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Schmidt I, Albert J, Ritthaler M, Papastavrou A, Steinmann P. Bone fracture healing within a continuum bone remodelling framework. Comput Methods Biomech Biomed Engin 2021; 25:1040-1050. [PMID: 34730042 DOI: 10.1080/10255842.2021.1998465] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Bone fracture healing is a complex process which is still under research. Computer-aided patient-specific prediction of bone development, fracture risk, prevention and treatment approaches promises a significant milestone in clinical practice. With this long-term goal in mind, a novel model is presented and examined in this work in the context of continuum bone remodelling. Therein, a clear distinction is made between external mechanical stimulation and the biological healing process of an injured bone tissue. The model is implemented within a finite element framework and investigated for the example of a fractured proximal femur head. The results show promising perspectives for further application. Besides, the model offers the possibility of easily integrating other factors like age-dependency and the availability of nutrition. For the future, further studies with large clinical datasets are essential for validation.
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Affiliation(s)
- Ina Schmidt
- Faculty of Mechanical Engineering, Nuremberg Tech, Nuremberg, Germany
| | - Jacob Albert
- Institute of Applied Mechanics, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Marina Ritthaler
- Faculty of Mechanical Engineering, Nuremberg Tech, Nuremberg, Germany
| | - Areti Papastavrou
- Faculty of Mechanical Engineering, Nuremberg Tech, Nuremberg, Germany
| | - Paul Steinmann
- Institute of Applied Mechanics, University of Erlangen-Nuremberg, Erlangen, Germany
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Falcinelli C, Whyne C. Image-based finite-element modeling of the human femur. Comput Methods Biomech Biomed Engin 2020; 23:1138-1161. [PMID: 32657148 DOI: 10.1080/10255842.2020.1789863] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Fracture is considered a critical clinical endpoint in skeletal pathologies including osteoporosis and bone metastases. However, current clinical guidelines are limited with respect to identifying cases at high risk of fracture, as they do not account for many mechanical determinants that contribute to bone fracture. Improving fracture risk assessment is an important area of research with clear clinical relevance. Patient-specific numerical musculoskeletal models generated from diagnostic images are widely used in biomechanics research and may provide the foundation for clinical tools used to quantify fracture risk. However, prior to clinical translation, in vitro validation of predictions generated from such numerical models is necessary. Despite adopting radically different models, in vitro validation of image-based finite element (FE) models of the proximal femur (predicting strains and failure loads) have shown very similar, encouraging levels of accuracy. The accuracy of such in vitro models has motivated their application to clinical studies of osteoporotic and metastatic fractures. Such models have demonstrated promising but heterogeneous results, which may be explained by the lack of a uniform strategy with respect to FE modeling of the human femur. This review aims to critically discuss the state of the art of image-based femoral FE modeling strategies, highlighting principal features and differences among current approaches. Quantitative results are also reported with respect to the level of accuracy achieved from in vitro evaluations and clinical applications and are used to motivate the adoption of a standardized approach/workflow for image-based FE modeling of the femur.
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Affiliation(s)
- Cristina Falcinelli
- Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, Canada
| | - Cari Whyne
- Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, Toronto, Canada
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Rayudu NM, Anitha DP, Mei K, Zoffl F, Kopp FK, Sollmann N, Löffler MT, Kirschke JS, Noël PB, Subburaj K, Baum T. Low-dose and sparse sampling MDCT-based femoral bone strength prediction using finite element analysis. Arch Osteoporos 2020; 15:17. [PMID: 32088769 DOI: 10.1007/s11657-020-0708-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 01/06/2020] [Indexed: 02/03/2023]
Abstract
UNLABELLED This study aims to evaluate the impact of dose reduction through tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis. FE-predicted femoral failure load obtained from MDCT scan data was not significantly affected by 50% dose reductions through sparse sampling. Further decrease in dose through sparse sampling (25% of original projections) and virtually reduced tube current (50% and 25% of the original dose) showed significant effects on the FE-predicted failure load results. PURPOSE To investigate the effect of virtually reduced tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis. METHODS Routine MDCT data covering the proximal femur of 21 subjects (17 males; 4 females; mean age, 71.0 ± 8.8 years) without any bone diseases aside from osteoporosis were included in this study. Fifty percent and 75% dose reductions were achieved by virtually reducing tube current and by applying a sparse sampling strategy from the raw image data. Images were then reconstructed with a statistically iterative reconstruction algorithm. FE analysis was performed on all reconstructed images and the failure load was calculated. The root mean square coefficient of variation (RMSCV) and coefficient of correlation (R2) were calculated to determine the variation in the FE-predicted failure load data for dose reductions, using original-dose MDCT scan as the standard of reference. RESULTS Fifty percent dose reduction through sparse sampling showed lower RMSCV and higher correlations when compared with virtually reduced tube current method (RMSCV = 5.70%, R2 = 0.96 vs. RMSCV = 20.78%, R2 = 0.79). Seventy-five percent dose reduction achieved through both methods (RMSCV = 22.38%, R2 = 0.80 for sparse sampling; RMSCV = 24.58%, R2 = 0.73 for reduced tube current) could not predict the failure load accurately. CONCLUSION Our simulations indicate that up to 50% reduction in radiation dose through sparse sampling can be used for FE-based prediction of femoral failure load. Sparse-sampled MDCT may allow fracture risk prediction and treatment monitoring in osteoporosis with less radiation exposure in the future.
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Affiliation(s)
- Nithin Manohar Rayudu
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - D Praveen Anitha
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore
| | - Kai Mei
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Florian Zoffl
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Felix K Kopp
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Maximilian T Löffler
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
| | - Peter B Noël
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Karupppasamy Subburaj
- Engineering Product Development (EPD) Pillar, Singapore University of Technology and Design (SUTD), 8 Somapah Road, Singapore, 487372, Singapore.
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675, Munich, Germany
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Effects of dose reduction on bone strength prediction using finite element analysis. Sci Rep 2016; 6:38441. [PMID: 27934902 PMCID: PMC5146932 DOI: 10.1038/srep38441] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2016] [Accepted: 11/08/2016] [Indexed: 01/29/2023] Open
Abstract
This study aimed to evaluate the effect of dose reduction, by means of tube exposure reduction, on bone strength prediction from finite-element (FE) analysis. Fresh thoracic mid-vertebrae specimens (n = 11) were imaged, using multi-detector computed tomography (MDCT), at different intensities of X-ray tube exposures (80, 150, 220 and 500 mAs). Bone mineral density (BMD) was estimated from the mid-slice of each specimen from MDCT images. Differences in image quality and geometry of each specimen were measured. FE analysis was performed on all specimens to predict fracture load. Paired t-tests were used to compare the results obtained, using the highest CT dose (500 mAs) as reference. Dose reduction had no significant impact on FE-predicted fracture loads, with significant correlations obtained with reference to 500 mAs, for 80 mAs (R2 = 0.997, p < 0.001), 150 mAs (R2 = 0.998, p < 0.001) and 220 mAs (R2 = 0.987, p < 0.001). There were no significant differences in volume quantification between the different doses examined. CT imaging radiation dose could be reduced substantially to 64% with no impact on strength estimates obtained from FE analysis. Reduced CT dose will enable early diagnosis and advanced monitoring of osteoporosis and associated fracture risk.
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