Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images

Prakosa, Adityo; Sermesant, Maxime; Delingette, Hervé; Marchesseau, Stéphanie; Saloux, Éric; Allain, Philippe; Villain, Nicolas; Ayache, Nicholas

doi:10.1109/tmi.2012.2220375

Cited by 41 publications

(70 citation statements)

References 47 publications

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“…Moreover, clutter noise typically makes tracking and segmentation challenging, particularly towards the apex, but this was not included in the current simulation model. Ad- More realistic approaches could be including real images for learning ultrasound artifacts [60], [61] and incorporating nonlinear propagation effects [62]. Second, regarding mechanical deformation patterns, several improvements could be made towards more realistic simulations.…”

Section: Discussionmentioning

confidence: 99%

3D Strain Assessment in Ultrasound (Straus): A Synthetic Comparison of Five Tracking Methodologies

Craene

Marchesseau

Heyde

et al. 2013

IEEE Trans. Med. Imaging

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Section: Discussionmentioning

confidence: 99%

3D Strain Assessment in Ultrasound (Straus): A Synthetic Comparison of Five Tracking Methodologies

Craene

Marchesseau

Heyde

et al. 2013

IEEE Trans. Med. Imaging

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“…Synthetic image generation is a research tool which is extensively used in image processing testing and evaluation [20,21]. A synthetic database allows the economical generation of images, which represent scenarios that are difficult or impossible to obtain in real data and provides a direct control of image parameters [22].…”

Section: Discussionmentioning

confidence: 99%

Test Image Generation using Segmental Symbolic Evaluation

Jameel¹,

Lin²

2014

IJNDC

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Image processing applications have played a vital role in modern life and they are required to be well tested due to their significance and human dependence on them. Testing of image processing application is difficult due to complex nature of images in terms of their generation and evaluation. The presented technique is first of its type to generate test images based on symbolic evaluation of program under test. The idea is based on the fact that, neighboring image operations are applied by selecting a segment of image pixels called a window, and iterated by sliding window over entire image. We imitate neighboring operations using symbolic values for the pixels rather than concrete values. The path constraint is extracted for each path in the program under test and solved for concrete solutions. Test images are generated based on solution of path constraints for each identified path. We have tested the proposed scheme on different programs and the results show that test images are successfully generated for each path to ensure the path coverage of the program under test and identifying infeasible paths.

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“…The barycentric template is therefore used as a prior on the cardiac motion for which we do not perform regularization. The method was evaluated using a synthetic time serie of T = 30 cardiac image frames [9], so that we have ground truth meshes along the sequence allowing us to estimate the accuracy of the registration. First, we find the optimal references by minimizing the energy in Eq.…”

Section: Barycentric Log-demons Algorithmmentioning

confidence: 99%

Barycentric Subspace Analysis: A New Symmetric Group-Wise Paradigm for Cardiac Motion Tracking

Rohé

Sermesant

Pennec

2016

Lecture Notes in Computer Science

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Abstract. In this paper, we propose a novel approach to study cardiac motion in 4D image sequences. Whereas traditional approaches rely on the registration of the whole sequence with respect to the first frame usually corresponding to the end-diastole (ED) image, we define a more generic basis using the barycentric subspace spanned by a number of references images of the sequence. These subspaces are implicitly defined as the locus of points which are weighted Karcher means of k + 1 references images. We build such subspace on the cardiac motion images, to get a Barycentric Template that is no longer defined by a single image but parametrized by coefficients: the barycentric coordinates. We first show that the barycentric coordinates -the coefficients of the projection of the motion during a cardiac sequence -define a meaningful signature for group-wise analysis of dynamics and can efficiently separate two populations. Then, we use the barycentric template as a prior for regularization in cardiac motion tracking, efficiently reducing the error of tracking between end-systole and end-diastole by almost 40% as well as the error of the evaluation of the ejection fraction. Finally, to best exploit the fact that multiple reference images allow to reduce the registration displacement, we derived a symmetric and transitive registration that can be used both for frame-to-frame and frame-to-reference registration and further improves the accuracy of the registration.

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Generation of Synthetic but Visually Realistic Time Series of Cardiac Images Combining a Biophysical Model and Clinical Images

Cited by 41 publications

References 47 publications

3D Strain Assessment in Ultrasound (Straus): A Synthetic Comparison of Five Tracking Methodologies

3D Strain Assessment in Ultrasound (Straus): A Synthetic Comparison of Five Tracking Methodologies

Test Image Generation using Segmental Symbolic Evaluation

Barycentric Subspace Analysis: A New Symmetric Group-Wise Paradigm for Cardiac Motion Tracking

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