Model-Based Sparse-to-Dense Image Registration for Realtime Respiratory Motion Estimation in Image-Guided Interventions

Ha, In Young; Wilms, Matthias; Handels, Heinz; Heinrich, Mattias P.

doi:10.1109/tbme.2018.2837387

Cited by 39 publications

(32 citation statements)

References 36 publications

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“…It goes without saying that strategies to mitigate tissue movement's impact on spatially encoded mass spectrometry results must be employed prior to clinical deployment. Here, utilization of electromagnetic tracking coupled with feature-based deformable registration synchronized with tissue movement (such as pulse or respiratory motions), used in imaging, 43 as a form of image stabilization may constitute attractive avenues to address this important point. Nevertheless, the low millimeter accuracy of the spatially encoded mass spectrometry method demonstrated in this study is a key attribute that can lead to a reduction in resection induced neurologic morbidity for tumors situated in close proximity to critical nervous system structures.…”

Section: Caveats and Future Directionsmentioning

confidence: 99%

In situ tissue pathology from spatially encoded mass spectrometry classifiers visualized in real time through augmented reality

et al. 2020

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Section: Caveats and Future Directionsmentioning

confidence: 99%

In situ tissue pathology from spatially encoded mass spectrometry classifiers visualized in real time through augmented reality

et al. 2020

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“…A motion model may be constructed from 4D MR or CT images, which can be acquired before therapy and the image data binned according to a measurement of the breathing cycle. As model input, 2D realtime images can be used to estimate 3D motion [214][215][216][217][218][219], to overcome slow MR image acquisitions [220] or system latencies using filters [221,222] or artificial neural networks [223,224].…”

Section: Tracking and Motion Modelingmentioning

confidence: 99%

Beyond T2 and 3T: New MRI techniques for clinicians

Knowles

Friedrich

Fischer

et al. 2019

Clinical and Translational Radiation Oncology

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“…In practice, the requirement for a physical deformation mechanism and tissue properties limits the application of biomechanical simulations. According to the application range, the existing whole organ motion models can be divided into cross‐population models 15,16 and patient‐specific models 17–19 . Although cross‐population models can reduce the cost of patient‐specific modeling, they ignore the dissimilarity of breathing dynamics among patients and sacrifice accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Ha et al. also built a PCA‐based motion model for dense motion field estimation in US‐ or MR‐guided interventions, and the model parameters are optimized by fitting the model to the sparse feature points tracked by the block‐matching technique 19 . The models in Refs.…”

Section: Introductionmentioning

confidence: 99%

Diffeomorphic respiratory motion estimation of thoracoabdominal organs for image‐guided interventions

et al. 2021

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Purpose Percutaneous image‐guided interventions are commonly used for the diagnosis and treatment of cancer. In practice, physiological breathing‐induced motion increases the difficulty of accurately inserting needles into tumors without impairing the surrounding vital structures. In this work, we propose a data‐driven patient‐specific hierarchical respiratory motion estimation framework to accurately estimate the position of a tumor and surrounding vital tissues in real time. Methods The motion of optical markers attached to the chest or abdomen skin is used as a surrogate signal to estimate tumor motion based on ɛ‐support vector regression (ɛ‐SVR). With the estimated tumor motion as the input, a novel respiratory motion model is developed to estimate the diffeomorphic deformation field of the whole organ (liver or lung) without intraoperative, iterative optimization computations. The respiratory motion model of the whole organ is established in Lie algebra space based on the kriging algorithm to ensure that the estimated deformation field is diffeomorphic, optimal, and unbiased. Preoperative prior knowledge for modeling the motion of whole organs is obtained by deformation registration between four‐dimensional computed tomography (4D CT) images using a hybrid diffeomorphic registration method. Results and Conclusions Experimental results on an in vivo beagle dog show that the minimum value of the determinant of the Jacobian of the estimated deformation field is greater than zero, so the estimated deformation field of the whole liver with our method is diffeomorphic. The mean position error of the tumor is 1.2 mm corresponding to a mean accuracy improvement of 76.5%, and the mean position error of the whole liver is 2.1 mm, corresponding to a mean accuracy improvement of 37.9%. The experimental results based on public human subject data show that the mean position error of the tumor is 1.1 mm, corresponding to a mean accuracy improvement of 83.1%, and the mean position error of the whole lung is 2.1 mm, corresponding to a mean accuracy improvement of 41.4%. The positioning errors for the tumor and whole organ are hierarchical and consistent with clinical demand.

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Model-Based Sparse-to-Dense Image Registration for Realtime Respiratory Motion Estimation in Image-Guided Interventions

Cited by 39 publications

References 36 publications

In situ tissue pathology from spatially encoded mass spectrometry classifiers visualized in real time through augmented reality

In situ tissue pathology from spatially encoded mass spectrometry classifiers visualized in real time through augmented reality

Beyond T2 and 3T: New MRI techniques for clinicians

Diffeomorphic respiratory motion estimation of thoracoabdominal organs for image‐guided interventions

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