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Muñoz-Moya E, Rasouligandomani M, Ruiz Wills C, Chemorion FK, Piella G, Noailly J. Unveiling interactions between intervertebral disc morphologies and mechanical behavior through personalized finite element modeling. Front Bioeng Biotechnol 2024; 12:1384599. [PMID: 38915337 PMCID: PMC11194671 DOI: 10.3389/fbioe.2024.1384599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 04/25/2024] [Indexed: 06/26/2024] Open
Abstract
Introduction: Intervertebral Disc (IVD) Degeneration (IDD) is a significant health concern, potentially influenced by mechanotransduction. However, the relationship between the IVD phenotypes and mechanical behavior has not been thoroughly explored in local morphologies where IDD originates. This work unveils the interplays among morphological and mechanical features potentially relevant to IDD through Abaqus UMAT simulations. Methods: A groundbreaking automated method is introduced to transform a calibrated, structured IVD finite element (FE) model into 169 patient-personalized (PP) models through a mesh morphing process. Our approach accurately replicates the real shapes of the patient's Annulus Fibrosus (AF) and Nucleus Pulposus (NP) while maintaining the same topology for all models. Using segmented magnetic resonance images from the former project MySpine, 169 models with structured hexahedral meshes were created employing the Bayesian Coherent Point Drift++ technique, generating a unique cohort of PP FE models under the Disc4All initiative. Machine learning methods, including Linear Regression, Support Vector Regression, and eXtreme Gradient Boosting Regression, were used to explore correlations between IVD morphology and mechanics. Results: We achieved PP models with AF and NP similarity scores of 92.06\% and 92.10\% compared to the segmented images. The models maintained good quality and integrity of the mesh. The cartilage endplate (CEP) shape was represented at the IVD-vertebra interfaces, ensuring personalized meshes. Validation of the constitutive model against literature data showed a minor relative error of 5.20%. Discussion: Analysis revealed the influential impact of local morphologies on indirect mechanotransduction responses, highlighting the roles of heights, sagittal areas, and volumes. While the maximum principal stress was influenced by morphologies such as heights, the disc's ellipticity influenced the minimum principal stress. Results suggest the CEPs are not influenced by their local morphologies but by those of the AF and NP. The generated free-access repository of individual disc characteristics is anticipated to be a valuable resource for the scientific community with a broad application spectrum.
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Affiliation(s)
- Estefano Muñoz-Moya
- BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Carlos Ruiz Wills
- BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
| | - Francis Kiptengwer Chemorion
- BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
- Department of Information Technology, InSilicoTrials Technologies, Trieste, Italy
| | - Gemma Piella
- BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jérôme Noailly
- BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
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Rasouligandomani M, Del Arco A, Chemorion FK, Bisotti MA, Galbusera F, Noailly J, González Ballester MA. Dataset of Finite Element Models of Normal and Deformed Thoracolumbar Spine. Sci Data 2024; 11:549. [PMID: 38811573 PMCID: PMC11137096 DOI: 10.1038/s41597-024-03351-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
Adult spine deformity (ASD) is prevalent and leads to a sagittal misalignment in the vertebral column. Computational methods, including Finite Element (FE) Models, have emerged as valuable tools for investigating the causes and treatment of ASD through biomechanical simulations. However, the process of generating personalised FE models is often complex and time-consuming. To address this challenge, we present a dataset of FE models with diverse spine morphologies that statistically represent real geometries from a cohort of patients. These models are generated using EOS images, which are utilized to reconstruct 3D surface spine models. Subsequently, a Statistical Shape Model (SSM) is constructed, enabling the adaptation of a FE hexahedral mesh template for both the bone and soft tissues of the spine through mesh morphing. The SSM deformation fields facilitate the personalization of the mean hexahedral FE model based on sagittal balance measurements. Ultimately, this new hexahedral SSM tool offers a means to generate a virtual cohort of 16807 thoracolumbar FE spine models, which are openly shared in a public repository.
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Affiliation(s)
| | | | - Francis Kiptengwer Chemorion
- BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, 08018, Spain
- InSilicoTrials Technologies Company, Trieste, 34123, Italy
- Barcelona Supercomputing Center, Barcelona, 08034, Spain
| | | | - Fabio Galbusera
- IRCCS Istituto Ortopedico Galeazzi, Milan, 20161, Italy
- Schulthess Klinik, Zürich, 8008, Switzerland
| | - Jérôme Noailly
- BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, 08018, Spain.
| | - Miguel A González Ballester
- BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, 08018, Spain
- ICREA, Barcelona, 08010, Spain
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Kok J, Shcherbakova YM, Schlösser TPC, Seevinck PR, van der Velden TA, Castelein RM, Ito K, van Rietbergen B. Automatic generation of subject-specific finite element models of the spine from magnetic resonance images. Front Bioeng Biotechnol 2023; 11:1244291. [PMID: 37731762 PMCID: PMC10508183 DOI: 10.3389/fbioe.2023.1244291] [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: 06/22/2023] [Accepted: 08/24/2023] [Indexed: 09/22/2023] Open
Abstract
The generation of subject-specific finite element models of the spine is generally a time-consuming process based on computed tomography (CT) images, where scanning exposes subjects to harmful radiation. In this study, a method is presented for the automatic generation of spine finite element models using images from a single magnetic resonance (MR) sequence. The thoracic and lumbar spine of eight adult volunteers was imaged using a 3D multi-echo-gradient-echo sagittal MR sequence. A deep-learning method was used to generate synthetic CT images from the MR images. A pre-trained deep-learning network was used for the automatic segmentation of vertebrae from the synthetic CT images. Another deep-learning network was trained for the automatic segmentation of intervertebral discs from the MR images. The automatic segmentations were validated against manual segmentations for two subjects, one with scoliosis, and another with a spine implant. A template mesh of the spine was registered to the segmentations in three steps using a Bayesian coherent point drift algorithm. First, rigid registration was applied on the complete spine. Second, non-rigid registration was used for the individual discs and vertebrae. Third, the complete spine was non-rigidly registered to the individually registered discs and vertebrae. Comparison of the automatic and manual segmentations led to dice-scores of 0.93-0.96 for all vertebrae and discs. The lowest dice-score was in the disc at the height of the implant where artifacts led to under-segmentation. The mean distance between the morphed meshes and the segmentations was below 1 mm. In conclusion, the presented method can be used to automatically generate accurate subject-specific spine models.
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Affiliation(s)
- Joeri Kok
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | | | - Tom P. C. Schlösser
- Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Peter R. Seevinck
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
- MRIguidance BV, Utrecht, Netherlands
| | - Tijl A. van der Velden
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
- MRIguidance BV, Utrecht, Netherlands
| | - René M. Castelein
- Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Keita Ito
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of Orthopaedic Surgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bert van Rietbergen
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
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Hirose O. Geodesic-Based Bayesian Coherent Point Drift. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:5816-5832. [PMID: 36227821 DOI: 10.1109/tpami.2022.3214191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Coherent point drift is a well-known algorithm for non-rigid registration, i.e., a procedure for deforming a shape to match another shape. Despite its prevalence, the algorithm has a major drawback that remains unsolved: It unnaturally deforms the different parts of a shape, e.g., human legs, when they are neighboring each other. The inappropriate deformations originate from a proximity-based deformation constraint, called motion coherence. This study proposes a non-rigid registration method that addresses the drawback. The key to solving the problem is to redefine the motion coherence using a geodesic, i.e., the shortest route between points on a shape's surface. We also propose the accelerated variant of the registration method. In numerical studies, we demonstrate that the algorithms can circumvent the drawback of coherent point drift. We also show that the accelerated algorithm can be applied to shapes comprising several millions of points.
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Gu J, Lan Y, Kong F, Liu L, Sun H, Liu J, Yi L. A Method of Setting the LiDAR Field of View in NDT Relocation Based on ROI. SENSORS (BASEL, SWITZERLAND) 2023; 23:843. [PMID: 36679641 PMCID: PMC9860606 DOI: 10.3390/s23020843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/02/2022] [Accepted: 01/10/2023] [Indexed: 06/17/2023]
Abstract
LiDAR placement and field of view selection play a role in detecting the relative position and pose of vehicles in relocation maps based on high-precision map automatic navigation. When the LiDAR field of view is obscured or the LiDAR position is misplaced, this can easily lead to loss of repositioning or low repositioning accuracy. In this paper, a method of LiDAR layout and field of view selection based on high-precision map normal distribution transformation (NDT) relocation is proposed to solve the problem of large NDT relocation error and position loss when the occlusion field of view is too large. To simulate the real placement environment and the LiDAR obstructed by obstacles, the ROI algorithm is used to cut LiDAR point clouds and to obtain LiDAR point cloud data of different sizes. The cut point cloud data is first downsampled and then relocated. The downsampling points for NDT relocation are recorded as valid matching points. The direction and angle settings of the LiDAR point cloud data are optimized using RMSE values and valid matching points. The results show that in the urban scene with complex road conditions, there are more front and rear matching points than left and right matching points within the unit angle. The more matching points of the NDT relocation algorithm there are, the higher the relocation accuracy. Increasing the front and rear LiDAR field of view prevents the loss of repositioning. The relocation accuracy can be improved by increasing the left and right LiDAR field of view.
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Affiliation(s)
- Jian Gu
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
- Shandong University of Technology Sub-Center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, National Sub-Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Zibo 255000, China
| | - Yubin Lan
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
- Shandong University of Technology Sub-Center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, National Sub-Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Zibo 255000, China
- National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, College of Engineering, Shandong University of Technology, Guangzhou 510642, China
| | - Fanxia Kong
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
- Shandong University of Technology Sub-Center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, National Sub-Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Zibo 255000, China
| | - Lei Liu
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
- Shandong University of Technology Sub-Center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, National Sub-Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Zibo 255000, China
| | - Haozheng Sun
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
- Shandong University of Technology Sub-Center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, National Sub-Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Zibo 255000, China
| | - Jie Liu
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
- Shandong University of Technology Sub-Center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, National Sub-Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Zibo 255000, China
| | - Lili Yi
- School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 255000, China
- Shandong University of Technology Sub-Center of National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, National Sub-Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Zibo 255000, China
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Yuan X, Maharjan A. Non-rigid point set registration: recent trends and challenges. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10292-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Castillon M, Ridao P, Siegwart R, Cadena C. Linewise Non-Rigid Point Cloud Registration. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3180038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Miguel Castillon
- Computer Vision and Robotics Research Institute (VICOROB), University of Girona, Girona, Spain
| | - Pere Ridao
- Computer Vision and Robotics Research Institute (VICOROB), University of Girona, Girona, Spain
| | - Roland Siegwart
- Autonomous Systems Lab (ASL), ETH Zurich, Zurich, Switzerland
| | - Cesar Cadena
- Autonomous Systems Lab (ASL), ETH Zurich, Zurich, Switzerland
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Zheng X, Ma R, Gao R, Hao Q. Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3162024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Porto A, Rolfe S, Maga AM. ALPACA: A fast and accurate computer vision approach for automated landmarking of three-dimensional biological structures. Methods Ecol Evol 2021; 12:2129-2144. [PMID: 35874971 PMCID: PMC9291522 DOI: 10.1111/2041-210x.13689] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2021] [Indexed: 11/27/2022]
Abstract
Landmark-based geometric morphometrics has emerged as an essential discipline for the quantitative analysis of size and shape in ecology and evolution. With the ever-increasing density of digitized landmarks, the possible development of a fully automated method of landmark placement has attracted considerable attention. Despite the recent progress in image registration techniques, which could provide a pathway to automation, three-dimensional (3D) morphometric data are still mainly gathered by trained experts. For the most part, the large infrastructure requirements necessary to perform image-based registration, together with its system specificity and its overall speed, have prevented its wide dissemination.Here, we propose and implement a general and lightweight point cloud-based approach to automatically collect high-dimensional landmark data in 3D surfaces (Automated Landmarking through Point cloud Alignment and Correspondence Analysis). Our framework possesses several advantages compared with image-based approaches. First, it presents comparable landmarking accuracy, despite relying on a single, random reference specimen and much sparser sampling of the structure's surface. Second, it can be efficiently run on consumer-grade personal computers. Finally, it is general and can be applied at the intraspecific level to any biological structure of interest, regardless of whether anatomical atlases are available.Our validation procedures indicate that the method can recover intraspecific patterns of morphological variation that are largely comparable to those obtained by manual digitization, indicating that the use of an automated landmarking approach should not result in different conclusions regarding the nature of multivariate patterns of morphological variation.The proposed point cloud-based approach has the potential to increase the scale and reproducibility of morphometrics research. To allow ALPACA to be used out-of-the-box by users with no prior programming experience, we implemented it as a SlicerMorph module. SlicerMorph is an extension that enables geometric morphometrics data collection and 3D specimen analysis within the open-source 3D Slicer biomedical visualization ecosystem. We expect that convenient access to this platform will make ALPACA broadly applicable within ecology and evolution.
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Affiliation(s)
- Arthur Porto
- Department of Biological SciencesLouisiana State UniversityBaton RougeLAUSA
- Center for Computation and TechnologyLouisiana State UniversityBaton RougeLAUSA
| | - Sara Rolfe
- Friday Harbor LaboratoriesUniversity of WashingtonSan Juan IslandWAUSA
- Center for Development Biology and Regenerative MedicineSeattle Children's Research InstituteSeattleWAUSA
| | - A. Murat Maga
- Center for Development Biology and Regenerative MedicineSeattle Children's Research InstituteSeattleWAUSA
- Division of Craniofacial MedicineDepartment of PediatricsUniversity of WashingtonSeattleWAUSA
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