Bilinear point distribution models for heart motion analysis

Ventura, R.; Hoogendoorn, Corné; Sukno, Federico M.; Frangi, Alejandro F.

doi:10.1109/isbi.2010.5490307

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Figure 6 shows three phases of four subjects, together with bar plots for their subject and phase parameters. The applications of cardiac bilinear shape models have already been demonstrated in image segmentation [18] and motion analysis [84], [85]. In earlier works we have also demonstrated pipelines towards electrophysiological [86] and mechanical simulations [87] from surface-based cardiac segmentations.…”

Section: B Statistical Modelingmentioning

confidence: 99%

A High-Resolution Atlas and Statistical Model of the Human Heart From Multislice CT

Hoogendoorn

Duchateau

Sánchez‐Quintana

et al. 2013

IEEE Trans. Med. Imaging

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Atlases and statistical models play important roles in the personalization and simulation of cardiac physiology. For the study of the heart, however, the construction of comprehensive atlases and spatio-temporal models is faced with a number of challenges, in particular the need to handle large and highly variable image datasets, the multi-region nature of the heart, and the presence of complex as well as small cardiovascular structures. In this paper, we present a detailed atlas and spatio-temporal statistical model of the human heart based on a large population of 3D+time multi-slice computed tomography sequences, and the framework for its construction. It uses spatial normalization based on nonrigid image registration to synthesize a population mean image and establish the spatial relationships between the mean and the subjects in the population. Temporal image registration is then applied to resolve each subject-specific cardiac motion and the resulting transformations are used to warp a surface mesh representation of the atlas to fit the images of the remaining cardiac phases in each subject. Subsequently, we demonstrate the construction of a spatio-temporal statistical model of shape such that the inter-subject and dynamic sources of variation are suitably separated. The framework is applied to a 3D+time data set of 138 subjects. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. The obtained level of detail and the extendability of the atlas present an advantage over most cardiac models published previously.

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Section: B Statistical Modelingmentioning

confidence: 99%

A High-Resolution Atlas and Statistical Model of the Human Heart From Multislice CT

Hoogendoorn

Duchateau

Sánchez‐Quintana

et al. 2013

IEEE Trans. Med. Imaging

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Atlas Construction and Image Analysis Using Statistical Cardiac Models

Craene¹,

Sukno²,

Tobon-Gómez³

et al. 2010

Statistical Atlases and Computational Models of the Heart

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This paper presents a brief overview of current trends in the construction of population and multi-modal heart atlases in our group and their application to atlas-based cardiac image analysis. The technical challenges around the construction of these atlases are organized around two main axes: groupwise image registration of anatomical, motion and fiber images and construction of statistical shape models. Applicationwise, this paper focuses on the extraction of atlas-based biomarkers for the detection of local shape or motion abnormalities, addressing several cardiac applications where the extracted information is used to study and grade different pathologies. The paper is concluded with a discussion about the role of statistical atlases in the integration of multiple information sources and the potential this can bring to in-silico simulations.

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Bilinear point distribution models for heart motion analysis

Cited by 2 publications

References 15 publications

A High-Resolution Atlas and Statistical Model of the Human Heart From Multislice CT

A High-Resolution Atlas and Statistical Model of the Human Heart From Multislice CT

Atlas Construction and Image Analysis Using Statistical Cardiac Models

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