Personalized Modeling and Assessment of the Aortic-Mitral Coupling from 4D TEE and CT

Ionasec, Razvan Ioan; Voigt, Ingmar; Georgescu, Bogdan; Yang, Wen‐Ching; Houle, Helene; Hornegger, Joachim; Navab, Nassir; Comanicìu, Dorin

doi:10.1007/978-3-642-04271-3_93

Cited by 18 publications

(17 citation statements)

References 12 publications

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“…The four-dimensional left heart model applied in this paper is physiologically defined to capture complex morphological, dynamical and pathological variations [2,4]. The model includes the following anatomic structures ( Figure 2): left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV), aortic valve (AV), mitral valve (MV), ascending aorta (Ao), and pulmonary veins (Pv).…”

Section: Patient-specific Cardiac Geometry and Dynamicsmentioning

confidence: 99%

“…The complex opening and closing motion of the aortic and mitral valves is estimated by trajectory detectors D traj within the Trajectory Spectrum Learning (TSL) framework [4]. A statistical model of the left heart, learned form the training data is projected with the estimated location and motion parameters to initialize the patient-specific geometry and dynamics.…”

Section: Patient-specific Cardiac Geometry and Dynamicsmentioning

confidence: 99%

“…The parametric models of the left atrium, left ventricle, right atrium and right ventricle are estimated in [2,3] for the entire the heart cycle. The physiological modeling of the aortic and mitral valves along with estimation methods of patient-specific geometry and dynamics are presented in [4]. As the heart valves regulate the blood flow inside the heart and towards the human body, morphological and functional parameters are essential for accurate hemodynamic simulations of the cardiac system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Patient-specific modeling of left heart anatomy, dynamics and hemodynamics from high resolution 4D CT

Mihalef

Ionasec

Wang

et al. 2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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There is a growing need for patient-specific cardiac models for intervention planning, outcome prediction or assessment of cardiac disease progression. However, most of the recent work in cardiovascular simulation relies on generic heart models built from at most one cardiac phase with simplified motion, driven by fluid dynamics equations. We propose to advance the state-of-the-art by exploiting a comprehensive, patient-specific left heart model extracted from 4D Computed Tomography (CT) data. Explicit physiological constrains are captured in the modeling of the left ventricle (including outflow tract), left atrium (including pulmonary veins), mitral valve, and aortic valve (including ascending aorta). By using this patient-specific model as an input to a 3D Navier-Stokes solver we derive realistic hemodynamics, constrained by the local anatomy, along the entire heart cycle. We present a differential assessment of the flow dynamics corresponding to specific heart conditions. The simulation results shed light upon the functional differences between one normal and two diseased hearts -one with a dilated aortic root and one with a bicuspid aortic valve.

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Section: Patient-specific Cardiac Geometry and Dynamicsmentioning

confidence: 99%

Section: Patient-specific Cardiac Geometry and Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Patient-specific modeling of left heart anatomy, dynamics and hemodynamics from high resolution 4D CT

Mihalef

Ionasec

Wang

et al. 2010

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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“…Hence, we adopt the MSL paradigm [14], a novel concept that has recently proven successful in numerous applications [3,5,13]. Instead of searching the whole nine dimensional parameter space, the MSL paradigm proposes a three-step approach.…”

Section: Iterated Marginal Space Learningmentioning

confidence: 99%

Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning

Kelm

Zhou

Suehling

et al. 2011

Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging

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Abstract. Determining spinal geometry and in particular the position and orientation of the intervertebral disks is an integral part of nearly every spinal examination with Computed Tomography (CT) and Magnetic Resonance (MR) imaging. It is particularly important for the standardized alignment of the scan geometry with the spine. In this paper, we present a novel method that combines Marginal Space Learning (MSL), a recently introduced concept for efficient discriminative object detection, with a generative anatomical network that incorporates relative pose information for the detection of multiple objects. It is used to simultaneously detect and label the intervertebral disks in a given spinal image volume. While a novel iterative version of MSL is used to quickly generate candidate detections comprising position, orientation, and scale of the disks with high sensitivity, the anatomical network selects the most likely candidates using a learned prior on the individual nine dimensional transformation spaces. Since the proposed approach is learning-based it can be trained for MR or CT alike. Experimental results based on 42 MR volumes show that our system not only achieves superior accuracy but also is the fastest system of its kind in the literature -on average, the spinal disks of a whole spine are detected in 11.5s with 98.6% sensitivity and 7.3% false positive detections. An average position error of 2.4mm and angular error of 3.9 • is achieved.

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“…1 Ionasec et al 2 proposed a method to provide patient specific assessment, however it was not capable to model ascending aortic root disorders, due to its limited length.…”

Section: Introductionmentioning

confidence: 99%

Aortic valve and ascending aortic root modeling from 3D and 3D+t CT

Grbić

Ionasec

Zäuner

et al. 2010

Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling

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Aortic valve disorders are the most frequent form of valvular heart disorders (VHD) affecting nearly 3% of the global population. A large fraction among them are aortic root diseases, such as aortic root aneurysm, often requiring surgical procedures (valve-sparing) as a treatment. Visual non-invasive assessment techniques could assist during pre-selection of adequate patients, planning procedures and afterward evaluation of the same. However state of the art approaches try to model a rather short part of the aortic root, insufficient to assist the physician during intervention planning. In this paper we propose a novel approach for morphological and functional quantification of both the aortic valve and the ascending aortic root. A novel physiological shape model is introduced, consisting of the aortic valve root, leaflets and the ascending aortic root. The model parameters are hierarchically estimated using robust and fast learning-based methods. Experiments performed on 63 CT sequences (630 Volumes) and 20 single phase CT volumes demonstrated an accuracy of 1.45mm and an performance of 30 seconds (3D+t) for this approach. To the best of our knowledge this is the first time a complete model of the aortic valve (including leaflets) and the ascending aortic root, estimated from CT, has been proposed.

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Personalized Modeling and Assessment of the Aortic-Mitral Coupling from 4D TEE and CT

Cited by 18 publications

References 12 publications

Patient-specific modeling of left heart anatomy, dynamics and hemodynamics from high resolution 4D CT

Patient-specific modeling of left heart anatomy, dynamics and hemodynamics from high resolution 4D CT

Detection of 3D Spinal Geometry Using Iterated Marginal Space Learning

Aortic valve and ascending aortic root modeling from 3D and 3D+t CT

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