A Localized Statistical Motion Model as a Reproducing Kernel for Non-rigid Image Registration

Jud, Christoph; Giger, Alina; Sandkühler, Robin; Cattin, Philippe C.

doi:10.1007/978-3-319-66185-8_30

Cited by 10 publications

(10 citation statements)

References 14 publications

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“…The US imaging plane was visually matched with the preceding imaging plane as good as possible. The MR images were aligned based on diffeomorphic image registration of two end-exhalation master volumes and inverse displacement field warping [10,3].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study

Giger

Jud

Nguyen

et al. 2019

Lecture Notes in Computer Science

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Motion management strategies are crucial for radiotherapy of mobile tumours in order to ensure proper target coverage, save organs at risk and prevent interplay effects. We present a feasibility study for an inter-fractional, patient-specific motion model targeted at active beam scanning proton therapy. The model is designed to predict dense lung motion information from 2D abdominal ultrasound images. In a pretreatment phase, simultaneous ultrasound and magnetic resonance imaging are used to build a regression model. During dose delivery, abdominal ultrasound imaging serves as a surrogate for lung motion prediction. We investigated the performance of the motion model on five volunteer datasets. In two cases, the ultrasound probe was replaced after the volunteer has stood up between two imaging sessions. The overall mean prediction error is 2.9 mm and 3.4 mm after repositioning and therefore within a clinically acceptable range. These results suggest that the ultrasound-based regression model is a promising approach for interfractional motion management in radiotherapy.

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Section: Experiments and Resultsmentioning

confidence: 99%

Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study

Giger

Jud

Nguyen

et al. 2019

Lecture Notes in Computer Science

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“…Therefore, it is suitable for clinical applications where the burden of data acquisition is high. A method for estimating the respiratory deformations of the lungs using the advantages of the kernel method has been reported by Jud et al (2017).…”

Section: Discussionmentioning

confidence: 99%

“…In deformable image registration in thoracic contexts, respiratory motion (Rietzel and Chen, 2006;Wilms et al, 2016;Yin et al, 2011) and patient posture (Nakamoto et al, 2007) have been the main focus of investigation. For the analysis of respiratory motion, image-based lung modeling techniques (Fuerst et al, 2015;Nakao et al, 2007) and statistical modeling techniques (Ehrhardt et al, 2011;Jud et al, 2017) have been developed previously.…”

Section: Introductionmentioning

confidence: 99%

“…However, considering the future clinical applications, a method that can be implemented in any computing environment is desired. In a study of non-rigid image registration of the lung during breathing (Jud et al, 2017), the authors formulated the statistical motion as kernel and integrated it into a parametric non-rigid registration. We also consider that a kernel method could be utilized to estimate deformations of the liver (Yamamoto et al, 2017b).…”

Section: Introductionmentioning

confidence: 99%

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Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks

Yamamoto,

Nakao,

Ohzeki

et al. 2021

Preprint

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In video-assisted thoracoscopic surgeries, successful procedures of nodule resection are highly dependent on the precise estimation of lung deformation between the inflated lung in the computed tomography (CT) images during preoperative planning and the deflated lung in the treatment views during surgery. Lungs in the pneumothorax state during surgery have a large volume change from normal lungs, making it difficult to build a mechanical model. The purpose of this study is to develop a deformation estimation method of 3D surface of a deflated lung from a few partial observations. To estimate deformations for a largely deformed lung, a kernel regression-based solution was introduced. The proposed method used a few landmarks to capture the partial deformation between the 3D surface mesh obtained from preoperative CT and the intraoperative anatomical positions. The deformation for each vertex of the entire mesh model was estimated per-vertex as a relative position from the landmarks. The landmarks were placed in the anatomical position of the lung's outer contour. The method was applied on nine datasets of the left lungs of live Beagle dogs. Contrast-enhanced CT images of the lungs were acquired. The proposed method achieved a local positional error of vertices of 2.74 mm, Hausdorff distance of 6.11 mm, and Dice similarity coefficient of 0.94. Moreover, the proposed method could estimate lung deformations from a small number of training cases and a small observation area. This study contributes to data-driven modeling of pneumothorax deformation of the lung.

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“…In the image-based analysis of lung modeling, respiratory motion has been extensively investigated [3]- [8]. However, be-cause of difficulties in both image registration and regularization, few studies have modelled the pneumothorax-associated deformation that can occur between the preoperative and intraoperative lung states.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Heterogeneity of Pneumothorax-associated Deformation using Model-based Registration

Nakao,

Kobayashi,

Tokuno

et al. 2020

Preprint

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Recent advances in imaging techniques have enabled us to visualize lung tumors or nodules in early-stage cancer. However, the positions of nodules can change because of intraoperative lung deflation, and the modeling of pneumothorax-associated deformation remains a challenging issue for intraoperative tumor localization. In this study, we introduce spatial and geometric analysis methods for inflated/deflated lungs and discuss heterogeneity in pneumothorax-associated deformation. Contrastenhanced CT images simulating intraoperative conditions were acquired from live Beagle dogs. Deformable mesh registration techniques were designed to map the surface and subsurface tissues of lung lobes. The developed framework addressed local mismatches of bronchial tree structures and achieved stable registration with a Hausdorff distance of less than 1 mm and a target registration error of less than 5 mm. Our results show that the strain of lung parenchyma was 35% higher than that of bronchi, and that subsurface deformation in the deflated lung is heterogeneous.

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A Localized Statistical Motion Model as a Reproducing Kernel for Non-rigid Image Registration

Cited by 10 publications

References 14 publications

Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study

Inter-fractional Respiratory Motion Modelling from Abdominal Ultrasound: A Feasibility Study

Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks

Analysis of Heterogeneity of Pneumothorax-associated Deformation using Model-based Registration

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