Anatomical Modeling with Fuzzy Implicit Surface Templates: Application to Automated Localization of the Heart and Lungs in Thoracic MR Volumes

Lelieveldt, Boudewijn P. F.; Sonka, Milan; Bolinger, Lizann; Scholz, Thomas; Kayser, Hein W. M.; Geest, Rob J. van der; Reiber, Johan H. C.

doi:10.1006/cviu.2000.0864

Cited by 17 publications

(8 citation statements)

References 58 publications

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“…It is defined via the boundary distance field, which was first introduced in [7]. This field represents a mapping b ∆ : R 3 → R that computes a value for each point according to the following formula:…”

Section: Metric and Optimizermentioning

confidence: 99%

See 1 more Smart Citation

3D Model-Based Approach to Lung Registration and Prediction of Respiratory Cardiac Motion

Danilouchkine

Westenberg

Assen

et al. 2005

Lecture Notes in Computer Science

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Abstract. This paper presents a new approach for lung registration and cardiac motion prediction, based on a 3D geometric model of the left lung. Feature points, describing a shape of this anatomical object, are automatically extracted from acquired tomographic images. The "goodness-of-fit" measure is assessed at each step in the iterative scheme until spatial alignment between the model and subject's specific data is achieved. We applied the proposed methods to register the 3D lung surfaces of 5 healthy volunteers of thoracic MRI acquired in different respiratory phases. We also utilized this approach to predict the spatial displacement of the human heart due to respiration. The obtained results demonstrate a promising registration performance.

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Section: Metric and Optimizermentioning

confidence: 99%

“…In this work we adopted the model-driven segmentation method for thoracic MRI images and automated recognition of the organs with the profound airtissue boundaries proposed in [7]. A new concept of multi-resolution registration is introduced.…”

Section: Introductionmentioning

confidence: 99%

3D Model-Based Approach to Lung Registration and Prediction of Respiratory Cardiac Motion

Danilouchkine

Westenberg

Assen

et al. 2005

Lecture Notes in Computer Science

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“…Magnetic resonance imaging (MRI) allows morphological characterization of heart structures with precision. For the past decade, there has been a great deal of efforts focused on automatic cardiac segmentation [5,1,2], however, less attention has been paid to automatic and fast planning for cardiac MRI acquisition [7,8,4,6], which still remains challenging in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

“…Lelieveldt et al [7,8] proposed fuzzy implicit surface templates to model thoracic anatomies and automatically estimate the LV orientation for view planning. The method was evaluated on 20 volumes and planning was for short-axis stack only.…”

Section: Introductionmentioning

confidence: 99%

Automatic View Planning for Cardiac MRI Acquisition

Jolly

Georgescu

et al. 2011

Lecture Notes in Computer Science

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Abstract. Conventional cardiac MRI acquisition involves a multi-step approach, requiring a few double-oblique localizers in order to locate the heart and prescribe long-and short-axis views of the heart. This approach is operator-dependent and time-consuming. We propose a new approach to automating and accelerating the acquisition process to improve the clinical workflow. We capture a highly accelerated static 3D full-chest volume through parallel imaging within one breath-hold. The left ventricle is localized and segmented, including left ventricle outflow tract. A number of cardiac landmarks are then detected to anchor the cardiac chambers and calculate standard 2-, 3-, and 4-chamber long-axis views along with a short-axis stack. Learning-based algorithms are applied to anatomy segmentation and anchor detection. The proposed algorithm is evaluated on 173 localizer acquisitions. The entire view planning is fully automatic and takes less than 10 seconds in our experiments.

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“…In our laboratory we developed a novel system for fully automatic planning of cardiac MR acquisition [1]. Our approach automatically combines information from the scout acquisitions into a three-dimensional data set, analyzes the volumetric data, determines the position of the heart in the thoracic cavity and devises the oblique planes for the optimal imaging of the cardiac chambers.…”

Section: Introductionmentioning

confidence: 99%