Learning-based 3D myocardial motion flowestimation using high frame rate volumetric ultrasound data

Wang, Yang; Georgescu, Bogdan; Comanicìu, Dorin; Houle, Hélène

doi:10.1109/isbi.2010.5490184

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…The incompressibility constraint has been widely used in cardiac image processing to improve segmentation and motion tracking (Bistoquet 2008; Garson 2008; Mansi 2010; Wang 2010; Zhu 2010). Geometric models of the left ventricle inherently incorporate both incompressibility and local smoothness of deformation, in addition to other prior information, but have been less widely used in image processing.…”

Section: Discussionmentioning

confidence: 99%

“…This is a particularly attractive constraint because it holds locally and requires no assumptions about heart geometry, material properties, or loading. For example, models using tissue incompressibility as a constraint have been used to improve both automated segmentation (Hansegard 2007; Garson 2008; Zhu 2007; Zhu 2010) and motion estimation (Bistoquet 2008; Mansi 2010; Touil 2010; Wang 2010). …”

Section: Introductonmentioning

confidence: 99%

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An Ultrasound-Driven Kinematic Model for Deformation of the Infarcted Mouse Left Ventricle Incorporating a Near-Incompressibility Constraint

Lin

French

et al. 2015

Ultrasound in Medicine & Biology

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Mathematical models of varying complexity have proved useful in fitting and interpreting regional cardiac displacements obtained from imaging methods such as ultrasound speckle tracking or MRI tagging. Simpler models, such as the classic thick-walled cylinder model of the left ventricle (LV), solve quickly and are easy to implement, but they ignore regional geometric variations and are difficult to adapt to the study of regional pathologies such as myocardial infarction. Complex, anatomically accurate finite-element models work well but are computationally intensive and require specialized expertise to implement. We developed a kinematic model that offers a compromise between these two traditional approaches, assuming only that displacements in the left ventricle are polynomial functions of initial position and that the myocardium is nearly incompressible, while allowing myocardial motion to vary spatially as would be expected in an ischemic or dyssynchronous left ventricle. Model parameters were determined using an objective function with adjustable weights to account for confidence in individual displacement components and desired strength of the incompressibility constraint. The model accurately represented the motion of both normal and infarcted mouse left ventricles during the cardiac cycle, with normalized root mean square errors in predicted deformed positions of 8.2 ± 2.3% and 7.4 ± 2.1% for normal and infarcted hearts, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Introductonmentioning

confidence: 99%

An Ultrasound-Driven Kinematic Model for Deformation of the Infarcted Mouse Left Ventricle Incorporating a Near-Incompressibility Constraint

Lin

French

et al. 2015

Ultrasound in Medicine & Biology

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show abstract

“…In addition, the images might include artefacts and image noise [107]. Another big problem is missing information, such as dropouts, shadowing, scan sector limitations and restricted echo windows [108]. Fast moving structures can also cause aliasing effects which result in spatial distortion [109].…”

Section: Ultrasound Image Acquisitionmentioning

confidence: 99%

Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: A review

et al. 2017

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“…, X n ], which need to be estimated at the current time instant t and n is the total number of points in the mesh model. To maximize the accuracy and robustness of the tracking performance, the likelihood term p(Z t |X t ) is computed from both boundary detection and image template matching as proposed in [39,40]…”

Section: Learning-based Detection and Motion Estimationmentioning

confidence: 99%

Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach

et al. 2012

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Medical image processing tools are playing an increasingly important role in assisting the clinicians in diagnosis, therapy planning and image-guided interventions. Accurate, robust and fast tracking of deformable anatomical objects, such as the heart, is a crucial task in medical image analysis. One of the main challenges is to maintain an anatomically consistent representation of target appearance that is robust enough to cope with inherent changes due to target movement, imaging device movement, varying imaging conditions, and is consistent with the domain expert clinical knowledge. To address these challenges, this chapter presents a probabilistic framework that relies on anatomically indexed component-based object models which integrate several sources of information to determine the temporal trajectory of the deformable target. Large annotated imaging databases are exploited to encode the domain knowledge in shape models and motion models and to learn discriminative image classifiers for the target appearance. The framework robustly fuses the prior information with traditional tracking approaches based on template match-

show abstract

Learning-based 3D myocardial motion flowestimation using high frame rate volumetric ultrasound data

Cited by 9 publications

References 11 publications

An Ultrasound-Driven Kinematic Model for Deformation of the Infarcted Mouse Left Ventricle Incorporating a Near-Incompressibility Constraint

An Ultrasound-Driven Kinematic Model for Deformation of the Infarcted Mouse Left Ventricle Incorporating a Near-Incompressibility Constraint

Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: A review

Learning-Based Detection and Tracking in Medical Imaging: A Probabilistic Approach

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