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Fleps I, Newman HR, Elliott DM, Morgan EF. Geometric determinants of the mechanical behavior of image-based finite element models of the intervertebral disc. J Orthop Res 2024; 42:1343-1355. [PMID: 38245852 PMCID: PMC11055679 DOI: 10.1002/jor.25788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 12/17/2023] [Accepted: 12/19/2023] [Indexed: 01/22/2024]
Abstract
The intervertebral disc is an important structure for load transfer through the spine. Its injury and degeneration have been linked to pain and spinal fractures. Disc injury and spine fractures are associated with high stresses; however, these stresses cannot be measured, necessitating the use of finite element (FE) models. These models should include the disc's complex structure, as changes in disc geometry have been linked to altered mechanical behavior. However, image-based models using disc-specific structures have yet to be established. This study describes a multiphasic FE modeling approach for noninvasive estimates of subject-specific intervertebral disc mechanical behavior based on medical imaging. The models (n = 22) were used to study the influence of disc geometry on the predicted global mechanical response (moments and forces), internal local disc stresses, and tractions at the interface between the disc and the bone. Disc geometry was found to have a strong influence on the predicted moments and forces on the disc (R2 = 0.69-0.93), while assumptions regarding the side curvature (bulge) of the disc had only a minor effect. Strong variability in the predicted internal disc stresses and tractions was observed between the models (mean absolute differences of 5.1%-27.7%). Disc height had a systematic influence on the internal disc stresses and tractions at the disc-to-bone interface. The influence of disc geometry on mechanics highlights the importance of disc-specific modeling to estimate disc injury risk, loading on the adjacent vertebral bodies, and the mechanical environment present in disc tissues.
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Jiang Y, Zhang W, Huang S, Huang Q, Ye H, Zeng Y, Hua X, Cai J, Liu Z, Liu Q. Preoperative Prediction of New Vertebral Fractures after Vertebral Augmentation with a Radiomics Nomogram. Diagnostics (Basel) 2023; 13:3459. [PMID: 37998595 PMCID: PMC10670105 DOI: 10.3390/diagnostics13223459] [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: 10/12/2023] [Revised: 11/11/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023] Open
Abstract
The occurrence of new vertebral fractures (NVFs) after vertebral augmentation (VA) procedures is common in patients with osteoporotic vertebral compression fractures (OVCFs), leading to painful experiences and financial burdens. We aim to develop a radiomics nomogram for the preoperative prediction of NVFs after VA. Data from center 1 (training set: n = 153; internal validation set: n = 66) and center 2 (external validation set: n = 44) were retrospectively collected. Radiomics features were extracted from MRI images and radiomics scores (radscores) were constructed for each level-specific vertebra based on least absolute shrinkage and selection operator (LASSO). The radiomics nomogram, integrating radiomics signature with presence of intravertebral cleft and number of previous vertebral fractures, was developed by multivariable logistic regression analysis. The predictive performance of the vertebrae was level-specific based on radscores and was generally superior to clinical variables. RadscoreL2 had the optimal discrimination (AUC ≥ 0.751). The nomogram provided good predictive performance (AUC ≥ 0.834), favorable calibration, and large clinical net benefits in each set. It was used successfully to categorize patients into high- or low-risk subgroups. As a noninvasive preoperative prediction tool, the MRI-based radiomics nomogram holds great promise for individualized prediction of NVFs following VA.
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Affiliation(s)
- Yang Jiang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, China; (Y.J.); (W.Z.); (J.C.)
| | - Wei Zhang
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, China; (Y.J.); (W.Z.); (J.C.)
| | - Shihao Huang
- Department of Radiology, The Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai 519000, China;
| | - Qing Huang
- Department of Endocrinology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, China;
| | - Haoyi Ye
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China;
| | - Yurong Zeng
- Department of Radiology, Huizhou Central People’s Hospital, Huizhou 516000, China;
| | - Xin Hua
- Department of Neurology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou 325000, China;
| | - Jinhui Cai
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, China; (Y.J.); (W.Z.); (J.C.)
| | - Zhifeng Liu
- Department of Radiology, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou 511300, China;
| | - Qingyu Liu
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518000, China; (Y.J.); (W.Z.); (J.C.)
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CT-Based Automatic Spine Segmentation Using Patch-Based Deep Learning. INT J INTELL SYST 2023. [DOI: 10.1155/2023/2345835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
CT vertebral segmentation plays an essential role in various clinical applications, such as computer-assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is challenging due to the overlapping shadows of thoracoabdominal structures such as the lungs, bony structures such as the ribs, and other issues such as ambiguous object borders, complicated spine architecture, patient variability, and fluctuations in image contrast. Deep learning is an emerging technique for disease diagnosis in the medical field. This study proposes a patch-based deep learning approach to extract the discriminative features from unlabeled data using a stacked sparse autoencoder (SSAE). 2D slices from a CT volume are divided into overlapping patches fed into the model for training. A random under sampling (RUS)-module is applied to balance the training data by selecting a subset of the majority class. SSAE uses pixel intensities alone to learn high-level features to recognize distinctive features from image patches. Each image is subjected to a sliding window operation to express image patches using autoencoder high-level features, which are then fed into a sigmoid layer to classify whether each patch is a vertebra or not. We validate our approach on three diverse publicly available datasets: VerSe, CSI-Seg, and the Lumbar CT dataset. Our proposed method outperformed other models after configuration optimization by achieving 89.9% in precision, 90.2% in recall, 98.9% in accuracy, 90.4% in F-score, 82.6% in intersection over union (IoU), and 90.2% in Dice coefficient (DC). The results of this study demonstrate that our model’s performance consistency using a variety of validation strategies is flexible, fast, and generalizable, making it suited for clinical application.
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Zhang D, Aoude A, Driscoll M. Development and model form assessment of an automatic subject-specific vertebra reconstruction method. Comput Biol Med 2022; 150:106158. [PMID: 37859278 DOI: 10.1016/j.compbiomed.2022.106158] [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: 04/26/2022] [Revised: 09/09/2022] [Accepted: 09/24/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Current spine models for analog bench models, surgical navigation and training platforms are conventionally based on 3D models from anatomical human body polygon database or from time-consuming manual-labelled data. This work proposed a workflow of quick and accurate subject-specific vertebra reconstruction method and quantified the reconstructed model accuracy and model form errors. METHODS Four different neural networks were customized for vertebra segmentation. To validate the workflow in clinical applications, an excised human lumbar vertebra was scanned via CT and reconstructed into 3D CAD models using four refined networks. A reverse engineering solution was proposed to obtain the high-precision geometry of the excised vertebra as gold standard. The 3D model evaluation metrics and a finite element analysis (FEA) method were designed to reflect the model accuracy and model form errors. RESULTS The automatic segmentation networks achieved the best Dice score of 94.20% in validation datasets. The accuracy of reconstructed models was quantified with the best 3D Dice index of 92.80%, 3D IoU of 86.56%, Hausdorff distance of 1.60 mm, and the heatmaps and histograms were used for error visualization. The FEA results showed the impact of different geometries and reflected partial surface accuracy of the reconstructed vertebra under biomechanical loads with the closest percentage error of 4.2710% compared to the gold standard model. CONCLUSIONS In this work, a workflow of automatic subject-specific vertebra reconstruction method was proposed while the errors in geometry and FEA were quantified. Such errors should be considered when leveraging subject-specific modelling towards the development and improvement of treatments.
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Affiliation(s)
- Dingzhong Zhang
- Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, 845 Sherbrooke St. W, Montréal, Quebec, H3A 0G4, Canada.
| | - Ahmed Aoude
- Orthopaedic Research Laboratory, Research Institute of McGill University Health Centre, Montreal General Hospital, 1650 Cedar Avenue, Montréal, Québec, H3G 1A4, Canada.
| | - Mark Driscoll
- Musculoskeletal Biomechanics Research Lab, Department of Mechanical Engineering, McGill University, 845 Sherbrooke St. W, Montréal, Quebec, H3A 0G4, Canada; Orthopaedic Research Laboratory, Research Institute of McGill University Health Centre, Montreal General Hospital, 1650 Cedar Avenue, Montréal, Québec, H3G 1A4, Canada.
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Fleps I, Morgan EF. A Review of CT-Based Fracture Risk Assessment with Finite Element Modeling and Machine Learning. Curr Osteoporos Rep 2022; 20:309-319. [PMID: 36048316 PMCID: PMC10941185 DOI: 10.1007/s11914-022-00743-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/06/2022] [Indexed: 11/03/2022]
Abstract
PURPOSE OF REVIEW We reviewed advances over the past 3 years in assessment of fracture risk based on CT scans, considering methods that use finite element models, machine learning, or a combination of both. RECENT FINDINGS Several studies have demonstrated that CT-based assessment of fracture risk, using finite element modeling or biomarkers derived from machine learning, is equivalent to currently used clinical tools. Phantomless calibration of CT scans for bone mineral density enables accurate measurements from routinely taken scans. This opportunistic use of CT scans for fracture risk assessment is facilitated by high-quality automated segmentation with deep learning, enabling workflows that do not require user intervention. Modeling of more realistic and diverse loading conditions, as well as improved modeling of fracture mechanisms, has shown promise to enhance our understanding of fracture processes and improve the assessment of fracture risk beyond the performance of current clinical tools. CT-based screening for fracture risk is effective and, by analyzing scans that were taken for other indications, could be used to expand the pool of people screened, therefore improving fracture prevention. Finite element modeling and machine learning both provide valuable tools for fracture risk assessment. Future approaches should focus on including more loading-related aspects of fracture risk.
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Affiliation(s)
- Ingmar Fleps
- College of Mechanical Engineering, Boston University, Boston, USA.
| | - Elise F Morgan
- College of Mechanical Engineering, Boston University, Boston, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
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Patient-Specific Finite Element Modeling of the Whole Lumbar Spine Using Clinical Routine Multi-Detector Computed Tomography (MDCT) Data-A Pilot Study. Biomedicines 2022; 10:biomedicines10071567. [PMID: 35884872 PMCID: PMC9312902 DOI: 10.3390/biomedicines10071567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/24/2022] [Accepted: 06/27/2022] [Indexed: 11/20/2022] Open
Abstract
(1) Background: To study the feasibility of developing finite element (FE) models of the whole lumbar spine using clinical routine multi-detector computed tomography (MDCT) scans to predict failure load (FL) and range of motion (ROM) parameters. (2) Methods: MDCT scans of 12 subjects (6 healthy controls (HC), mean age ± standard deviation (SD): 62.16 ± 10.24 years, and 6 osteoporotic patients (OP), mean age ± SD: 65.83 ± 11.19 years) were included in the current study. Comprehensive FE models of the lumbar spine (5 vertebrae + 4 intervertebral discs (IVDs) + ligaments) were generated (L1−L5) and simulated. The coefficients of correlation (ρ) were calculated to investigate the relationship between FE-based FL and ROM parameters and bone mineral density (BMD) values of L1−L3 derived from MDCT (BMDQCT-L1-3). Finally, Mann−Whitney U tests were performed to analyze differences in FL and ROM parameters between HC and OP cohorts. (3) Results: Mean FE-based FL value of the HC cohort was significantly higher than that of the OP cohort (1471.50 ± 275.69 N (HC) vs. 763.33 ± 166.70 N (OP), p < 0.01). A strong correlation of 0.8 (p < 0.01) was observed between FE-based FL and BMDQCT-L1-L3 values. However, no significant differences were observed between ROM parameters of HC and OP cohorts (p = 0.69 for flexion; p = 0.69 for extension; p = 0.47 for lateral bending; p = 0.13 for twisting). In addition, no statistically significant correlations were observed between ROM parameters and BMDQCT- L1-3. (4) Conclusions: Clinical routine MDCT data can be used for patient-specific FE modeling of the whole lumbar spine. ROM parameters do not seem to be significantly altered between HC and OP. In contrast, FE-derived FL may help identify patients with increased osteoporotic fracture risk in the future.
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Zhang Y, Zhang T, Ge X, Ma Y, Cui Z, Wu S, Liang Y, Zhu S, Li Z. A Three-Dimensional Cement Quantification Method for Decision Prediction of Vertebral Recompression after Vertebroplasty. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2330472. [PMID: 35602341 PMCID: PMC9119757 DOI: 10.1155/2022/2330472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/25/2022] [Indexed: 11/17/2022]
Abstract
Objective Proposing parameters to quantify cement distribution and increasing accuracy for decision prediction of vertebroplasty postoperative complication. Methods Finite element analysis was used to biomechanically assess vertebral mechanics (n = 51) after percutaneous vertebroplasty (PVP) or kyphoplasty (PKP). The vertebral space was divided into 27 portions. The numbers of cement occupied portions and numbers of cement-endplate contact portions were defined as overall distribution number (oDN) and overall endplate contact number (oEP), respectively. And cement distribution was parametrized by oDN and oEP. The determination coefficients of vertebral mechanics and parameters (R 2) can validate the correlation of proposed parameters with vertebral mechanics. Results oDN and oEP were mainly correlated with failure load (R 2 = 0.729) and stiffness (R 2 = 0.684), respectively. oDN, oEP, failure load, and stiffness had obvious difference between the PVP group and the PKP group (P < 0.05). The regional endplate contact number in the front column is most correlated with vertebral stiffness (R 2 = 0.59) among all regional parameters. Cement volume and volume fraction are not dominant factors of vertebral augmentation, and they are not suitable for postoperative fracture risk prediction. Conclusions Proposed parameters with high correlation on vertebral mechanics are promising for clinical utility. The oDN and oEP can strongly affect augmented vertebral mechanics thus is suitable for postoperative fracture risk prediction. The parameters are beneficial for decision-making process of revision surgery necessity. Parametrized methods are also favorable for surgeon's preoperative planning. The methods can be inspirational for clinical image recognition development and auxiliary diagnosis.
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Affiliation(s)
- Yanming Zhang
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin 300350, China
| | - Tao Zhang
- Department of Orthopedic Surgery, Tianjin First Central Hospital, Tianjin 300190, China
| | - Xiang Ge
- Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin 300354, China
| | - Yong Ma
- Pain Department, The Third People's Hospital of Yunnan Province, Kunming 650010, China
| | - Zhenduo Cui
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin 300350, China
| | - Shuilin Wu
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin 300350, China
| | - Yanqin Liang
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin 300350, China
| | - Shengli Zhu
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin 300350, China
| | - Zhaoyang Li
- Tianjin Key Laboratory of Composite and Functional Materials, School of Materials Science and Engineering, Tianjin University, Tianjin 300350, China
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Sollmann N, Kirschke JS, Kronthaler S, Boehm C, Dieckmeyer M, Vogele D, Kloth C, Lisson CG, Carballido-Gamio J, Link TM, Karampinos DC, Karupppasamy S, Beer M, Krug R, Baum T. Imaging of the Osteoporotic Spine - Quantitative Approaches in Diagnostics and for the Prediction of the Individual Fracture Risk. ROFO-FORTSCHR RONTG 2022; 194:1088-1099. [PMID: 35545103 DOI: 10.1055/a-1770-4626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Osteoporosis is a highly prevalent systemic skeletal disease that is characterized by low bone mass and microarchitectural bone deterioration. It predisposes to fragility fractures that can occur at various sites of the skeleton, but vertebral fractures (VFs) have been shown to be particularly common. Prevention strategies and timely intervention depend on reliable diagnosis and prediction of the individual fracture risk, and dual-energy X-ray absorptiometry (DXA) has been the reference standard for decades. Yet, DXA has its inherent limitations, and other techniques have shown potential as viable add-on or even stand-alone options. Specifically, three-dimensional (3 D) imaging modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), are playing an increasing role. For CT, recent advances in medical image analysis now allow automatic vertebral segmentation and value extraction from single vertebral bodies using a deep-learning-based architecture that can be implemented in clinical practice. Regarding MRI, a variety of methods have been developed over recent years, including magnetic resonance spectroscopy (MRS) and chemical shift encoding-based water-fat MRI (CSE-MRI) that enable the extraction of a vertebral body's proton density fat fraction (PDFF) as a promising surrogate biomarker of bone health. Yet, imaging data from CT or MRI may be more efficiently used when combined with advanced analysis techniques such as texture analysis (TA; to provide spatially resolved assessments of vertebral body composition) or finite element analysis (FEA; to provide estimates of bone strength) to further improve fracture prediction. However, distinct and experimentally validated diagnostic criteria for osteoporosis based on CT- and MRI-derived measures have not yet been achieved, limiting broad transfer to clinical practice for these novel approaches. KEY POINTS:: · DXA is the reference standard for diagnosis and fracture prediction in osteoporosis, but it has important limitations.. · CT- and MRI-based methods are increasingly used as (opportunistic) approaches.. · For CT, particularly deep-learning-based automatic vertebral segmentation and value extraction seem promising.. · For MRI, multiple techniques including spectroscopy and chemical shift imaging are available to extract fat fractions.. · Texture and finite element analyses can provide additional measures for vertebral body composition and bone strength.. CITATION FORMAT: · Sollmann N, Kirschke JS, Kronthaler S et al. Imaging of the Osteoporotic Spine - Quantitative Approaches in Diagnostics and for the Prediction of the Individual Fracture Risk. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1770-4626.
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Affiliation(s)
- Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany.,Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States.,Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Jan Stefan Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Sophia Kronthaler
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christof Boehm
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Daniel Vogele
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | | | - Julio Carballido-Gamio
- Department of Radiology, University of Colorado - Anschutz Medical Campus, Aurora, CO, United States
| | - Thomas Marc Link
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
| | - Dimitrios Charalampos Karampinos
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Subburaj Karupppasamy
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design, Singapore.,Sobey School of Business, Saint Mary's University, Halifax, NS, Canada
| | - Meinrad Beer
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Roland Krug
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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Greve T, Rayudu NM, Dieckmeyer M, Boehm C, Ruschke S, Burian E, Kloth C, Kirschke JS, Karampinos DC, Baum T, Subburaj K, Sollmann N. Finite Element Analysis of Osteoporotic and Osteoblastic Vertebrae and Its Association With the Proton Density Fat Fraction From Chemical Shift Encoding-Based Water-Fat MRI - A Preliminary Study. Front Endocrinol (Lausanne) 2022; 13:900356. [PMID: 35898459 PMCID: PMC9313539 DOI: 10.3389/fendo.2022.900356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 05/11/2022] [Indexed: 11/16/2022] Open
Abstract
PURPOSE Osteoporosis is prevalent and entails alterations of vertebral bone and marrow. Yet, the spine is also a common site of metastatic spread. Parameters that can be non-invasively measured and could capture these alterations are the volumetric bone mineral density (vBMD), proton density fat fraction (PDFF) as an estimate of relative fat content, and failure displacement and load from finite element analysis (FEA) for assessment of bone strength. This study's purpose was to investigate if osteoporotic and osteoblastic metastatic changes in lumbar vertebrae can be differentiated based on the abovementioned parameters (vBMD, PDFF, and measures from FEA), and how these parameters correlate with each other. MATERIALS AND METHODS Seven patients (3 females, median age: 77.5 years) who received 3-Tesla magnetic resonance imaging (MRI) and multi-detector computed tomography (CT) of the lumbar spine and were diagnosed with either osteoporosis (4 patients) or diffuse osteoblastic metastases (3 patients) were included. Chemical shift encoding-based water-fat MRI (CSE-MRI) was used to extract the PDFF, while vBMD was extracted after automated vertebral body segmentation using CT. Segmentation masks were used for FEA-based failure displacement and failure load calculations. Failure displacement, failure load, and PDFF were compared between patients with osteoporotic vertebrae versus patients with osteoblastic metastases, considering non-fractured vertebrae (L1-L4). Associations between those parameters were assessed using Spearman correlation. RESULTS Median vBMD was 59.3 mg/cm3 in osteoporotic patients. Median PDFF was lower in the metastatic compared to the osteoporotic patients (11.9% vs. 43.8%, p=0.032). Median failure displacement and failure load were significantly higher in metastatic compared to osteoporotic patients (0.874 mm vs. 0.348 mm, 29,589 N vs. 3,095 N, p=0.034 each). A strong correlation was noted between PDFF and failure displacement (rho -0.679, p=0.094). A very strong correlation was noted between PDFF and failure load (rho -0.893, p=0.007). CONCLUSION PDFF as well as failure displacement and load allowed to distinguish osteoporotic from diffuse osteoblastic vertebrae. Our findings further show strong associations between PDFF and failure displacement and load, thus may indicate complimentary pathophysiological associations derived from two non-invasive techniques (CSE-MRI and CT) that inherently measure different properties of vertebral bone and marrow.
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Affiliation(s)
- Tobias Greve
- Department of Neurosurgery, University Hospital, Ludwig-Maximilians-University (LMU) Munich, Munich, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- *Correspondence: Tobias Greve,
| | - Nithin Manohar Rayudu
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore, Singapore
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christof Boehm
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stefan Ruschke
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Dimitrios C. Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Karupppasamy Subburaj
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore, Singapore
- Sobey School of Business, Saint Mary’s University, Halifax, NS, Canada
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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MDCT-Based Finite Element Analysis for the Prediction of Functional Spine Unit Strength-An In Vitro Study. MATERIALS 2021; 14:ma14195791. [PMID: 34640187 PMCID: PMC8510093 DOI: 10.3390/ma14195791] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/16/2021] [Accepted: 09/29/2021] [Indexed: 11/23/2022]
Abstract
(1) Objective: This study aimed to analyze the effect of ligaments on the strength of functional spine unit (FSU) assessed by finite element (FE) analysis of anatomical models developed from multi-detector computed tomography (MDCT) data. (2) Methods: MDCT scans for cadaveric specimens were acquired from 16 donors (7 males, mean age of 84.29 ± 6.06 years and 9 females, mean age of 81.00 ± 11.52 years). Two sets of FSU models (three vertebrae + two disks), one with and another without (w/o) ligaments, were generated. The vertebrae were segmented semi-automatically, intervertebral disks (IVD) were generated manually, and ligaments were modeled based on the anatomical location. FE-predicted failure loads of FSU models (with and w/o ligaments) were compared with the experimental failure loads obtained from the uniaxial biomechanical test of specimens. (3) Results: The mean and standard deviation of the experimental failure load of FSU specimens was 3513 ± 1029 N, whereas of FE-based failure loads were 2942 ± 943 N and 2537 ± 929 N for FSU models with ligaments and without ligament attachments, respectively. A good correlation (ρ = 0.79, and ρ = 0.75) was observed between the experimental and FE-based failure loads for the FSU model with and with ligaments, respectively. (4) Conclusions: The FE-based FSU model can be used to determine bone strength, and the ligaments seem to have an effect on the model accuracy for the failure load calculation; further studies are needed to understand the contribution of ligaments.
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Finite Element Method for the Evaluation of the Human Spine: A Literature Overview. J Funct Biomater 2021; 12:jfb12030043. [PMID: 34449646 PMCID: PMC8395922 DOI: 10.3390/jfb12030043] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 07/23/2021] [Accepted: 07/29/2021] [Indexed: 02/07/2023] Open
Abstract
The finite element method (FEM) represents a computer simulation method, originally used in civil engineering, which dates back to the early 1940s. Applications of FEM have also been used in numerous medical areas and in orthopedic surgery. Computing technology has improved over the years and as a result, more complex problems, such as those involving the spine, can be analyzed. The spine is a complex anatomical structure that maintains the erect posture and supports considerable loads. Applications of FEM in the spine have contributed to the understanding of bone biomechanics, both in healthy and abnormal conditions, such as scoliosis, fractures (trauma), degenerative disc disease and osteoporosis. However, since FEM is only a digital simulation of the real condition, it will never exactly simulate in vivo results. In particular, when it concerns biomechanics, there are many features that are difficult to represent in a FEM. More FEM studies and spine research are required in order to examine interpersonal spine stiffness, young spine biomechanics and model accuracy. In the future, patient-specific models will be used for better patient evaluations as well as for better pre- and inter-operative planning.
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Sekuboyina A, Husseini ME, Bayat A, Löffler M, Liebl H, Li H, Tetteh G, Kukačka J, Payer C, Štern D, Urschler M, Chen M, Cheng D, Lessmann N, Hu Y, Wang T, Yang D, Xu D, Ambellan F, Amiranashvili T, Ehlke M, Lamecker H, Lehnert S, Lirio M, Olaguer NPD, Ramm H, Sahu M, Tack A, Zachow S, Jiang T, Ma X, Angerman C, Wang X, Brown K, Kirszenberg A, Puybareau É, Chen D, Bai Y, Rapazzo BH, Yeah T, Zhang A, Xu S, Hou F, He Z, Zeng C, Xiangshang Z, Liming X, Netherton TJ, Mumme RP, Court LE, Huang Z, He C, Wang LW, Ling SH, Huỳnh LD, Boutry N, Jakubicek R, Chmelik J, Mulay S, Sivaprakasam M, Paetzold JC, Shit S, Ezhov I, Wiestler B, Glocker B, Valentinitsch A, Rempfler M, Menze BH, Kirschke JS. VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images. Med Image Anal 2021; 73:102166. [PMID: 34340104 DOI: 10.1016/j.media.2021.102166] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 06/25/2021] [Accepted: 07/06/2021] [Indexed: 11/25/2022]
Abstract
Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.
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Affiliation(s)
- Anjany Sekuboyina
- Department of Informatics, Technical University of Munich, Germany; Munich School of BioEngineering, Technical University of Munich, Germany; Department of Neuroradiology, Klinikum Rechts der Isar, Germany.
| | - Malek E Husseini
- Department of Informatics, Technical University of Munich, Germany; Department of Neuroradiology, Klinikum Rechts der Isar, Germany
| | - Amirhossein Bayat
- Department of Informatics, Technical University of Munich, Germany; Department of Neuroradiology, Klinikum Rechts der Isar, Germany
| | | | - Hans Liebl
- Department of Neuroradiology, Klinikum Rechts der Isar, Germany
| | - Hongwei Li
- Department of Informatics, Technical University of Munich, Germany
| | - Giles Tetteh
- Department of Informatics, Technical University of Munich, Germany
| | - Jan Kukačka
- Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Germany
| | - Christian Payer
- Institute of Computer Graphics and Vision, Graz University of Technology, Austria
| | - Darko Štern
- Gottfried Schatz Research Center: Biophysics, Medical University of Graz, Austria
| | - Martin Urschler
- School of Computer Science, The University of Auckland, New Zealand
| | - Maodong Chen
- Computer Vision Group, iFLYTEK Research South China, China
| | - Dalong Cheng
- Computer Vision Group, iFLYTEK Research South China, China
| | - Nikolas Lessmann
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center Nijmegen, The Netherlands
| | - Yujin Hu
- Shenzhen Research Institute of Big Data, China
| | - Tianfu Wang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, China
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Xin Wang
- Department of Electronic Engineering, Fudan University, China; Department of Radiology, University of North Carolina at Chapel Hill, USA
| | | | | | | | | | | | | | | | | | | | - Feng Hou
- Institute of Computing Technology, Chinese Academy of Sciences, China
| | | | | | - Zheng Xiangshang
- College of Computer Science and Technology, Zhejiang University, China; Real Doctor AI Research Centre, Zhejiang University, China
| | - Xu Liming
- College of Computer Science and Technology, Zhejiang University, China
| | | | | | | | - Zixun Huang
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, China
| | - Chenhang He
- Department of Computing, The Hong Kong Polytechnic University, China
| | - Li-Wen Wang
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, China
| | - Sai Ho Ling
- The School of Biomedical Engineering, University of Technology Sydney, Australia
| | - Lê Duy Huỳnh
- EPITA Research and Development Laboratory (LRDE), France
| | - Nicolas Boutry
- EPITA Research and Development Laboratory (LRDE), France
| | - Roman Jakubicek
- Department of Biomedical Engineering, Brno University of Technology, Czech Republic
| | - Jiri Chmelik
- Department of Biomedical Engineering, Brno University of Technology, Czech Republic
| | - Supriti Mulay
- Indian Institute of Technology Madras, India; Healthcare Technology Innovation Centre, India
| | | | | | - Suprosanna Shit
- Department of Informatics, Technical University of Munich, Germany
| | - Ivan Ezhov
- Department of Informatics, Technical University of Munich, Germany
| | | | - Ben Glocker
- Department of Computing, Imperial College London, UK
| | | | - Markus Rempfler
- Friedrich Miescher Institute for Biomedical Engineering, Switzerland
| | - Björn H Menze
- Department of Informatics, Technical University of Munich, Germany; Department for Quantitative Biomedicine, University of Zurich, Switzerland
| | - Jan S Kirschke
- Department of Neuroradiology, Klinikum Rechts der Isar, Germany
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13
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Dieckmeyer M, Rayudu NM, Yeung LY, Löffler M, Sekuboyina A, Burian E, Sollmann N, Kirschke JS, Baum T, Subburaj K. Prediction of incident vertebral fractures in routine MDCT: Comparison of global texture features, 3D finite element parameters and volumetric BMD. Eur J Radiol 2021; 141:109827. [PMID: 34225250 DOI: 10.1016/j.ejrad.2021.109827] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/06/2021] [Accepted: 06/14/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE In this case-control study, we evaluated different quantitative parameters derived from routine multi-detector computed tomography (MDCT) scans with respect to their ability to predict incident osteoporotic vertebral fractures of the thoracolumbar spine. METHODS 16 patients who received baseline and follow-up contrast-enhanced MDCT and were diagnosed with an incident osteoporotic vertebral fracture at follow-up, and 16 age-, sex-, and follow-up-time-matched controls were included in the study. Vertebrae were labelled and segmented using a fully automated pipeline. Volumetric bone mineral density (vBMD), finite element analysis (FEA)-based failure load (FL) and failure displacement (FD), as well as 24 texture features were extracted from L1 - L3 and averaged. Odds ratios (OR) with 95% confidence intervals (CI), expressed per standard deviation decrease, receiver operating characteristic (ROC) area under the curve (AUC), as well as logistic regression models, including all analyzed parameters as independent variables, were used to assess the prediction of incident vertebral fractures. RESULTS The texture feature Correlation (AUC = 0.754, p = 0.014; OR = 2.76, CI = 1.16-6.58) and vBMD (AUC = 0.750, p = 0.016; OR = 2.67, CI = 1.12-6.37) classified incident vertebral fractures best, while the best FEA-based parameter FL showed an AUC = 0.719 (p = 0.035). Correlation was the only significant predictor of incident fractures in the logistic regression analysis of all parameters (p = 0.022). CONCLUSION MDCT-derived FEA parameters and texture features, averaged from L1 - L3, showed only a moderate, but no statistically significant improvement of incident vertebral fracture prediction beyond BMD, supporting the hypothesis that vertebral-specific parameters may be superior for fracture risk assessment.
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Affiliation(s)
- Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Nithin Manohar Rayudu
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
| | - Long Yu Yeung
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
| | - Maximilian Löffler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; Department of Radiology, University Medical Center, Albert-Ludwigs-University Freiburg, Hugstetter Str. 55, 79106 Freiburg, Germany.
| | - Anjany Sekuboyina
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany.
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany.
| | - Karupppasamy Subburaj
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore; Changi General Hospital, 2 Simei Street 3, Singapore 529889, Singapore.
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14
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Sollmann N, Rayudu NM, Yeung LY, Sekuboyina A, Burian E, Dieckmeyer M, Löffler MT, Schwaiger BJ, Gersing AS, Kirschke JS, Baum T, Subburaj K. MDCT-Based Finite Element Analyses: Are Measurements at the Lumbar Spine Associated with the Biomechanical Strength of Functional Spinal Units of Incidental Osteoporotic Fractures along the Thoracolumbar Spine? Diagnostics (Basel) 2021; 11:455. [PMID: 33800876 PMCID: PMC7998199 DOI: 10.3390/diagnostics11030455] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 02/27/2021] [Accepted: 03/02/2021] [Indexed: 11/16/2022] Open
Abstract
Assessment of osteoporosis-associated fracture risk during clinical routine is based on the evaluation of clinical risk factors and T-scores, as derived from measurements of areal bone mineral density (aBMD). However, these parameters are limited in their ability to identify patients at high fracture risk. Finite element models (FEMs) have shown to improve bone strength prediction beyond aBMD. This study aims to investigate whether FEM measurements at the lumbar spine can predict the biomechanical strength of functional spinal units (FSUs) with incidental osteoporotic vertebral fractures (VFs) along the thoracolumbar spine. Multi-detector computed tomography (MDCT) data of 11 patients (5 females and 6 males, median age: 67 years) who underwent MDCT twice (median interval between baseline and follow-up MDCT: 18 months) and sustained an incidental osteoporotic VF between baseline and follow-up scanning were used. Based on baseline MDCT data, two FSUs consisting of vertebral bodies and intervertebral discs (IVDs) were modeled: one standardly capturing L1-IVD-L2-IVD-L3 (FSU_L1-L3) and one modeling the incidentally fractured vertebral body at the center of the FSU (FSU_F). Furthermore, volumetric BMD (vBMD) derived from MDCT, FEM-based displacement, and FEM-based load of the single vertebrae L1 to L3 were determined. Statistically significant correlations (adjusted for a BMD ratio of fracture/L1-L3 segments) were revealed between the FSU_F and mean load of L1 to L3 (r = 0.814, p = 0.004) and the mean vBMD of L1 to L3 (r = 0.745, p = 0.013), whereas there was no statistically significant association between the FSU_F and FSU_L1-L3 or between FSU_F and the mean displacement of L1 to L3 (p > 0.05). In conclusion, FEM measurements of single vertebrae at the lumbar spine may be able to predict the biomechanical strength of incidentally fractured vertebral segments along the thoracolumbar spine, while FSUs seem to predict only segment-specific fracture risk.
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Affiliation(s)
- Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (N.S.); (A.S.); (E.B.); (M.D.); (M.T.L.); (B.J.S.); (J.S.K.); (T.B.)
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Nithin Manohar Rayudu
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (N.M.R.); (L.Y.Y.)
| | - Long Yu Yeung
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (N.M.R.); (L.Y.Y.)
| | - Anjany Sekuboyina
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (N.S.); (A.S.); (E.B.); (M.D.); (M.T.L.); (B.J.S.); (J.S.K.); (T.B.)
| | - Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (N.S.); (A.S.); (E.B.); (M.D.); (M.T.L.); (B.J.S.); (J.S.K.); (T.B.)
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (N.S.); (A.S.); (E.B.); (M.D.); (M.T.L.); (B.J.S.); (J.S.K.); (T.B.)
| | - Maximilian T. Löffler
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (N.S.); (A.S.); (E.B.); (M.D.); (M.T.L.); (B.J.S.); (J.S.K.); (T.B.)
- Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany
| | - Benedikt J. Schwaiger
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (N.S.); (A.S.); (E.B.); (M.D.); (M.T.L.); (B.J.S.); (J.S.K.); (T.B.)
| | - Alexandra S. Gersing
- Institute of Neuroradiology, University Hospital, LMU Munich, Marchioninistrasse 15, 81377 Munich, Germany;
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (N.S.); (A.S.); (E.B.); (M.D.); (M.T.L.); (B.J.S.); (J.S.K.); (T.B.)
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str. 22, 81675 Munich, Germany; (N.S.); (A.S.); (E.B.); (M.D.); (M.T.L.); (B.J.S.); (J.S.K.); (T.B.)
| | - Karupppasamy Subburaj
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (N.M.R.); (L.Y.Y.)
- Changi General Hospital, 2 Simei Street 3, Singapore 529889, Singapore
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Yeung LY, Rayudu NM, Löffler M, Sekuboyina A, Burian E, Sollmann N, Dieckmeyer M, Greve T, Kirschke JS, Subburaj K, Baum T. Prediction of Incidental Osteoporotic Fractures at Vertebral-Specific Level Using 3D Non-Linear Finite Element Parameters Derived from Routine Abdominal MDCT. Diagnostics (Basel) 2021; 11:208. [PMID: 33573295 PMCID: PMC7911185 DOI: 10.3390/diagnostics11020208] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 01/27/2021] [Accepted: 01/28/2021] [Indexed: 02/06/2023] Open
Abstract
To investigate whether finite element (FE) analysis of the spine in routine thoracic/abdominal multi-detector computed tomography (MDCT) can predict incidental osteoporotic fractures at vertebral-specific level; Baseline routine thoracic/abdominal MDCT scans of 16 subjects (8(m), mean age: 66.1 ± 8.2 years and 8(f), mean age: 64.3 ± 9.5 years) who sustained incidental osteoporotic vertebral fractures as confirmed in follow-up MDCTs were included in the current study. Thoracic and lumbar vertebrae (T5-L5) were automatically segmented, and bone mineral density (BMD), finite element (FE)-based failure-load, and failure-displacement were determined. These values of individual vertebrae were normalized globally (g), by dividing the absolute value with the average of L1-3 and locally by dividing the absolute value with the average of T5-12 and L1-5 for thoracic and lumbar vertebrae, respectively. Mean-BMD of L1-3 was determined as reference. Receiver operating characteristics (ROC) and area under the curve (AUC) were calculated for different normalized FE (Kload, Kdisplacement,K(load)g, and K(displacement)g) and BMD (KBMD, and K(BMD)g) ratio parameter combinations for identifying incidental fractures. Kload, K(load)g, KBMD, and K(BMD)g showed significantly higher discriminative power compared to standard mean BMD of L1-3 (BMDStandard) (AUC = 0.67 for Kload; 0.64 for K(load)g; 0.64 for KBMD; 0.61 for K(BMD)g vs. 0.54 for BMDStandard). The combination of Kload, Kdisplacement, and KBMD increased the AUC further up to 0.77 (p < 0.001). The combination of FE with BMD measurements derived from routine thoracic/abdominal MDCT allowed an improved prediction of incidental fractures at vertebral-specific level.
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Affiliation(s)
- Long Yu Yeung
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (L.Y.Y.); (N.M.R.)
| | - Nithin Manohar Rayudu
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (L.Y.Y.); (N.M.R.)
| | - Maximilian Löffler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
| | - Anjany Sekuboyina
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
| | - Egon Burian
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
- TUM-Neuroimaging Center, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
| | - Tobias Greve
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
- Department of Neurosurgery, Ludwig-Maximilians-University, Marchioninistraße 15, 81377 Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
- TUM-Neuroimaging Center, Klinikum Rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Karupppasamy Subburaj
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore; (L.Y.Y.); (N.M.R.)
- Changi General Hospital, 2 Simei Street 3, Singapore 529889, Singapore
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum Rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Street 22, 81675 Munich, Germany; (M.L.); (A.S.); (E.B.); (N.S.); (M.D.); (T.G.); (J.S.K.); (T.B.)
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Rayudu NM, Subburaj K, Mei K, Dieckmeyer M, Kirschke JS, Noël PB, Baum T. Finite Element Analysis-Based Vertebral Bone Strength Prediction Using MDCT Data: How Low Can We Go? Front Endocrinol (Lausanne) 2020; 11:442. [PMID: 32849260 PMCID: PMC7399039 DOI: 10.3389/fendo.2020.00442] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Accepted: 06/04/2020] [Indexed: 12/16/2022] Open
Abstract
Objective: To study the impact of dose reduction in MDCT images through tube current reduction or sparse sampling on the vertebral bone strength prediction using finite element (FE) analysis for fracture risk assessment. Methods: Routine MDCT data covering lumbar vertebrae of 12 subjects (six male; six female; 74.70 ± 9.13 years old) were included in this study. Sparsely sampled and virtually reduced tube current-based MDCT images were computed using statistical iterative reconstruction (SIR) with reduced dose levels at 50, 25, and 10% of the tube current and original projections, respectively. Subject-specific static non-linear FE analyses were performed on vertebra models (L1, L2, and L3) 3-D-reconstructed from those dose-reduced MDCT images to predict bone strength. Coefficient of correlation (R2), Bland-Altman plots, and root mean square coefficient of variation (RMSCV) were calculated to find the variation in the FE-predicted strength at different dose levels, using high-intensity dose-based strength as the reference. Results: FE-predicted failure loads were not significantly affected by up to 90% dose reduction through sparse sampling (R2 = 0.93, RMSCV = 8.6% for 50%; R2 = 0.89, RMSCV = 11.90% for 75%; R2 = 0.86, RMSCV = 11.30% for 90%) and up to 50% dose reduction through tube current reduction method (R2 = 0.96, RMSCV = 12.06%). However, further reduction in dose with the tube current reduction method affected the ability to predict the failure load accurately (R2 = 0.88, RMSCV = 22.04% for 75%; R2 = 0.43, RMSCV = 54.18% for 90%). Conclusion: Results from this study suggest that a 50% radiation dose reduction through reduced tube current and a 90% radiation dose reduction through sparse sampling can be used to predict vertebral bone strength. Our findings suggest that the sparse sampling-based method performs better than the tube current-reduction method in generating images required for FE-based bone strength prediction models.
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Affiliation(s)
- Nithin Manohar Rayudu
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore, Singapore
| | - Karupppasamy Subburaj
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), Singapore, Singapore
| | - Kai Mei
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Michael Dieckmeyer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Jan S. Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Peter B. Noël
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
- *Correspondence: Thomas Baum
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