2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models

Baka, Nóra; Kaptein, Bart L; Bruijne, Marleen de; Walsum, Theo van; Giphart, J. Erik; Niessen, Wiro J.; Lelieveldt, Boudewijn P. F.

doi:10.1016/j.media.2011.04.001

Cited by 151 publications

(108 citation statements)

References 20 publications

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“…One of the main claims of novelty over other work such as [1] According to the reviewer comment, we modified the text in paragraph 2.4.1 section as follows:…”

Section: In Paragraph 22 We Modified the Text As Followsmentioning

confidence: 99%

“…Overall the paper describes a method which seems to work (on the synthetic radiographs it was tested on), but does not seem to be a particularly significant advance over existing published methods (such as [1]). Significant information about the test set appears to be missing.…”

Section: In Paragraph 22 We Modified the Text As Followsmentioning

confidence: 99%

“…(will be inserted by the editor) the design of prosthetic components [1,2]. In case of severe osteoarthritis, that are eligible for joint implant surgery, in vivo pre-operatory knee kinematics is fundamental to understand the relative motion between the three joint bones.…”

Section: Fig8 : "Exted" -> "Extend"mentioning

confidence: 99%

“…Traditional CT and MRI provide an accurate evaluation of the morphology of the knee, but are limited to static positioning of the patient. A great number of pathologies, such as patellofemoral pain and osteoarthritis require a dynamic evaluation of the knee motion [5,1,3].…”

Section: Fig8 : "Exted" -> "Extend"mentioning

confidence: 99%

“…Their method was based on manual segmentation of the bone contour from X-ray images. In [1], the authors introduced a fully automatic technique to extract the contour from the fluoroscopic images, based on a Canny edge detector [15]. They used two fluoroscopic sequences of drop-landing motion with intra-fluoroscopic distance between 58 • − 82 • .…”

Section: Fig8 : "Exted" -> "Extend"mentioning

confidence: 99%

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Fluoroscopy-based tracking of femoral kinematics with statistical shape models

Valenti

Momi

et al. 2015

Int J CARS

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Abstract:Purpose Precise knee kinematics assessment helps to diagnose knee pathologies and to improve the design of customized prosthetic components. The first step in identifying knee kinematics is to assess the femoral motion in the anatomical frame. However, no work has been done on pathological femurs, whose shape can be highly different from healthy ones. Method We propose a new femoral tracking technique based on Statistical Shape Models (SSMs) and two calibrated fluoroscopic images, taken at different flexion-extension angles. The cost function optimization is based on genetic algorithms, to avoid local minima. The proposed approach was evaluated on 3 sets of digitally reconstructed radiographic images of osteoarthritic patients. Results It is found that using the estimated shape, rather than that calculated from CT, significantly reduces the pose accuracy, but still has reasonably good results (angle errors around 2 degrees, translation around 1.5mm). Cover Letter. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems CorporationThe authors thank the reviewers and the editor for their interest in this topic and for giving them the possibility to improve the paper with their comments. Hereunder we answer the questions arisen, with indications on the changes made to the manuscript According to the reviewer comment, we modified the text in the "State of the Art" section as follows:"Knee kinematics assessment has great importance both to understand the problems associated with a large number of knee pathologies and to improve the design of prosthetic components. In case of severe osteoarthritis, that are eligible for joint implant surgery, in vivo pre-operatory knee kinematics is fundamental to understand the relative motion between the three joint bones. The relative movement can give an insight of how the ligaments are stretched and their stability, and how could be the feeling for the patient. In this way, pre operatory knee kinematics could help the surgeon to decide which prosthesis should be used and how to correct the misalignment of the bones [3]. Currently, the reconstruction of the pose of the knee can be done using 3D scans such as real-time Magnetic Resonance Imaging (MRI) or through a 2D/3D registration method that superimposes the shape extracted from MRI or Computed Tomography (CT) onto an image, usually X-ray or fluoroscopy. Real-time MRI is suitable to study joint kinematics, as it evidences the muscle structure during movements. However, it can only be used with relatively slow movements, and the accuracy obtained increases from 1mm to more than 3mm depending on the velocity of the movement. In addition, MRI scans are highly expensive [4]. Traditional CT and MRI provide an accurate evaluation of the morphology of the knee, but are limited to static positioning of the patient.[…]The use of dynamic fluoroscopy to detect knee kinematics is described in [12]. The authors use a fluoroscopic system flashing at 30Hz, obtaining continuous images of the knee flexion from 0° to 120°...

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“…One of the main claims of novelty over other work such as [1] According to the reviewer comment, we modified the text in paragraph 2.4.1 section as follows:…”

Section: In Paragraph 22 We Modified the Text As Followsmentioning

confidence: 99%

Section: In Paragraph 22 We Modified the Text As Followsmentioning

confidence: 99%

Section: Fig8 : "Exted" -> "Extend"mentioning

confidence: 99%

Section: Fig8 : "Exted" -> "Extend"mentioning

confidence: 99%

Section: Fig8 : "Exted" -> "Extend"mentioning

confidence: 99%

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Fluoroscopy-based tracking of femoral kinematics with statistical shape models

Valenti

Momi

et al. 2015

Int J CARS

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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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