Cardiac arrhythmias are a major clinical health problem. We have developed a significant and unique solution. During the current award period, we have designed, constructed and successfully tested a novel prototype system which integrates, in real-time, all image and associated physiological variables, device signals and computer generated data derived there from required for accurate navigation and precise targeting in catheter- based ablation of cardiac arrhythmias. The system renders obsolete or unnecessary current approaches to such treatment, including open chest surgery, pacemakers, medications that don't work, and present ablation systems. Our system design is advanced, open and flexible, permitting ready integration of new imaging modalities, interventional devices and physiologic signals as they become available. This system has achieved, for the first time, real-time performance in fusing dynamic cardiac anatomy with physiologic signals. Based on this promising, but preliminary progress, we now can specify and complete the requirements for seamless clinical implementation and application. These requirements include enhancement, optimization and addition of novel algorithms and technology. We will achieve real-time segmentation of pre- and intra- operative image data, including dyna-CT and 3D ultrasound. The segmented multimodality data will be fused in real-time in the procedure room. Using tissue characterization and catheter tracking, we will develop and validate an innovative approach to physiologic visualization of intra-operative ablation ("burn") sites. This capability will provide accurate visualization of the burn targets on anatomy to guide and verify the desired ablation pattern. These high-performance segmentation, fusion and visualization capabilities will be the basis for the final real-time platform for cardiac ablation guidance, which will be fully validated in detailed animal experiments. The system will be demonstrated efficacious both in the standard clinical set-up using catheter tracking and x-ray fluoroscopy (and/or pre-operative CT) and in an advanced set-up using a robotic manipulator for precise and stable catheter control. The system will be fully documented to facilitate ready reproduction. This project is poised to deliver a new generation of technology for effective treatment of cardiac arrhythmias that will result in significant clinical benefits, including dramatically improved outcomes with reduced morbidity, mortality, procedure time, x-ray exposure and cost.

Public Health Relevance

Cardiac arrhythmias are a major public health problem with limited effective treatment options. The project will deliver a new generation of technology based upon multi-dimensional image fusion and guidance that will provide effective treatment of cardiac arrhythmias, resulting in significant clinical benefits which include dramatically improved outcomes (cases) while reducing morbidity, mortality, procedure time, x-ray exposure and cost.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
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Biomedical Imaging Technology Study Section (BMIT)
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Krosnick, Steven
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Mayo Clinic, Rochester
United States
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Rettmann, Maryam E; Holmes 3rd, David R; Kwartowitz, David M et al. (2014) Quantitative modeling of the accuracy in registering preoperative patient-specific anatomic models into left atrial cardiac ablation procedures. Med Phys 41:021909
Linte, Cristian A; Davenport, Katherine P; Cleary, Kevin et al. (2013) On mixed reality environments for minimally invasive therapy guidance: systems architecture, successes and challenges in their implementation from laboratory to clinic. Comput Med Imaging Graph 37:83-97
Linte, Cristian A; Camp, Jon J; Holmes 3rd, David R et al. (2013) Toward modeling of radio-frequency ablation lesions for image-guided left atrial fibrillation therapy: model formulation and preliminary evaluation. Stud Health Technol Inform 184:261-7
Linte, Cristian A; Lang, Pencilla; Rettmann, Maryam E et al. (2012) Accuracy considerations in image-guided cardiac interventions: experience and lessons learned. Int J Comput Assist Radiol Surg 7:13-25
Liu, Jiquan; Rettmann, Maryam E; Holmes 3rd, David R et al. (2011) A piecewise patch-to-model matching method for image-guided cardiac catheter ablation. Comput Med Imaging Graph 35:324-32
Rettmann, M E; Holmes 3rd, D R; Cameron, B M et al. (2009) An event-driven distributed processing architecture for image-guided cardiac ablation therapy. Comput Methods Programs Biomed 95:95-104
Zavaletta, Vanessa A; Bartholmai, Brian J; Robb, Richard A (2007) High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis. Acad Radiol 14:772-87
Rettmann, M E; Holmes 3rd, D R; Su, Y et al. (2006) An integrated system for real-time image guided cardiac catheter ablation. Stud Health Technol Inform 119:455-60
Cameron, Bruce M; Robb, Richard A (2006) Virtual-reality-assisted interventional procedures. Clin Orthop Relat Res 442:63-73