scholarly journals A novel method for defining ligament characteristics in subject specific dynamic surgical planning

10.29007/mkjf ◽  
2018 ◽  
Author(s):  
Willy Theodore ◽  
Joseph Little ◽  
David Liu ◽  
Jonathan Bare ◽  
David Dickison ◽  
...  
Keyword(s):  
Knee Joint ◽  
Computational Model ◽  
Computational Models ◽  
Reference Strain ◽  
Knee Laxity ◽  
Patient Specific ◽  
X Rays ◽  
Anatomical Characteristics ◽  
Free Length ◽  
Subject Specific

Despite of the high success of TKA, 20% of recipients remain dissatisfied with their surgery. There is an increasing discordance in the literature on what is an optimal goal for component alignment. Furthermore, the unique patient specific anatomical characteristics will also play a role. The dynamic characteristics of a TKR is a product of the complex interaction between a patient’s individual anatomical characteristics and the specific alignment of the components in that patient knee joint. These interactions can be better understood with computational models. Our objective was to characterise ligament characteristics by measuring knee joint laxity with functional radiograph and with the aid of a computational model and an optimisation study to estimate the subject specific free length of the ligaments.Pre-operative CT and functional radiographs, varus and valgus stressed X-rays assessing the collateral ligaments, were captured for 10 patients. CT scan was segmented and 3D-2D pose estimation was performed against the radiographs. Patient specific tibio-femoral joint computational model was created. The model was virtually positioned to the functional radiograph positions to simulate the boundary conditions when the knee is stressed. The model was simulated to achieve static equilibrium. Optimisation was done on ligament free length and a scaling coefficient, flexion factor, to consider the ligaments wrapping behaviour.Our findings show the generic values for reference strain differ significantly from reference strains calculated from the optimised ligament parameters, up to 35% as percentage strain. There was also a wide variation in the reference strain values between subjects and ligaments, with a range of 37% strain between subjects. Additionally, the knee laxity recorded clinically shows a large variation between patients and it appears to be divorced from coronal alignment measured in CT. This suggest the ligaments characteristics vary widely between subjects and non-functional imaging is insufficient to determine its characteristics. These large variations necessitates a subject-specific approach when creating knee computational models and functional radiographs may be a viable method to characterise patient specific ligaments.

scholarly journals Implementation of subject-specific collagen architecture of cartilage into a 2D computational model of a knee joint-data from the osteoarthritis initiative (OAI)

2012 ◽  
Vol 31 (1) ◽  
pp. 10-22 ◽  
Author(s):  
Lasse P. Räsänen ◽  
Mika E. Mononen ◽  
Miika T. Nieminen ◽  
Eveliina Lammentausta ◽  
Jukka S. Jurvelin ◽  
...  
Keyword(s):  
Knee Joint ◽  
Computational Model ◽  
Subject Specific ◽  
Osteoarthritis Initiative

scholarly journals Subject-Specific Analysis of Joint Contact Mechanics: Application to the Study of Osteoarthritis and Surgical Planning

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Corinne R. Henak ◽  
Andrew E. Anderson ◽  
Jeffrey A. Weiss
Keyword(s):  
Contact Mechanics ◽  
Computational Models ◽  
Preoperative Planning ◽  
Patient Specific ◽  
Joint Mechanics ◽  
Imaging Data ◽  
Computational Biomechanics ◽  
Subject Specific ◽  
Joint Contact ◽  
Technical Considerations

Advances in computational mechanics, constitutive modeling, and techniques for subject-specific modeling have opened the door to patient-specific simulation of the relationships between joint mechanics and osteoarthritis (OA), as well as patient-specific preoperative planning. This article reviews the application of computational biomechanics to the simulation of joint contact mechanics as relevant to the study of OA. This review begins with background regarding OA and the mechanical causes of OA in the context of simulations of joint mechanics. The broad range of technical considerations in creating validated subject-specific whole joint models is discussed. The types of computational models available for the study of joint mechanics are reviewed. The types of constitutive models that are available for articular cartilage are reviewed, with special attention to choosing an appropriate constitutive model for the application at hand. Issues related to model generation are discussed, including acquisition of model geometry from volumetric image data and specific considerations for acquisition of computed tomography and magnetic resonance imaging data. Approaches to model validation are reviewed. The areas of parametric analysis, factorial design, and probabilistic analysis are reviewed in the context of simulations of joint contact mechanics. Following the review of technical considerations, the article details insights that have been obtained from computational models of joint mechanics for normal joints; patient populations; the study of specific aspects of joint mechanics relevant to OA, such as congruency and instability; and preoperative planning. Finally, future directions for research and application are summarized.

scholarly journals Computational Knee Ligament Modeling Using Experimentally Determined Zero-Load Lengths

2012 ◽  
Vol 6 (1) ◽  
pp. 33-41 ◽  
Author(s):  
Katherine H Bloemker ◽  
Trent M Guess ◽  
Lorin Maletsky ◽  
Kevin Dodd
Keyword(s):  
Knee Joint ◽  
Range Of Motion ◽  
Computational Models ◽  
Knee Kinematics ◽  
Specific Method ◽  
Knee Ligament ◽  
Knee Simulator ◽  
Subject Specific

This study presents a subject-specific method of determining the zero-load lengths of the cruciate and collateral ligaments in computational knee modeling. Three cadaver knees were tested in a dynamic knee simulator. The cadaver knees also underwent manual envelope of motion testing to find their passive range of motion in order to determine the zero-load lengths for each ligament bundle. Computational multibody knee models were created for each knee and model kinematics were compared to experimental kinematics for a simulated walk cycle. One-dimensional non-linear spring damper elements were used to represent cruciate and collateral ligament bundles in the knee models. This study found that knee kinematics were highly sensitive to altering of the zero-load length. The results also suggest optimal methods for defining each of the ligament bundle zero-load lengths, regardless of the subject. These results verify the importance of the zero-load length when modeling the knee joint and verify that manual envelope of motion measurements can be used to determine the passive range of motion of the knee joint. It is also believed that the method described here for determining zero-load length can be used for in vitro or in vivo subject-specific computational models.

scholarly journals A Combined Experimental and Computational Approach to Subject-Specific Analysis of Knee Joint Laxity

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Michael D. Harris ◽  
Adam J. Cyr ◽  
Azhar A. Ali ◽  
Clare K. Fitzpatrick ◽  
Paul J. Rullkoetter ◽  
...  
Keyword(s):  
Knee Joint ◽  
Knee Laxity ◽  
Experimental Tests ◽  
Population Based ◽  
Knee Mechanics ◽  
Ligament Strain ◽  
Subject Specific ◽  
Joint Biomechanics ◽  
The Individual

Modeling complex knee biomechanics is a continual challenge, which has resulted in many models of varying levels of quality, complexity, and validation. Beyond modeling healthy knees, accurately mimicking pathologic knee mechanics, such as after cruciate rupture or meniscectomy, is difficult. Experimental tests of knee laxity can provide important information about ligament engagement and overall contributions to knee stability for development of subject-specific models to accurately simulate knee motion and loading. Our objective was to provide combined experimental tests and finite-element (FE) models of natural knee laxity that are subject-specific, have one-to-one experiment to model calibration, simulate ligament engagement in agreement with literature, and are adaptable for a variety of biomechanical investigations (e.g., cartilage contact, ligament strain, in vivo kinematics). Calibration involved perturbing ligament stiffness, initial ligament strain, and attachment location until model-predicted kinematics and ligament engagement matched experimental reports. Errors between model-predicted and experimental kinematics averaged <2 deg during varus–valgus (VV) rotations, <6 deg during internal–external (IE) rotations, and <3 mm of translation during anterior–posterior (AP) displacements. Engagement of the individual ligaments agreed with literature descriptions. These results demonstrate the ability of our constraint models to be customized for multiple individuals and simultaneously call attention to the need to verify that ligament engagement is in good general agreement with literature. To facilitate further investigations of subject-specific or population based knee joint biomechanics, data collected during the experimental and modeling phases of this study are available for download by the research community.

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model

2017 ◽  
Vol 139 (9) ◽  
Author(s):  
Ruchi D. Chande ◽  
Jennifer S. Wayne
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Model ◽  
Computational Models ◽  
Soft Tissues ◽  
Predictive Ability ◽  
Patient Specific ◽  
Specific Work ◽  
Diarthrodial Joints ◽  
Artificial Neural

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

Computed Tomography-Based Patient-Specific Instrumentation Loses Accuracy with Significant Varus Preoperative Misalignment

2020 ◽  
Author(s):  
Vicente Jesús León-Muñoz ◽  
Mirian López-López ◽  
Alonso José Lisón-Almagro ◽  
Francisco Martínez-Martínez ◽  
Fernando Santonja-Medina
Keyword(s):  
Computed Tomography ◽  
Deformity Correction ◽  
Patient Specific ◽  
Control Group ◽  
X Rays ◽  
Varus Alignment ◽  
Patient Specific Instrumentation ◽  
Conventional Instrumentation ◽  
The Mean ◽  
Valgus Alignment

AbstractPatient-specific instrumentation (PSI) has been introduced to simplify and make total knee arthroplasty (TKA) surgery more precise, effective, and efficient. We performed this study to determine whether the postoperative coronal alignment is related to preoperative deformity when computed tomography (CT)-based PSI is used for TKA surgery, and how the PSI approach compares with deformity correction obtained with conventional instrumentation. We analyzed pre-and post-operative full length standing hip-knee-ankle (HKA) X-rays of the lower limb in both groups using a convention > 180 degrees for valgus alignment and < 180 degrees for varus alignment. For the PSI group, the mean (± SD) pre-operative HKA angle was 172.09 degrees varus (± 6.69 degrees) with a maximum varus alignment of 21.5 degrees (HKA 158.5) and a maximum valgus alignment of 14.0 degrees. The mean post-operative HKA was 179.43 degrees varus (± 2.32 degrees) with a maximum varus alignment of seven degrees and a maximum valgus alignment of six degrees. There has been a weak correlation among the values of the pre- and postoperative HKA angle. The adjusted odds ratio (aOR) of postoperative alignment outside the range of 180 ± 3 degrees was significantly higher with a preoperative varus misalignment of 15 degrees or more (aOR: 4.18; 95% confidence interval: 1.35–12.96; p = 0.013). In the control group (conventional instrumentation), this loss of accuracy occurs with preoperative misalignment of 10 degrees. Preoperative misalignment below 15 degrees appears to present minimal influence on postoperative alignment when a CT-based PSI system is used. The CT-based PSI tends to lose accuracy with preoperative varus misalignment over 15 degrees.

CSBPI_Site：Multi-Information Sources of Features to RNA Binding Sites Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Lichao Zhang ◽  
Zihong Huang ◽  
Liang Kong
Keyword(s):  
Computational Model ◽  
Binding Sites ◽  
Noncoding Rna ◽  
Computational Models ◽  
Information Sources ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Gradient Boosting ◽  
Position Information ◽  
Rna Binding Sites

Background: RNA-binding proteins establish posttranscriptional gene regulation by coordinating the maturation, editing, transport, stability, and translation of cellular RNAs. The immunoprecipitation experiments could identify interaction between RNA and proteins, but they are limited due to the experimental environment and material. Therefore, it is essential to construct computational models to identify the function sites. Objective: Although some computational methods have been proposed to predict RNA binding sites, the accuracy could be further improved. Moreover, it is necessary to construct a dataset with more samples to design a reliable model. Here we present a computational model based on multi-information sources to identify RNA binding sites. Method: We construct an accurate computational model named CSBPI_Site, based on xtreme gradient boosting. The specifically designed 15-dimensional feature vector captures four types of information (chemical shift, chemical bond, chemical properties and position information). Results: The satisfied accuracy of 0.86 and AUC of 0.89 were obtained by leave-one-out cross validation. Meanwhile, the accuracies were slightly different (range from 0.83 to 0.85) among three classifiers algorithm, which showed the novel features are stable and fit to multiple classifiers. These results showed that the proposed method is effective and robust for noncoding RNA binding sites identification. Conclusion: Our method based on multi-information sources is effective to represent the binding sites information among ncRNAs. The satisfied prediction results of Diels-Alder riboz-yme based on CSBPI_Site indicates that our model is valuable to identify the function site.

scholarly journals Numerical and Experimental Investigations on Vocal Fold Approximation in Healthy and Simulated Unilateral Vocal Fold Paralysis

2021 ◽  
Vol 11 (4) ◽  
pp. 1817
Author(s):  
Zheng Li ◽  
Azure Wilson ◽  
Lea Sayce ◽  
Amit Avhad ◽  
Bernard Rousseau ◽  
...  
Keyword(s):  
Computational Model ◽  
Vocal Fold ◽  
High Speed ◽  
Computational Models ◽  
Ex Vivo ◽  
Vocal Fold Paralysis ◽  
Future Application ◽  
Experimental Investigations ◽  
Type I ◽  
Mri Scans

We have developed a novel surgical/computational model for the investigation of unilat-eral vocal fold paralysis (UVFP) which will be used to inform future in silico approaches to improve surgical outcomes in type I thyroplasty. Healthy phonation (HP) was achieved using cricothyroid suture approximation on both sides of the larynx to generate symmetrical vocal fold closure. Following high-speed videoendoscopy (HSV) capture, sutures on the right side of the larynx were removed, partially releasing tension unilaterally and generating asymmetric vocal fold closure characteristic of UVFP (sUVFP condition). HSV revealed symmetric vibration in HP, while in sUVFP the sutured side demonstrated a higher frequency (10–11%). For the computational model, ex vivo magnetic resonance imaging (MRI) scans were captured at three configurations: non-approximated (NA), HP, and sUVFP. A finite-element method (FEM) model was built, in which cartilage displacements from the MRI images were used to prescribe the adduction, and the vocal fold deformation was simulated before the eigenmode calculation. The results showed that the frequency comparison between the two sides was consistent with observations from HSV. This alignment between the surgical and computational models supports the future application of these methods for the investigation of treatment for UVFP.

scholarly journals Application of subject-specific adaptive mechanical loading for bone healing in a mouse tail vertebral defect

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Angad Malhotra ◽  
Matthias Walle ◽  
Graeme R. Paul ◽  
Gisela A. Kuhn ◽  
Ralph Müller
Keyword(s):  
Mechanical Loading ◽  
Bone Defect ◽  
Bone Healing ◽  
Three Dimensional ◽  
Patient Specific ◽  
Dependent Manner ◽  
Micro Computed Tomography ◽  
Computed Tomography Images ◽  
Bone Defect Healing ◽  
Subject Specific

AbstractMethods to repair bone defects arising from trauma, resection, or disease, continue to be sought after. Cyclic mechanical loading is well established to influence bone (re)modelling activity, in which bone formation and resorption are correlated to micro-scale strain. Based on this, the application of mechanical stimulation across a bone defect could improve healing. However, if ignoring the mechanical integrity of defected bone, loading regimes have a high potential to either cause damage or be ineffective. This study explores real-time finite element (rtFE) methods that use three-dimensional structural analyses from micro-computed tomography images to estimate effective peak cyclic loads in a subject-specific and time-dependent manner. It demonstrates the concept in a cyclically loaded mouse caudal vertebral bone defect model. Using rtFE analysis combined with adaptive mechanical loading, mouse bone healing was significantly improved over non-loaded controls, with no incidence of vertebral fractures. Such rtFE-driven adaptive loading regimes demonstrated here could be relevant to clinical bone defect healing scenarios, where mechanical loading can become patient-specific and more efficacious. This is achieved by accounting for initial bone defect conditions and spatio-temporal healing, both being factors that are always unique to the patient.

scholarly journals Supervised Domain Adaptation for Automated Semantic Segmentation of the Atrial Cavity

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 898
Author(s):  
Marta Saiz-Vivó ◽  
Adrián Colomer ◽  
Carles Fonfría ◽  
Luis Martí-Bonmatí ◽  
Valery Naranjo
Keyword(s):  
High Performance ◽  
Computational Models ◽  
Domain Adaptation ◽  
Semantic Segmentation ◽  
Patient Specific ◽  
Mr Images ◽  
Training Samples ◽  
Volumetric Images ◽  
Acquisition Costs ◽  
Left And Right

Atrial fibrillation (AF) is the most common cardiac arrhythmia. At present, cardiac ablation is the main treatment procedure for AF. To guide and plan this procedure, it is essential for clinicians to obtain patient-specific 3D geometrical models of the atria. For this, there is an interest in automatic image segmentation algorithms, such as deep learning (DL) methods, as opposed to manual segmentation, an error-prone and time-consuming method. However, to optimize DL algorithms, many annotated examples are required, increasing acquisition costs. The aim of this work is to develop automatic and high-performance computational models for left and right atrium (LA and RA) segmentation from a few labelled MRI volumetric images with a 3D Dual U-Net algorithm. For this, a supervised domain adaptation (SDA) method is introduced to infer knowledge from late gadolinium enhanced (LGE) MRI volumetric training samples (80 LA annotated samples) to a network trained with balanced steady-state free precession (bSSFP) MR images of limited number of annotations (19 RA and LA annotated samples). The resulting knowledge-transferred model SDA outperformed the same network trained from scratch in both RA (Dice equals 0.9160) and LA (Dice equals 0.8813) segmentation tasks.

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