Quantitative<i>in vivo</i>analysis of the kinematics of carpal bones from three-dimensional CT images using a deformable surface model and a three-dimensional matching technique

Snel, J.G.; Venema, Henk W.; Moojen, Thybout M.; Ritt, Marco J.P.F.; Grimbergen, C.A.; Heeten, Gerard J. den

doi:10.1118/1.1289896

Cited by 48 publications

(28 citation statements)

References 23 publications

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“…8). The grey-value registration results are consistent with those reported by Snel et al [8]. A onesample t-test on the difference between the mean results produced by the two methods confirmed the tissue-classification translational accuracy improvement was statistically significant .…”

Section: ) In Vitro Accuracy and Robustness Experimentsupporting

confidence: 90%

“…Grey-value registration implementation Grey-value registration was implemented as in Snel et al [8], with several modifications to increase accuracy. First, all the points, as opposed to a random 10%, with a greyscale value greater than 600 Hounsfield units of each image were used in the calculation of the rootmean-square cost function.…”

Section: B Experiments 1) In Vitro Accuracy and Robustness Experimentmentioning

confidence: 99%

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Super-resolution registration using tissue-classified distance fields

Marai

Laidlaw

Crisco

2006

IEEE Trans. Med. Imaging

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Abstract-We present a method for registering the position and orientation of bones across multiple computed-tomography (CT) volumes of the same subject. The method is subvoxel accurate, can operate on multiple bones within a set of volumes, and registers bones that have features commensurate in size to the voxel dimension. First, a geometric object model is extracted from a reference volume image. We use then unsupervised tissue classification to generate from each volume to be registered a super-resolution distance field-a scalar field that specifies, at each point, the signed distance from the point to a material boundary. The distance fields and the geometric bone model are finally used to register an object through the sequence of CT images. In the case of multiobject structures, we infer a motion-directed hierarchy from the distance-field information that allows us to register objects that are not within each other's capture region.We describe a validation framework and evaluate the new technique in contrast with grey-value registration. Results on human wrist data show average accuracy improvements of 74% over greyvalue registration. The method is of interest to any intrasubject, same-modality registration applications where subvoxel accuracy is desired.

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Section: ) In Vitro Accuracy and Robustness Experimentsupporting

confidence: 90%

Section: B Experiments 1) In Vitro Accuracy and Robustness Experimentmentioning

confidence: 99%

Super-resolution registration using tissue-classified distance fields

Marai

Laidlaw

Crisco

2006

IEEE Trans. Med. Imaging

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“…A number of studies have sought to represent the carpal kinematics using CT or MR data, mainly concentrating on representing 'average' kinematics over a small number of individuals (e.g. [1], [2]). More recently, Van deGiessen et al [3] presented a 3D rigid registration method based on segmented meshes, which aims to build SM of carpal bones.…”

Section: Introductionmentioning

confidence: 99%

Inferring 3D Kinematics of Carpal Bones from Single View Fluoroscopic Sequences

Chen

Graham

Hutchinson³

et al. 2011

Lecture Notes in Computer Science

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Abstract. We present a novel framework for inferring 3D carpal bone kinematics and bone shapes from a single view fluoroscopic sequence. A hybrid statistical model representing both the kinematics and shape variation of the carpal bones is built, based on a number of 3D CT data sets obtained from different subjects at different poses. Given a fluoroscopic sequence, the wrist pose, carpal bone kinematics and bone shapes are estimated iteratively by matching the statistical model with the 2D images. A specially designed cost function enables smoothed parameter estimation across frames. We have evaluated the proposed method on both simulated data and real fluoroscopic sequences. It was found that the relative positions between carpal bones can be accurately estimated, which is potentially useful for detection of conditions such as scapholunate dissociation.

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“…The set of mesh points Q i that represent the i th bone shape are obtained by substituting the estimated SSM parameter b q into equation (8). Subsequently, the 2D projection that represents the current estimated 3D pose of the carpal bones can be generated using equation (1).…”

Section: Optimisationmentioning

confidence: 99%

“…[1], [2]). Van de Giessen et al [3] presented a 3D rigid registration method based on segmented meshes, which aims to build a SM of carpal bones.…”

mentioning

confidence: 99%

Automatic Inference and Measurement of 3D Carpal Bone Kinematics From Single View Fluoroscopic Sequences

Chen

Graham

Hutchinson

et al. 2013

IEEE Trans. Med. Imaging

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.ukCopyright (c) 2011 IEEE. Personal use is permitted. For any other purposes, permission must be obtained from the IEEE by emailing pubs-permissions@ieee.org. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-We present a novel framework for estimating the 3D poses and shapes of the carpal bones from single view fluoroscopic sequences. A hybrid statistical model representing both the pose and shape variation of the carpal bones is built, based on a number of 3D CT data sets obtained from different subjects at different poses. Given a fluoroscopic sequence, the wrist pose, carpal bone pose and bone shapes are estimated iteratively by matching the statistical model with the 2D images. A specially designed cost function enables smoothed parameter estimation across frames and constrains local bone pose with a penalty term. We have evaluated the proposed method on both simulated data and real fluoroscopic sequences and demonstrated that the relative poses of carpal bones can be accurately estimated. One condition that may be assessed using this measurement is dissociation, where the distance between the bones is larger than normal. Scaphoid-Lunate dissociation is one of the most common of these. The error of the measured 3D Scaphoid-Lunate distances were 0.75 ± 0.50 mm for simulated data (25 subjects) and 0.93 ± 0.47 mm for real data (15 subjects). We also propose a method for constructing a 'standard' pathology measurement tool for automatically detecting Scaphoid-Lunate dissociation conditions, based on single-view fluoroscopic sequences. For the simulated data, it produced 100% sensitivity and specificity. For the real data, it achieved 83% sensitivity and 78% specificity.

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Quantitativein vivoanalysis of the kinematics of carpal bones from three-dimensional CT images using a deformable surface model and a three-dimensional matching technique

Cited by 48 publications

References 23 publications

Super-resolution registration using tissue-classified distance fields

Super-resolution registration using tissue-classified distance fields

Inferring 3D Kinematics of Carpal Bones from Single View Fluoroscopic Sequences

Automatic Inference and Measurement of 3D Carpal Bone Kinematics From Single View Fluoroscopic Sequences

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