2008, vol 5242. Springer, Heidelberg, pp~847–854Google Scholar
- 18.Lickfett L, Dickfeld T, Kato R, Tandri H, Vasamreddy CR, Berger R, Bluemke D, Lüderitz B, Halperin H, Calkins H (2005) Changes of pulmonary vein orifice size and location throughout the cardiac cycle: dynamic analysis using magnetic resonance cine imaging. J Cardiovasc Electrophysiol 16(6): 582–588PubMedCrossRefGoogle Scholar
- 19.Okumura Y, Henz BD, Johnson SB, Bunch TJ, O’Brien CJ, Hodge DO, Altman A, Govari A, Packer DL (2008) Three-dimensional ultrasound for image-guided mapping and intervention: methods, quantitative validation, and clinical feasibility of a novel multimodality image mapping system. Circ Arrhythmia Electrophysiol 1(2): 110–119CrossRefGoogle Scholar
- 25.Bushberg JT, Seibert JA, Edwin M, Leidholdt M, Boone JM (2002) The essential physics of medical imaging, 2nd edn. Lippincott Williams & Wilkins, BaltimoreGoogle Scholar
- 27.Howard A, Kitchen L (1996) Generating sonar maps in highly specular environments. In: Proceedings of the fourth international conference on control automation robotics and vision, pp 1870–1874Google Scholar
- 28.Martin MC, Moravec HP (1996) Robot evidence grids. Tech Rep CMU-RI-TR-96-06. The Robotics Institute, Carnegie Mellon UniversityGoogle Scholar
- 29.Smith R, Self M, Cheeseman P (1990) Estimating uncertain spatial relationships in robotics. In: Autonomous robot vehicles. Springer, New York, Inc, pp 167–193Google Scholar