Variation of the femoral J-Curve in the native knee

Grothues, Sonja; Asseln, Malte; Radermacher, Klaus

doi:10.29007/5k32

Cited by 3 publications

(6 citation statements)

References 11 publications

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“…For the combined analyses, the identified radii of the 3D J-Curve’s mean shape are comparable to those of previous studies on the 2D J-Curve [ 11 , 12 , 15 , 27 ]. Most of the parameter values derived in this study are also comparable to a previous study on the 2D J-Curve by our group [ 16 ]. However, a relevant difference regarding the AP length of the medial J-Curve can be seen.…”

Section: Discussionsupporting

confidence: 88%

“…Hence, for future implant systems it might be valuable not only to consider narrow and standard versions but, e.g., high and low curvature implants as well as versions with different offsets. Taking into account the importance of shape mismatches along the articulating surfaces [ 9 , 30 ] as well as the discrepancy between actual implant designs and patient specific J-Curves [ 16 ], the number of additional sizes needed potentially will be very high. Against this background, we agree to the conclusion of Delport et al, that another way could be to customize the implant design to each patient individually [ 9 ].…”

Section: Discussionmentioning

confidence: 99%

“…Most studies used geometrical primitives such as ellipses and circles and fitted them to the respective 2D J-Curve contours for investigation [ 10 , 11 , 12 , 13 , 14 , 15 ]. In a previous study, we evaluated the variation in the native femoral J-Curve by principal component analysis (PCA), enabling a more comprehensive investigation of the shape variation [ 16 ]. However, due to the 3D nature of knee motion, the restriction to a 2D evaluation remained a limitation of this study.…”

Section: Introductionmentioning

confidence: 99%

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Variation of the Three-Dimensional Femoral J-Curve in the Native Knee

Grothues

Radermacher

2021

JPM

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The native femoral J-Curve is known to be a relevant determinant of knee biomechanics. Similarly, after total knee arthroplasty, the J-Curve of the femoral implant component is reported to have a high impact on knee kinematics. The shape of the native femoral J-Curve has previously been analyzed in 2D, however, the knee motion is not planar. In this study, we investigated the J-Curve in 3D by principal component analysis (PCA) and the resulting mean shapes and modes by geometric parameter analysis. Surface models of 90 cadaveric femora were available, 56 male, 32 female and two without respective information. After the translation to a bone-specific coordinate system, relevant contours of the femoral condyles were derived using virtual rotating cutting planes. For each derived contour, an extremum search was performed. The extremum points were used to define the 3D J-Curve of each condyle. Afterwards a PCA and a geometric parameter analysis were performed on the medial and lateral 3D J-Curves. The normalized measures of the mean shapes and the aspects of shape variation of the male and female 3D J-Curves were found to be similar. When considering both female and male J-Curves in a combined analysis, the first mode of the PCA primarily consisted of changes in size, highlighting size differences between female and male femora. Apart from changes in size, variation regarding aspect ratio, arc lengths, orientation, circularity, as well as regarding relative location of the 3D J-Curves was found. The results of this study are in agreement with those of previous 2D analyses on shape and shape variation of the femoral J-Curves. The presented 3D analysis highlights new aspects of shape variability, e.g., regarding curvature and relative location in the transversal plane. Finally, the analysis presented may support the design of (patient-specific) femoral implant components for TKA.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Variation of the Three-Dimensional Femoral J-Curve in the Native Knee

Grothues

Radermacher

2021

JPM

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“…Third, we only considered AP and ML measures, while many more parameters such as the femoral J‐curve or the posterior condylar offset are linked to the clinical outcome 5,31 . The patient‐specific J‐Curve, for example, shows large interindividual variations in shape and orientation 32 . In addition, we only considered implant mismatch with regard to the overall AP and ML size, while (in contrast to patient specific implants) mismatch of OTS implants, for example, in terms of overhang or underhang can occur along the whole implant–bone interface contour.…”

Section: Discussionmentioning

confidence: 99%

“…5,31 The patientspecific J-Curve, for example, shows large interindividual variations in shape and orientation. 32 In addition, we only considered implant mismatch with regard to the overall AP and ML size, while (in contrast to patient specific implants) mismatch of OTS implants, for example, in terms of overhang or underhang can occur along the whole implant-bone interface contour. However, a recent study showed relevant relationships between parameters of the ML (AP) plane and overall ML (AP) size.…”

Section: F I G U R Ementioning

confidence: 99%

Potential for femoral size optimization for off‐the‐shelf implants: A CT derived implant database analysis

Grothues

Hohlmann

Zingde

et al. 2022

Journal Orthopaedic Research

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In total knee arthroplasty, the femoral implant size is chosen mainly based on the femoral anteroposterior (AP) height and mediolateral (ML) width. This choice often is a compromise, due to limited size availability. Inadequate AP fit is expected to alter flexion laxity and thus knee function. Inadequate ML fit entails underhang or overhang, which is linked to worse clinical outcomes. Hence, we aimed to find implant size distributions, which maximize population coverage, and to evaluate the sensitivity regarding error bounds and the number of implant sizes for a database of 85,143 cases. All patients in the database have been provided with a patient‐specific implant in the past. For a subset of 1049 cases, three‐dimensional preoperative bone surface models were available. These were used to validate whether the implant dimensions were representative of the bone dimensions. Particle Swarm Optimization was used for optimizing the implant size distribution. The deviations between implant and bone measures in the subset were found to be clinically irrelevant. Therefore, the full database of 85,143 cases was used for further analyses. A higher sensitivity of the population coverage regarding the error bounds compared to the number of implant sizes was found. For an exemplary setup of 12 optimized implant sizes and error bounds of ±1.5 mm for AP and ±3 mm for ML, a population coverage of almost 85% was achieved. In contrast, even with 30 implant sizes, a full population coverage could not be reached. Hence, remaining cases should be provided with patient‐specific implants.

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Knee Bone Models From Ultrasound

Hohlmann

Brößner²,

Phlippen³

et al. 2023

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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The number of total knee arthroplasties performed world-wide are on a rise. Patient-specific planning and implants may improve surgical outcomes, but require 3D models of the bones involved. Ultrasound may become a cheap and non-harmful imaging modality, if the short comings of segmentation techniques in terms of automation, accuracy and robustness are overcome. Furthermore, any kind of ultrasound-based bone reconstruction must involve some kind of model completion, in order to handle occluded areas, e.g., the frontal femur. A fully-automatic and robust processing pipeline is proposed, generating full bone models from 3D freehand ultrasound scanning. A convolutional neural network is combined with a statistical shape model to segment and extrapolate the bone surface. We evaluate the method in-vivo on 10 subjects, comparing the ultrasound-based model to a magnetic resonance imaging (MRI) reference. The partial freehand 3D record of the femur and tibia bones deviate by 0.7 to 0.8mm from the MRI reference. After completion and on average, the full bone model shows sub-millimetric error in case of the femur and 1.24mm in case of the tibia. Processing of the images is performed in real-time, and the final model fitting step is computed in less than one minute. On average, it took 22 minutes for a full record per subject.

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Variation of the femoral J-Curve in the native knee

Cited by 3 publications

References 11 publications

Variation of the Three-Dimensional Femoral J-Curve in the Native Knee

Variation of the Three-Dimensional Femoral J-Curve in the Native Knee

Potential for femoral size optimization for off‐the‐shelf implants: A CT derived implant database analysis

Knee Bone Models From Ultrasound

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