Methods for abdominal respiratory motion tracking

Spińczyk, Dominik; Karwan, Adam; Copik, Marcin

doi:10.3109/10929088.2014.891657

Cited by 21 publications

(19 citation statements)

References 17 publications

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“…The aim of this article is to present a modification of non-rigid Iterative Closest Point Algorithm by introducing an anisotropic noise model for the Time of Flight camera. Our article extends/continues research carried out by [11]. We present the application of the developed non-rigid ICP modification for tracking and predicting abdominal organs displacement in patients.…”

Section: Introductionmentioning

confidence: 55%

“…As mentioned in the introduction, this article is a continuation for work presented by [11]. This approach extends the methods shown in the referenced article by taking the algorithms of both [2] and [3] and creating a method of registering point clouds that would allow the model to remain non-rigid but would also take the point localization error into consideration.…”

Section: Methodsmentioning

confidence: 92%

“…As mentioned, the source cloud covariance matrix has to be recalculated after each iteration of algorithm because of updated positions of source cloud points [12]. Due to that, the cross-covariance matrix has must also be updated [11]. Once the weighting matrix has been found for an iteration [13], it can be used to modify the corresponding points distances [15].…”

Section: Methodsmentioning

confidence: 99%

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Anisotropic non-rigid Iterative Closest Point Algorithm for respiratory motion abdominal surface matching

Spińczyk

Bas

2019

BioMed Eng OnLine

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Surface registration is a one of the crucial and actual problems of computer aided surgery. This paper presents the modification of the non-rigid Iterative Closest Point Algorithm which takes into account an anisotropic noise model and landmarks as guided correspondence at the transformation step in every iteration. The presented approach was validated on human abdominal briefing surface data from a time-of-flight camera. We took the median of the resulting measures and the outcome is presented: the median of means of surfaces distance was at the same level for both variants of the ICP algorithm and is comparable with the isotropic variant, the median of mean landmark position errors decreased by 0.93 units (over 20% improvement) and the median of percentage of single correspondences in target point cloud increased by 11.96%. The results showed that the introduction of the anisotropic model of noise for the ToF camera allows for the improvement the percentage of target cloud points which had only one correspondent over 10% impartment and additional weighting of markers also improves the measure of the quality of finding real correspondents over 20% improvement. In the examined dataset, where the average initial distance between the clouds of points in the inspiratory and expiration is equal to approx. 7.5 mm, a more than 10% improvement in the quality of the correspondence improves the accuracy of matching the surface within 1 mm which is a significant value in application of minimally invasive image guided interventions.

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Section: Introductionmentioning

confidence: 55%

Section: Methodsmentioning

confidence: 92%

Section: Methodsmentioning

confidence: 99%

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Anisotropic non-rigid Iterative Closest Point Algorithm for respiratory motion abdominal surface matching

Spińczyk

Bas

2019

BioMed Eng OnLine

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“…Optical imaging is a modality of choice for surgery assistance: it is inexpensive, it has high temporal and spatial resolution and, as opposed to magnetic resonance or computational tomography, it implies only limited constraints on the surgical material and room. This is why even simple devices such as true color camera are often used for assistance or monitoring during surgical intervention or radiotherapy for a variety of body parts: brain [24], heart [27] or abdomen [34] for example. Analysis of videos from these camera can be used for brain surface strain estimation [15], brain surface 3D reconstruction [18,22], to investigate the deformation of the exposed cortical surface [16], to measure hemodynamic response [24,38,35,33,20].…”

Section: Introductionmentioning

confidence: 99%

Repetitive motion compensation for real time intraoperative video processing

Sdika

Alston

Rousseau

et al. 2019

Medical Image Analysis

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In this paper, we present a motion compensation algorithm dedicated to video processing during neurosurgery. After craniotomy, the brain surface undergoes a repetitive motion due to the cardiac pulsation. This motion as well as potential video camera motion prevent accurate video analysis. We propose a dedicated motion model where the brain deformation is described using a linear basis learned from a few initial frames of the video. As opposed to other works using linear basis for the flow, the camera motion is explicitly accounted in the transformation model. Despite the nonlinear nature of our model, all the motion parameters are robustly estimated all at once, using only one singular value decomposition (SVD), making our procedure computationally efficient. A Lagrangian specification of the flow field ensures the stability of the method. Experiments on in vivo data are presented to evaluate the capacity of the method to cope with occlusion or camera motion. The method we propose satisfies the intraoperative constraints: it is robust to surgical tools occlusions, it works in real time, and it is able to handle large camera viewpoint changes.

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“…The noninvasiveness requirement is being overcome by the surface matching techniques replacing marker matching approaches [ 20 ]. The ToF-camera is a device suitable for surface tracking and matching to the preprocessed data [ 21 ], and several attempts to employ it in an intra-operative computer aided diagnosis and therapy have been reported recently [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

et al. 2016

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PurposeA growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning. However, new modalities, like the time-of-flight (ToF) sensors, widely explored in non-medical fields are powerful and have the potential to become a part of computer aided surgery set-up. Connection of different acquisition systems promises to provide a valuable support for operating room procedures. Therefore, the detailed analysis of the accuracy of such multi-sensors positioning systems is needed.MethodsWe present the system combining pre-operative CT series with intra-operative ToF-sensor and optical tracker point clouds. The methodology contains: optical sensor set-up and the ToF-camera calibration procedures, data pre-processing algorithms, and registration technique. The data pre-processing yields a surface, in case of CT, and point clouds for ToF-sensor and marker-driven optical tracker representation of an object of interest. An applied registration technique is based on Iterative Closest Point algorithm.ResultsThe experiments validate the registration of each pair of modalities/sensors involving phantoms of four various human organs in terms of Hausdorff distance and mean absolute distance metrics. The best surface alignment was obtained for CT and optical tracker combination, whereas the worst for experiments involving ToF-camera.ConclusionThe obtained accuracies encourage to further develop the multi-sensors systems. The presented substantive discussion concerning the system limitations and possible improvements mainly related to the depth information produced by the ToF-sensor is useful for computer aided surgery developers.

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Methods for abdominal respiratory motion tracking

Cited by 21 publications

References 17 publications

Anisotropic non-rigid Iterative Closest Point Algorithm for respiratory motion abdominal surface matching

Anisotropic non-rigid Iterative Closest Point Algorithm for respiratory motion abdominal surface matching

Repetitive motion compensation for real time intraoperative video processing

Time-Of-Flight Camera, Optical Tracker and Computed Tomography in Pairwise Data Registration

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