Robust initialisation for single-plane 3D CT to 2D fluoroscopy image registration

Akter, Masuma; Lambert, Andrew; Pickering, Mark; Scarvell, Jennie M.; Smith, Paul N.

doi:10.1080/21681163.2014.897649

Cited by 6 publications

(4 citation statements)

References 44 publications

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“…Kinematics were calculated by a 2D-to-3D image registration algorithm using bespoke software (Orthovis, UNSW, Canberra 24 ). Orthovis precision was previously reported for in-plane (sagittal) registration as 0.2 mm for translation and 0.3°for rotation, while the out-of-plane precision was 0.9 mm and 0.5°.…”

Section: Kinematicsmentioning

confidence: 99%

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Shape is only a weak predictor of deep knee flexion kinematics in healthy and osteoarthritic knees

Lynch

Perriman

Scarvell

et al. 2020

Journal Orthopaedic Research

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Tibiofemoral shape influences knee kinematics but little is known about the effect of shape on deep knee flexion kinematics. The aim of this study was to examine the association between tibiofemoral joint shape and kinematics during deep kneeling in patients with and without osteoarthritis (OA). Sixty‐one healthy participants and 58 patients with end‐stage knee OA received a computed tomography (CT) of their knee. Participants completed full flexion kneeling while being imaged using single‐plane fluoroscopy. Six‐degree‐of‐freedom kinematics were measured by registering a three‐dimensional (3D)‐static CT onto 2D‐dynamic fluoroscopic images. Statistical shape modeling and bivariate functional principal component analysis (bfPCA) were used to describe variability in knee shape and kinematics, respectively. Random‐forest‐regression models were created to test the ability of shape to predict kinematics controlling for body mass index, sex, and group. The first seven modes of the shape model up to three modes of the bfPCAs captured more than 90% of the variation. The ability of the random forest models to predict kinematics from shape was low, with no more than 50% of the variation being explained in any model. Furthermore, prediction errors were high, ranging between 24.2% and 29.4% of the data. Variations in the bony morphology of the tibiofemoral joint were weakly associated with the kinematics of deep knee flexion. The models only explained a small amount of variation in the data with high error rates indicating that additional predictors need to be identified. These results contribute to the clinical understanding of knee kinematics and potentially the expectations placed on high‐flexion total knee replacement design.

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Section: Kinematicsmentioning

confidence: 99%

“…Orthovis precision was previously reported for in-plane (sagittal) registration as 0.2 mm for translation and 0.3°for rotation, while the out-of-plane precision was 0.9 mm and 0.5°. 24 Briefly, the registration aimed to find the best image match between the 3D CT in the 2D fluoroscopy space. This achieved using a number of steps:…”

Section: Kinematicsmentioning

confidence: 99%

Shape is only a weak predictor of deep knee flexion kinematics in healthy and osteoarthritic knees

Lynch

Perriman

Scarvell

et al. 2020

Journal Orthopaedic Research

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“…So the proposed work employed three different similarity measure techniques i.e., Binary Image Matching (BIM) technique, Normalized Cross Correlation (NCC), and Dice Coefficient (DC) similarity, to evaluate the best cost function for optimizing the multi-modal 3D to 2D registration procedure. The proposed work also defines the sensitivity analysis of the range of initial displacements in the optimization algorithm [11]. The registration accuracy of the proposed algorithm is measured in terms of mean Target Registration Error (mTRE), mean Iteration times and success rate [12].…”

Section: Introductionmentioning

confidence: 99%

Evaluating similarity measure for multimodal 3D to 2D registration

Kiran¹,

Naik²,

Bhat³

et al. 2023

Biomed. Phys. Eng. Express

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The 3D to 2D registration technique in spine surgery is vital to aid surgeons in avoiding the wrong site surgery by estimating the vertebral pose. The vertebral poses are estimated by generating the spatial correspondence relationship between pre-operative MR with intra-operative X-ray images, then evaluated using a similarity measure. Different similarity measures are used in 3D to 2D registration techniques to assess the spatial correspondence between the pre-operative and intra-operative images. However, to evaluate the registration performance of the similarity measures, the proposed framework employs three different similarity measures: Binary Image Matching, Dice Coefficients, and Normalized Cross-correlation technique to compare the images based on pixel positions. The registration accuracy of the proposed similarity measures is compared based on the mean Target Registration Error, mean Iteration Times, and success rate. In the absence of simulated test images, the experiment is conducted on the simulated AP and Lateral test images. The experiment conducted on the simulated test images shows that all three similarity measures work well for the feature based 3D to 2D registration in that BIM gives better results. The experiment also indicates high registration accuracy when the initial displacements are varied up to ±20mm and ±10° of the translational and rotational parameters, respectively, for three similarity measures.

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“…More recently CT and MRI have been used to provide a 3-dimensional model, which when registered to fluoroscopy, provides 4-dimensional analysis (Li et al 2005, Hamai et al 2009, Pickering et al 2009, Koga 2015. Fluoroscopy units are now capable of capture rates of up to 250 frames per second (You et al 2001) and image registration algorithms can provide precision of less than one millimetre and one degree (DeFrate et al 2006, Akter et al 2015, Zeighami et al 2017. Computer algorithms for 4D CT are also being developed (Alta et al 2012).…”

Section: Introductionmentioning

confidence: 99%

Kinematics of knees with osteoarthritis show reduced lateral femoral roll-back and maintain an adducted position. A systematic review of research using medical imaging

Scarvell

Galvin

Perriman

et al. 2018

Journal of Biomechanics

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Robust initialisation for single-plane 3D CT to 2D fluoroscopy image registration

Cited by 6 publications

References 44 publications

Shape is only a weak predictor of deep knee flexion kinematics in healthy and osteoarthritic knees

Shape is only a weak predictor of deep knee flexion kinematics in healthy and osteoarthritic knees

Evaluating similarity measure for multimodal 3D to 2D registration

Kinematics of knees with osteoarthritis show reduced lateral femoral roll-back and maintain an adducted position. A systematic review of research using medical imaging

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