Automatic Image-Based Cardiac and Respiratory Cycle Synchronization and Gating of Image Sequences

Abstract. 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 imagebased 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.

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“…We compare our approach to the phase correlation technique [5] 1 . Unfortunately, we do not achieve meaningful results for our datasets.…”

Section: Methodsmentioning

confidence: 99%

“…We are only aware of the work of Sundar et al . [5], where an automatic technique for breathing gating in ultrasound is proposed. It bases on the phase correlation technique to estimate the motion between successive frames.…”

Section: Related Workmentioning

confidence: 99%

Manifold Learning for Image-Based Breathing Gating with Application to 4D Ultrasound

Wachinger

Yiğitsoy

Navab

2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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“…Managing synchronization [9] and data frame rates of streaming data -ECG, ultrasound and EM -is a challenge in interventional systems such as the one proposed in this paper. In order to test the proposed motion compensation system on reduced data rates, we tested its robustness to sub-sampling of US image streams in spatial and temporal dimensions.…”

Section: Animal Data Validationmentioning

confidence: 99%

Real-Time 3D Ultrasound Guided Interventional System for Cardiac Stem Cell Therapy with Motion Compensation

Parthasarathy

Hatt

Stanković

et al. 2011

Lecture Notes in Computer Science

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Abstract. This paper describes a clinically translatable interventional guidance platform to improve the accuracy and precision of stem cell injections into a beating heart. The proposed platform overlays live position of an injection catheter onto a fusion of a pre-procedural MR roadmap with real-time 3D transesophageal echocardiography (TEE). Electromagnetic (EM) tracking is used to initialize the fusion. The fusion is intra-operatively compensated for respiratory motion using a novel algorithm that uses peri-operative full volume ultrasound images. Validation of the system on a moving heart phantom produced a landmark registration accuracy of 2.8 ± 1.45mm. Validation on animal in vivo data produced an average registration accuracy of 2.2 ± 1.8 mm; indicating that it is feasible to reliably and robustly fuse the MR road-map with catheter position using 3D ultrasound in a clinical setting.

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“…ECG gating is a common solution; however the ECG signal might be delayed with respect to actual cardiac motion, or, in the worst case, not directly be related at all, in patients with heart arrhythmia. An imagebased technique for detecting periodic motion has therefore been chosen that uses extrema of the cumulated phase shift in the spectral domain of successive image frames [4]. End-diastole ICE frames are tagged with this method and then used for image-based registration to the baseline data 1 .…”

Section: Cardiac Phase Detectionmentioning

confidence: 99%

Towards Guidance of Electrophysiological Procedures with Real-Time 3D Intracardiac Echocardiography Fusion to C-arm CT

Wein

Camus

John

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. This paper describes a novel method for improving the navigation and guidance of devices and catheters in electrophysiology and interventional cardiology procedures using volumetric data fusion. The clinical workflow includes the acquisition and reconstruction of CT data from a C-arm X-ray angiographic system and the real-time acquisition of volumetric ultrasound datasets with a new intracardiac real-time 3D ultrasound catheter. Mono-and multi-modal volumetric registration methods, as well as visualization modes, that are suitable for real-time fusion are described, which are the key components of this work. Evaluation on phantom and in-vivo animal data shows that it is feasible to register and track the motion of real-time 3D intracardiac ultrasound in C-arm CT.

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Automatic Image-Based Cardiac and Respiratory Cycle Synchronization and Gating of Image Sequences

Cited by 26 publications

References 13 publications

Manifold Learning for Image-Based Breathing Gating with Application to 4D Ultrasound

Manifold Learning for Image-Based Breathing Gating with Application to 4D Ultrasound

Real-Time 3D Ultrasound Guided Interventional System for Cardiac Stem Cell Therapy with Motion Compensation

Towards Guidance of Electrophysiological Procedures with Real-Time 3D Intracardiac Echocardiography Fusion to C-arm CT

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