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Carman L, Besier TF, Rooks NB, Choisne J. An articulated shape model to predict paediatric lower limb bone geometry using sparse landmarks. J Biomech 2024; 172:112211. [PMID: 38955093 DOI: 10.1016/j.jbiomech.2024.112211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 05/22/2024] [Accepted: 06/26/2024] [Indexed: 07/04/2024]
Abstract
Creating musculoskeletal models in a paediatric population currently involves either creating an image-based model from medical imaging data or a generic model using linear scaling. Image-based models provide a high level of accuracy but are time-consuming and costly to implement, on the other hand, linear scaling of an adult template musculoskeletal model is faster and common practice, but the output errors are significantly higher. An articulated shape model incorporates pose and shape to predict geometry for use in musculoskeletal models based on existing information from a population to provide both a fast and accurate method. From a population of 333 children aged 4-18 years old, we have developed an articulated shape model of paediatric lower limb bones to predict bone geometry from eight bone landmarks commonly used for motion capture. Bone surface root mean squared errors were found to be 2.63 ± 0.90 mm, 1.97 ± 0.61 mm, and 1.72 ± 0.51 mm for the pelvis, femur, and tibia/fibula, respectively. Linear scaling produced bone surface errors of 4.79 ± 1.39 mm, 4.38 ± 0.72 mm, and 4.39 ± 0.86 mm for the pelvis, femur, and tibia/fibula, respectively. Clinical bone measurement errors were low across all bones predicted using the articulated shape model, which outperformed linear scaling for all measurements. However, the model failed to accurately capture torsional measures (femoral anteversion and tibial torsion). Overall, the articulated shape model was shown to be a fast and accurate method to predict lower limb bone geometry in a paediatric population, superior to linear scaling.
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Affiliation(s)
- Laura Carman
- Auckland Bioengineering Institute, 70 Symonds Street, Level 8, The University of Auckland, Auckland, New Zealand.
| | - Thor F Besier
- Auckland Bioengineering Institute, 70 Symonds Street, Level 8, The University of Auckland, Auckland, New Zealand; Department of Engineering Science & Biomedical Engineering, 70 Symonds Street, Level 0, The University of Auckland, Auckland, New Zealand.
| | - Nynke B Rooks
- Auckland Bioengineering Institute, 70 Symonds Street, Level 8, The University of Auckland, Auckland, New Zealand; Formus Labs, 70 Symonds Street, Level 9, Auckland, New Zealand.
| | - Julie Choisne
- Auckland Bioengineering Institute, 70 Symonds Street, Level 8, The University of Auckland, Auckland, New Zealand.
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Dastgerdi AK, Esrafilian A, Carty CP, Nasseri A, Barzan M, Korhonen RK, Astori I, Hall W, Saxby DJ. Sensitivity analysis of paediatric knee kinematics to the graft surgical parameters during anterior cruciate ligament reconstruction: A sequentially linked neuromusculoskeletal-finite element analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 248:108132. [PMID: 38503071 DOI: 10.1016/j.cmpb.2024.108132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/26/2024] [Accepted: 03/08/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND AND OBJECTIVE Incidence of paediatric anterior cruciate ligament (ACL) rupture has increased substantially over recent decades. Following ACL rupture, ACL reconstruction (ACLR) surgery is typically performed to restore passive knee stability. This surgery involves replacing the failed ACL with a graft, however, surgeons must select from range of surgical parameters (e.g., type, size, insertion, and pre-tension) with no robust evidence guiding these decisions. This study presents a systemmatic computational approach to study effects of surgical parameter variation on kinematics of paediatric knees. METHODS This study used sequentially-linked neuromusculoskeletal (NMSK) finite element (FE) models of three paediatric knees to estimate the: (i) sensitivity of post-operative knee kinematics to four surgical parameters (type, size, insertion, and pre-tension) through multi-input multi-output sensitivity analysis; (ii) influence of motion and loading conditions throughout stance phase of walking gait on sensitivity indices; and (iii) influence of subject-specific anatomy (i.e., knee size) on sensitivivty indices. A previously validated FE model of the intact knee for each subject served as a reference against which ACLR knee kinematics were compared. RESULTS Sensitivity analyses revealed significant influences of surgical parameters on ACLR knee kinematics, albeit without discernible trend favouring any one parameter. Graft size and pre-tension were primary drivers of variation in knee translations and rotations, however, their effects fluctuated across stance indicating motion and loading conditions affect system sensitivity to surgical parameters. Importantly, the sensitivity of knee kinematics to surgical parameter varied across subjects, indicating geometry (i.e., knee size) influenced system sensitivity. Notably, alterations in graft parameters yielded substantial effects on kinematics (normalized root-mean-square-error > 10 %) compared to intact knee models, indicating surgical parameters vary post-operative knee kinematics. CONCLUSIONS Overall, this initial study highlights the importance of surgical parameter selection on post-operative kinematics in the paediatric ACLR knee, and provides evidence of the need for personalized surgical planning to ultimately enhance patient outcomes.
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Affiliation(s)
- Ayda Karimi Dastgerdi
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia.
| | - Amir Esrafilian
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Christopher P Carty
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia; Department of Orthopedics, Children's Health Queensland Hospital and Health Service, QLD, Australia
| | - Azadeh Nasseri
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
| | - Martina Barzan
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Ivan Astori
- Department of Orthopedics, Children's Health Queensland Hospital and Health Service, QLD, Australia
| | - Wayne Hall
- School of Engineering and Built Environment, Mechanical Engineering and Industrial Design, Griffith University, Gold Coast, QLD, Australia
| | - David John Saxby
- Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland and the Advanced Design and Prototyping Technologies Institute (ADAPT), Griffith University, Gold Coast, QLD, Australia
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Xiang L, Gu Y, Shim V, Yeung T, Wang A, Fernandez J. A hybrid statistical morphometry free-form deformation approach to 3D personalized foot-ankle models. J Biomech 2024; 168:112120. [PMID: 38677027 DOI: 10.1016/j.jbiomech.2024.112120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 02/12/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024]
Abstract
Foot and ankle joint models are widely used in the biomechanics community for musculoskeletal and finite element analysis. However, personalizing a foot and ankle joint model is highly time-consuming in terms of medical image collection and data processing. This study aims to develop and evaluate a framework for constructing a comprehensive 3D foot model that integrates statistical shape modeling (SSM) with free-form deformation (FFD) of internal bones. The SSM component is derived from external foot surface scans (skin measurements) of 50 participants, utilizing principal component analysis (PCA) to capture the variance in foot shapes. The derived surface shapes from SSM then guide the FFD process to accurately reconstruct the internal bone structures. The workflow accuracy was established by comparing three model-generated foot models against corresponding skin and bone geometries manually segmented and not part of the original training set. We used the top ten principal components representing 85 % of the population variation to create the model. For prediction validation, the average Dice similarity coefficient, Hausdorff distance error, and root mean square error were 0.92 ± 0.01, 2.2 ± 0.19 mm, and 2.95 ± 0.23 mm for soft tissues, and 0.84 ± 0.03, 1.83 ± 0.1 mm, and 2.36 ± 0.12 mm for bones, respectively. This study presents an efficient approach for 3D personalized foot model reconstruction via SSM generation of the foot surface that informs bone reconstruction based on FFD. The proposed workflow is part of the open-source Musculoskeletal Atlas Project linked to OpenSim and makes it feasible to accurately generate foot models informed by population anatomy, and suitable for rigid body analysis and finite element simulation.
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Affiliation(s)
- Liangliang Xiang
- Faculty of Sports Science, Ningbo University, China; Auckland Bioengineering Institute, The University of Auckland, New Zealand
| | - Yaodong Gu
- Faculty of Sports Science, Ningbo University, China; Auckland Bioengineering Institute, The University of Auckland, New Zealand.
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, New Zealand
| | - Ted Yeung
- Auckland Bioengineering Institute, The University of Auckland, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, New Zealand; Center for Medical Imaging, Faculty of Medical and Health Sciences, The University of Auckland, New Zealand
| | - Justin Fernandez
- Faculty of Sports Science, Ningbo University, China; Auckland Bioengineering Institute, The University of Auckland, New Zealand; Department of Engineering Science, The University of Auckland, New Zealand
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Zhu J, Zhao J, Luo X, Hua Z. Nonunion scaphoid bone shape prediction using iterative kernel principal polynomial shape analysis. Med Phys 2024. [PMID: 38497549 DOI: 10.1002/mp.17027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 02/06/2024] [Accepted: 03/01/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND The scaphoid is an important mechanical stabilizer for both the proximal and distal carpal columns. The precise estimation of the complete scaphoid bone based on partial bone geometric information is a crucial factor in the effective management of scaphoid nonunion. Statistical shape model (SSM) could be utilized to predict the complete scaphoid shape based on the defective scaphoid. However, traditional principal component analysis (PCA) based SSM is limited by its linearity and the inability to adjust the number of modes used for prediction. PURPOSE This study proposes an iterative kernel principal polynomial shape analysis (iKPPSA)-based SSM to predict the pre-morbid shape of the scaphoid, aiming at enhancing the accuracy as well as the robustness of the model. METHODS Sixty-five sets of scaphoid images were used to train SSM and nine sets of scaphoid images were used for validation. For each validation image set, three defect types (tubercle, proximal pole, and avascular necrosis) were virtually created. The predicted shapes of the scaphoid by PCA, PPSA, KPCA, and iKPPSA-based SSM were evaluated against the original shape in terms of mean error, Hausdorff distance error, and Dice coefficient. RESULTS The proposed iKPPSA-based scaphoid SSM demonstrates significant robustness, with a generality of 0.264 mm and a specificity of 0.260 mm. It accounts for 99% of variability with the first seven principal modes of variation. Compared to the traditional PCA-based model, the iKPPSA-based scaphoid model prediction demonstrated superior performance for the proximal pole type fracture, with significant reductions of 25.2%, 24.7%, and 24.6% in mean error, Hausdorff distance, and root mean square error (RMSE), respectively, and a 0.35% improvement in Dice coefficient. CONCLUSION This study showed that the iKPPSA-based SSM exploits the nonlinearity of data features and delivers high reconstruction accuracy. It can be effectively integrated into preoperative planning for scaphoid fracture management or morphology-based biomechanical modeling of the scaphoid.
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Affiliation(s)
- Junjun Zhu
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
| | - Junhao Zhao
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
| | - Xianggeng Luo
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
| | - Zikai Hua
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
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Wang J, Li S, Sun Z, Lao Q, Shen B, Li K, Nie Y. Full-length radiograph based automatic musculoskeletal modeling using convolutional neural network. J Biomech 2024; 166:112046. [PMID: 38467079 DOI: 10.1016/j.jbiomech.2024.112046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 02/27/2024] [Accepted: 03/07/2024] [Indexed: 03/13/2024]
Abstract
Full-length radiographs contain information from which many anatomical parameters of the pelvis, femur, and tibia may be derived, but only a few anatomical parameters are used for musculoskeletal modeling. This study aimed to develop a fully automatic algorithm to extract anatomical parameters from full-length radiograph to generate a musculoskeletal model that is more accurate than linear scaled one. A U-Net convolutional neural network was trained to segment the pelvis, femur, and tibia from the full-length radiograph. Eight anatomic parameters (six for length and width, two for angles) were automatically extracted from the bone segmentation masks and used to generate the musculoskeletal model. Sørensen-Dice coefficient was used to quantify the consistency of automatic bone segmentation masks with manually segmented labels. Maximum distance error, root mean square (RMS) distance error and Jaccard index (JI) were used to evaluate the geometric accuracy of the automatically generated pelvis, femur and tibia models versus CT bone models. Mean Sørensen-Dice coefficients for the pelvis, femur and tibia 2D segmentation masks were 0.9898, 0.9822 and 0.9786, respectively. The algorithm-driven bone models were closer to the 3D CT bone models than the scaled generic models in geometry, with significantly lower maximum distance error (28.3 % average decrease from 24.35 mm) and RMS distance error (28.9 % average decrease from 9.55 mm) and higher JI (17.2 % average increase from 0.46) (P < 0.001). The algorithm-driven musculoskeletal modeling (107.15 ± 10.24 s) was faster than the manual process (870.07 ± 44.79 s) for the same full-length radiograph. This algorithm provides a fully automatic way to generate a musculoskeletal model from full-length radiograph that achieves an approximately 30 % reduction in distance errors, which could enable personalized musculoskeletal simulation based on full-length radiograph for large scale OA populations.
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Affiliation(s)
- Junqing Wang
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
| | - Shiqi Li
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China; College of Electrical Engineering, Sichuan University, Chengdu, Sichuan Province, China.
| | - Zitong Sun
- Sichuan University-Pittsburgh Institute (SCUPI), Sichuan University, Chengdu, Sichuan Province, China.
| | - Qicheng Lao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing, China
| | - Bin Shen
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
| | - Yong Nie
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.
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Nolte D, Xie S, Bull AMJ. 3D shape reconstruction of the femur from planar X-ray images using statistical shape and appearance models. Biomed Eng Online 2023; 22:30. [PMID: 36964560 PMCID: PMC10039582 DOI: 10.1186/s12938-023-01093-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/09/2023] [Indexed: 03/26/2023] Open
Abstract
Major trauma is a condition that can result in severe bone damage. Customised orthopaedic reconstruction allows for limb salvage surgery and helps to restore joint alignment. For the best possible outcome three dimensional (3D) medical imaging is necessary, but its availability and access, especially in developing countries, can be challenging. In this study, 3D bone shapes of the femur reconstructed from planar radiographs representing bone defects were evaluated for use in orthopaedic surgery. Statistical shape and appearance models generated from 40 cadaveric X-ray computed tomography (CT) images were used to reconstruct 3D bone shapes. The reconstruction simulated bone defects of between 0% and 50% of the whole bone, and the prediction accuracy using anterior-posterior (AP) and anterior-posterior/medial-lateral (AP/ML) X-rays were compared. As error metrics for the comparison, measures evaluating the distance between contour lines of the projections as well as a measure comparing similarities in image intensities were used. The results were evaluated using the root-mean-square distance for surface error as well as differences in commonly used anatomical measures, including bow, femoral neck, diaphyseal-condylar and version angles between reconstructed surfaces from the shape model and the intact shape reconstructed from the CT image. The reconstructions had average surface errors between 1.59 and 3.59 mm with reconstructions using the contour error metric from the AP/ML directions being the most accurate. Predictions of bow and femoral neck angles were well below the clinical threshold accuracy of 3°, diaphyseal-condylar angles were around the threshold of 3° and only version angle predictions of between 5.3° and 9.3° were above the clinical threshold, but below the range reported in clinical practice using computer navigation (i.e., 17° internal to 15° external rotation). This study shows that the reconstructions from partly available planar images using statistical shape and appearance models had an accuracy which would support their potential use in orthopaedic reconstruction.
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Affiliation(s)
- Daniel Nolte
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
| | - Shuqiao Xie
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK.
| | - Anthony M J Bull
- Department of Bioengineering, Imperial College London, London, SW7 2AZ, UK
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Cheng Y, Bailly R, Scavinner-Dorval C, Fouquet B, Borotikar B, Ben Salem D, Brochard S, Rousseau F. Comprehensive personalized ankle joint shape analysis of children with cerebral palsy from pediatric MRI. Front Bioeng Biotechnol 2022; 10:1059129. [PMID: 36507255 PMCID: PMC9732549 DOI: 10.3389/fbioe.2022.1059129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/07/2022] [Indexed: 11/26/2022] Open
Abstract
Cerebral palsy, a common physical disability in childhood, often causes abnormal patterns of movement and posture. To better understand the pathology and improve rehabilitation of patients, a comprehensive bone shape analysis approach is proposed in this article. First, a group analysis is performed on a clinical MRI dataset using two state-of-the-art shape analysis methods: ShapeWorks and a voxel-based method relying on Advanced Normalization Tools (ANTs) registration. Second, an analysis of three bones of the ankle is done to provide a complete view of the ankle joint. Third, a bone shape analysis is carried out at subject level to highlight variability patterns for personnalized understanding of deformities.
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Affiliation(s)
- Yue Cheng
- IMT Atlantique, LaTIM U1101 INSERM, Brest, France
| | | | | | | | | | | | - Sylvain Brochard
- CHU, UBO, LaTIM U1101 INSERM, Brest, France,*Correspondence: François Rousseau, francois.rousseau@imt-atlantique; Sylvain Brochard,
| | - François Rousseau
- IMT Atlantique, LaTIM U1101 INSERM, Brest, France,*Correspondence: François Rousseau, francois.rousseau@imt-atlantique; Sylvain Brochard,
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