Acoustic emission signals can discriminate between compressive bone fractures and tensile ligament injuries in the spine during dynamic loading

Toen, Carolyn Van; Street, John; Oxland, Thomas R.; Cripton, Peter A.

doi:10.1016/j.jbiomech.2012.03.025

Cited by 26 publications

(10 citation statements)

References 35 publications

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“…This underscores the importance of gathering the full set of biomechanical metrics for each specimen, a factor not emphasized in the current ISO approach. (Arun et al, 2014;Cormier et al, 2008;Cormier et al, 2011;Funk et al, 2002;Lockner, 1993;McKay and Bir, 2009;Shridharani JK et al, 2014;Van Toen et al, 2012). From this perspective, it will be prudent to resort to the left censoring scheme to identify injury data points for the development of human injury probability curves from survival analysis.…”

Section: Discussionmentioning

confidence: 99%

Deriving injury risk curves using survival analysis from biomechanical experiments

Yoganandan

Banerjee

Hsu

et al. 2016

Journal of Biomechanics

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Injury risk curves from biomechanical experimental data analysis are used in automotive studies to improve crashworthiness and advance occupant safety. Metrics such as acceleration and deflection coupled with outcomes such as fractures and anatomical disruptions from impact tests are used in simple binary regression models. As an improvement, the International Standards Organization suggested a different approach. It was based on survival analysis. While probability curves for side-impact-induced thorax and abdominal injuries and frontal impact-induced foot-ankle-leg injuries are developed using this approach, deficiencies are apparent. The objective of this study is to present an improved, robust and generalizable methodology in an attempt to resolve these issues. It includes: (a) statistical identification of the most appropriate independent variable (metric) from a pool of candidate metrics, measured and or derived during experimentation and analysis processes, based on the highest area under the receiver operator curve, (b) quantitative determination of the most optimal probability distribution based on the lowest Akaike information criterion, (c) supplementing the qualitative/visual inspection method for comparing the selected distribution with a non-parametric distribution with objective measures, (d) identification of overly influential observations using different methods, and (e) estimation of confidence intervals using techniques more appropriate to the underlying survival statistical model. These clear and quantified details can be easily implemented with commercial/open source packages. They can be used in retrospective analysis and prospective design of experiments, and in applications to different loading scenarios such as underbody blast events. The feasibility of the methodology is demonstrated using post mortem human subject experiments and 24 metrics associated with thoracic/abdominal injuries in side-impacts.

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Section: Discussionmentioning

confidence: 99%

Deriving injury risk curves using survival analysis from biomechanical experiments

Yoganandan

Banerjee

Hsu

et al. 2016

Journal of Biomechanics

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“…The gain for the pre-amplifier of AE channel was set at 40 dB, and the sample rate at 20 MHz. The threshold for AE channel was set at 40 dB and the band-pass analogue filter at 20 to 300 kHz to eliminate any false triggers and to filter out noise from the machine [23]. Timing parameters for AE channel (peak definition time, 50 s; hit definition time, 200 s; hit lockout time, 300 s) were set based on the previous pencil lead break test [15,22].…”

Section: Mechanical Tests and Ae Measurementmentioning

confidence: 99%

Effects of bone damage on creep behaviours of human vertebral trabeculae

O’Callaghan

Szarko

Wang

et al. 2018

Bone

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A subgroup of patients suffering with vertebral fractures can develop progressive spinal deformities over time. The mechanism underlying such clinical observation, however, remains unknown. Previous studies suggested that creep deformation of the vertebral trabeculae may play a role. Using the acoustic emission (AE) technique, this study investigated effects of bone damage (modulus reduction) on creep behaviours of vertebral trabecular bone. Thirty-seven human vertebral trabeculae samples were randomly assigned into five groups (A to E). Bones underwent mechanical tests using similar experimental protocols but varied degree of bone damage was induced. Samples first underwent creep test (static compressive stress of 0.4MPa) for 30min, and then were loaded in compression to a specified strain level (0.4%, 1.0%, 1.5%, 2.5%, and 4% for group A to E, respectively) to induce different degrees of bone damage (0.4%, no damage control; 1.0%, yield strain; 1.5%, beyond yield strain, 2.5% and 4%, post-ultimate strains). Samples were creep loaded (0.4MPa) again for 30min. AE techniques were used to monitor bone damage. Bone damage increased significantly from group A to E (P<0.05), with >30% of modulus reduction in group D and E. Before compressive loading, creep deformation was not different among the five groups and AE hits in creep test were rare. After compressive loading, creep deformation was significantly greater in group D and E than those in other groups (P<0.05). The number of AE hits and other AE measurements during creep test were significantly greater in group D and E than in group A, B, and C (P<0.05 for all). Data suggested that with the increase of vertebral trabecular bone damage, substantial creep deformation may occur even when the vertebra was under physiological loads. The boosted creep deformation observed may be attributed to newly created trabecular microfractures. Findings provide a possible explanation as to why some vertebral fracture patients develop progressive spinal deformity over time.

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“…As a result, studies of injury due to high-rate loading have largely been limited to post-test medical imaging. 3,32,36 A reliable technique that allows the rapid identification and differentiation between different vertebral fracture types is not yet available.…”

Section: Introductionmentioning

confidence: 99%

“…While µCT has been used to visualise bone fracture using incremental quasi-static loading, 18,24 three-dimensional imaging sequences are not yet capable of acquiring the temporal characteristics of a bone fracture, as fracture initiation and propagation has been observed to occur in less than 0.50 ms. 19 An alternative approach to detecting a fracture event is with Acoustic Emission (AE) sensors, which detect the transient elastic waves generated by the localised releases of strain energy. This approach has been used to identify severe compression fractures of the vertebral body under dynamic axial compression 11,36 and has demonstrated thresholds in AE amplitude, timing and frequency content that corresponded to the fracture event. However, these studies reported axial strains in excess of 25% of the vertebral body height and did not distinguish between cortical and trabecular bone failure.…”

Section: Introductionmentioning

confidence: 99%

Cortical and Trabecular Bone Fracture Characterisation in the Vertebral Body Using Acoustic Emission

et al. 2019

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Acoustic emission signals can discriminate between compressive bone fractures and tensile ligament injuries in the spine during dynamic loading

Cited by 26 publications

References 35 publications

Deriving injury risk curves using survival analysis from biomechanical experiments

Deriving injury risk curves using survival analysis from biomechanical experiments

Effects of bone damage on creep behaviours of human vertebral trabeculae

Cortical and Trabecular Bone Fracture Characterisation in the Vertebral Body Using Acoustic Emission

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