A H Aly1, A H Aly1, E K Lai3, N Yushkevich1, R H Stoffers4, J H Gorman3, A T Cheung5, J H Gorman3, R C Gorman3, P A Yushkevich1, A M Pouch1. 1. Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA. 2. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA. 3. Gorman Cardiovascular Research Group, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA. 4. University of Groningen, Groningen, Netherlands. 5. Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University Medical Center, Stanford, CA, USA.
Abstract
BACKGROUND: In vivo characterization of mitral valve dynamics relies on image analysis algorithms that accurately reconstruct valve morphology and motion from clinical images. The goal of such algorithms is to provide patient-specific descriptions of both competent and regurgitant mitral valves, which can be used as input to biomechanical analyses and provide insights into the pathophysiology of diseases like ischemic mitral regurgitation (IMR). OBJECTIVE: The goal is to generate accurate image-based representations of valve dynamics that visually and quantitatively capture normal and pathological valve function. METHODS: We present a novel framework for 4D segmentation and geometric modeling of the mitral valve in real-time 3D echocardiography (rt-3DE), an imaging modality used for pre-operative surgical planning of mitral interventions. The framework integrates groupwise multi-atlas label fusion and template-based medial modeling with Kalman filtering to generate quantitatively descriptive and temporally consistent models of valve dynamics. RESULTS: The algorithm is evaluated on rt-3DE data series from 28 patients: 14 with normal mitral valve morphology and 14 with severe IMR. In these 28 data series that total 613 individual 3DE images, each 3D mitral valve segmentation is validated against manual tracing, and temporal consistency between segmentations is demonstrated. CONCLUSIONS: Automated 4D image analysis allows for reliable non-invasive modeling of the mitral valve over the cardiac cycle for comparison of annular and leaflet dynamics in pathological and normal mitral valves. Future studies can apply this algorithm to cardiovascular mechanics applications, including patient-specific strain estimation, fluid dynamics simulation, inverse finite element analysis, and risk stratification for surgical treatment.
BACKGROUND: In vivo characterization of mitral valve dynamics relies on image analysis algorithms that accurately reconstruct valve morphology and motion from clinical images. The goal of such algorithms is to provide patient-specific descriptions of both competent and regurgitant mitral valves, which can be used as input to biomechanical analyses and provide insights into the pathophysiology of diseases like ischemic mitral regurgitation (IMR). OBJECTIVE: The goal is to generate accurate image-based representations of valve dynamics that visually and quantitatively capture normal and pathological valve function. METHODS: We present a novel framework for 4D segmentation and geometric modeling of the mitral valve in real-time 3D echocardiography (rt-3DE), an imaging modality used for pre-operative surgical planning of mitral interventions. The framework integrates groupwise multi-atlas label fusion and template-based medial modeling with Kalman filtering to generate quantitatively descriptive and temporally consistent models of valve dynamics. RESULTS: The algorithm is evaluated on rt-3DE data series from 28 patients: 14 with normal mitral valve morphology and 14 with severe IMR. In these 28 data series that total 613 individual 3DE images, each 3D mitral valve segmentation is validated against manual tracing, and temporal consistency between segmentations is demonstrated. CONCLUSIONS: Automated 4D image analysis allows for reliable non-invasive modeling of the mitral valve over the cardiac cycle for comparison of annular and leaflet dynamics in pathological and normal mitral valves. Future studies can apply this algorithm to cardiovascular mechanics applications, including patient-specific strain estimation, fluid dynamics simulation, inverse finite element analysis, and risk stratification for surgical treatment.
Authors: Robert J Schneider; Neil A Tenenholtz; Douglas P Perrin; Gerald R Marx; Pedro J del Nido; Robert D Howe Journal: Med Image Comput Comput Assist Interv Date: 2011
Authors: Alison M Pouch; Ahmed H Aly; Eric K Lai; Natalie Yushkevich; Rutger H Stoffers; Joseph H Gorman; Albert T Cheung; Joseph H Gorman; Robert C Gorman; Paul A Yushkevich Journal: Med Image Comput Comput Assist Interv Date: 2017-09-04
Authors: Gaurav Krishnamurthy; Akinobu Itoh; Julia C Swanson; Wolfgang Bothe; Matts Karlsson; Ellen Kuhl; D Craig Miller; Neil B Ingels Journal: J Biomech Date: 2009-09-18 Impact factor: 2.712
Authors: Manuel K Rausch; Nele Famaey; Tyler O'Brien Shultz; Wolfgang Bothe; D Craig Miller; Ellen Kuhl Journal: Biomech Model Mechanobiol Date: 2012-12-21
Authors: Andrew W Siefert; David A Icenogle; Jean-Pierre M Rabbah; Neelakantan Saikrishnan; Jarek Rossignac; Stamatios Lerakis; Ajit P Yoganathan Journal: Ann Biomed Eng Date: 2013-03-05 Impact factor: 3.934