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Manifold learning for image-based breathing gating in ultrasound and MRI.
Med Image Anal. 2012 May; 16(4):806-18.MI

Abstract

Respiratory motion is a challenging factor for image acquisition and image-guided procedures in the abdominal and thoracic region. In order to address the issues arising from respiratory motion, it is often necessary to detect the respiratory signal. In this article, we propose a novel, purely image-based retrospective respiratory gating method for ultrasound and MRI. Further, we apply this technique to acquire breathing-affected 4D ultrasound with a wobbler probe and, similarly, to create 4D MR with a slice stacking approach. 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 to each image frame a coordinate in low-dimensional space by respecting the neighborhood relationship, they are well suited for analyzing the breathing cycle. We perform the image-based gating on several 2D and 3D ultrasound datasets over time, and quantify its very good performance by comparing it to measurements from an external gating system. For MRI, we perform the manifold learning on several datasets for various orientations and positions. We achieve very high correlations by a comparison to an alternative gating with diaphragm tracking.

Authors+Show Affiliations

Computer Aided Medical Procedures (CAMP), Technische Universität München, München, Germany. wachinge@in.tum.deNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

22226466

Citation

Wachinger, Christian, et al. "Manifold Learning for Image-based Breathing Gating in Ultrasound and MRI." Medical Image Analysis, vol. 16, no. 4, 2012, pp. 806-18.
Wachinger C, Yigitsoy M, Rijkhorst EJ, et al. Manifold learning for image-based breathing gating in ultrasound and MRI. Med Image Anal. 2012;16(4):806-18.
Wachinger, C., Yigitsoy, M., Rijkhorst, E. J., & Navab, N. (2012). Manifold learning for image-based breathing gating in ultrasound and MRI. Medical Image Analysis, 16(4), 806-18. https://doi.org/10.1016/j.media.2011.11.008
Wachinger C, et al. Manifold Learning for Image-based Breathing Gating in Ultrasound and MRI. Med Image Anal. 2012;16(4):806-18. PubMed PMID: 22226466.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Manifold learning for image-based breathing gating in ultrasound and MRI. AU - Wachinger,Christian, AU - Yigitsoy,Mehmet, AU - Rijkhorst,Erik-Jan, AU - Navab,Nassir, Y1 - 2011/12/08/ PY - 2011/06/10/received PY - 2011/11/27/revised PY - 2011/11/28/accepted PY - 2012/1/10/entrez PY - 2012/1/10/pubmed PY - 2012/8/8/medline SP - 806 EP - 18 JF - Medical image analysis JO - Med Image Anal VL - 16 IS - 4 N2 - Respiratory motion is a challenging factor for image acquisition and image-guided procedures in the abdominal and thoracic region. In order to address the issues arising from respiratory motion, it is often necessary to detect the respiratory signal. In this article, we propose a novel, purely image-based retrospective respiratory gating method for ultrasound and MRI. Further, we apply this technique to acquire breathing-affected 4D ultrasound with a wobbler probe and, similarly, to create 4D MR with a slice stacking approach. 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 to each image frame a coordinate in low-dimensional space by respecting the neighborhood relationship, they are well suited for analyzing the breathing cycle. We perform the image-based gating on several 2D and 3D ultrasound datasets over time, and quantify its very good performance by comparing it to measurements from an external gating system. For MRI, we perform the manifold learning on several datasets for various orientations and positions. We achieve very high correlations by a comparison to an alternative gating with diaphragm tracking. SN - 1361-8423 UR - https://www.unboundmedicine.com/medline/citation/22226466/Manifold_learning_for_image_based_breathing_gating_in_ultrasound_and_MRI_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1361-8415(11)00170-8 DB - PRIME DP - Unbound Medicine ER -