Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
On Manifold Structure of Cardiac MRI Data: Application to Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Automatic Image-Based Cardiac and Respiratory Cycle Synchronization and Gating of Image Sequences
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Efficient Large Deformation Registration via Geodesics on a Learned Manifold of Images
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Manifold learning for patient position detection in MRI
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Temporal groupwise registration for motion modeling
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Random forest-based manifold learning for classification of imaging data in dementia
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Hierarchical manifold learning
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Sparse projections of medical images onto manifolds
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Breathing motion leads to a significant displacement and deformation of organs in the abdominal region. This makes the detection of the breathing phase for numerous applications necessary. We propose a new, purely image-based respiratory gating method for ultrasound. Further, we use this technique to provide a solution for breathing affected 4D ultrasound acquisitions with a wobbler probe. We achieve the gating with Laplacian eigenmaps, a manifold learning technique, to determine the low-dimensional manifold embedded in the high-dimensional image space. Since Laplacian eigenmaps assign each ultrasound frame a coordinate in low-dimensional space by respecting the neighborhood relationship, they are well suited for analyzing the breathing cycle. For the 4D application, we perform the manifold learning for each angle, and consecutively, align all the local curves and perform a curve fitting to achieve a globally consistent breathing signal. We performed the image-based gating on several 2D and 3D ultrasound datasets over time, and quantified its very good performance by comparing it to measurements from an external gating system.