Digital volume correlation for the characterization of musculoskeletal tissues: current challenges and future developments

Biological tissues are complex hierarchical materials, difficult to characterise due to the challenges associated to the separation of scale and heterogeneity of the mechanical properties at different dimensional levels. The Digital Volume Correlation approach is the only image-based experimental approach that can accurately measure internal strain field within biological tissues under complex loading scenarios. In this minireview examples of DVC applications to study the deformation of musculoskeletal tissues at different dimensional scales are reported, highlighting the potential and challenges of this relatively new technique. The manuscript aims at reporting the wide breath of DVC applications in the past 2 decades and discuss future perspective for this unique technique, including fast analysis, applications on soft tissues, high precision approaches, and clinical applications

The Role of the Loading Condition in Predictions of Bone Adaptation in a Mouse Tibial Loading Model

The in vivo mouse tibial loading model is used to evaluate the effectiveness of mechanical loading treatment against skeletal diseases. Although studies have correlated bone adaptation with the induced mechanical stimulus, predictions of bone remodeling remained poor, and the interaction between external and physiological loading in engendering bone changes have not been determined. The aim of this study was to determine the effect of passive mechanical loading on the strain distribution in the mouse tibia and its predictions of bone adaptation. Longitudinal micro-computed tomography (micro-CT) imaging was performed over 2 weeks of cyclic loading from weeks 18 to 22 of age, to quantify the shape change, remodeling, and changes in densitometric properties. Micro-CT based finite element analysis coupled with an optimization algorithm for bone remodeling was used to predict bone adaptation under physiological loads, nominal 12N axial load and combined nominal 12N axial load superimposed to the physiological load. The results showed that despite large differences in the strain energy density magnitudes and distributions across the tibial length, the overall accuracy of the model and the spatial match were similar for all evaluated loading conditions. Predictions of densitometric properties were most similar to the experimental data for combined loading, followed closely by physiological loading conditions, despite no significant difference between these two predicted groups. However, all predicted densitometric properties were significantly different for the 12N and the combined loading conditions. The results suggest that computational modeling of bone’s adaptive response to passive mechanical loading should include the contribution of daily physiological load

Automatic segmentation of skeletal muscles from MR images using modified U-Net and a novel data augmentation approach

Rapid and accurate muscle segmentation is essential for the diagnosis and monitoring of many musculoskeletal diseases. As gold standard, manual annotation suffers from intensive labor and high inter-operator reproducibility errors. In this study, deep learning (DL) based automatic muscle segmentation from MR scans is investigated for post-menopausal women, who normally experience a decline in muscle volume. The performance of four Deep Learning (DL) models was evaluated: U-Net and UNet++ and two modified U-Net networks, which combined feature fusion and attention mechanisms (Feature-Fusion-UNet, FFU, and Attention-Feature-Fusion-UNet, AFFU). The models were tested for automatic segmentation of 16-lower limb muscles from MRI scans of two cohorts of post-menopausal women (11 subjects in PMW-1, 8 subjects in PMW-2; from two different studies so considered independent datasets) and 10 obese post-menopausal women (PMW-OB). Furthermore, a novel data augmentation approach is proposed to enlarge the training dataset. The results were assessed and compared by using the Dice similarity coefficient (DSC), relative volume error (RVE), and Hausdorff distance (HD). The best performance among all four DL models was achieved by AFFU (PMW-1: DSC 0.828 ± 0.079, 1-RVE 0.859 ± 0.122, HD 29.9 mm ± 26.5 mm; PMW-2: DSC 0.833 ± 0.065, 1-RVE 0.873 ± 0.105, HD 25.9 mm ± 27.9 mm; PMW-OB: DSC 0.862 ± 0.048, 1-RVE 0.919 ± 0.076, HD 34.8 mm ± 46.8 mm). Furthermore, the augmentation of data significantly improved the DSC scores of U-Net and AFFU for all 16 tested muscles (between 0.23% and 2.17% (DSC), 1.6%–1.93% (1-RVE), and 9.6%–19.8% (HD) improvement). These findings highlight the feasibility of utilizing DL models for automatic segmentation of muscles in post-menopausal women and indicate that the proposed augmentation method can enhance the performance of models trained on small datasets

Effect of radiation-induced damage of trabecular bone tissue evaluated using indentation and digital volume correlation

Exposure to X-ray radiation for an extended amount of time can cause damage to the bone tissue and therefore affect its mechanical properties. Specifically, high-resolution X-ray Computed Tomography (XCT), in both synchrotron and lab-based systems, has been employed extensively for evaluating bone micro-to-nano architecture. However, to date, it is still unclear how long exposures to X-ray radiation affect the mechanical properties of trabecular bone, particularly in relation to lab-XCT systems. Indentation has been widely used to identify local mechanical properties such as hardness and elastic modulus of bone and other biological tissues. The purpose of this study is therefore, to use indentation and XCT-based investigative tools such as digital volume correlation (DVC) to assess the microdamage induced by long exposure of trabecular bone tissue to X-ray radiation and how this affects its local mechanical properties. Trabecular bone specimens were indented before and after X-ray exposures of 33 and 66 h, where variation of elastic modulus was evaluated at every stage. The resulting elastic modulus was decreased, and micro-cracks appeared in the specimens after the first long X-ray exposure and crack formation increased after the second exposure. High strain concentration around the damaged tissue exceeding 1% was also observed from DVC analysis. The outcomes of this study show the importance of designing appropriate XCT-based experiments in lab systems to avoid degradation of the bone tissue mechanical properties due to radiation and these results will help to inform future studies that require long X-ray exposure for in situ experiments or generation of reliable subject-specific computational models

Critical COVID-19 Patients Through First, Second And Third Wave: Retrospective Observational Study Comparing Outcomes In ICU.

Introduction- The time-course of the COVID-19 pandemic was characterized by subsequent waves identified by peaks of Intensive Care Unit (ICU) admission rates. During these periods, progressive knowledge of the disease led to the development of specific therapeutic strategies. This retrospective study investigates whether this led to improvement in outcomes of COVID-19 patients admitted to ICU. Methods- Outcomes were evaluated in consecutive adult COVID19 patients admitted to our ICU, divided into three waves based on the admission period: the first wave from February 25th, 2020, to July 6th, 2020; the second wave from September 20th, 2020, to February 13th, 2021; the third wave from February 14th, 2021 to April 30th, 2021. Differences were assessed comparing outcomes and by using different multivariable Cox models adjusted for variables related to outcome. Further sensitivity analysis was performed in patients undergoing invasive mechanical ventilation. Results- Overall, 428 patients were included in the analysis: 102, 169 and 157 patients in the first, second and third wave. The ICU and in-hospital crude mortalities were lower by 7% and 10% in the third wave compared to the other 2 waves (p>0.05). A higher number of ICU and hospital free days at day 90 was found in the third wave when compared to the other 2 waves (p=0.001). Overall, 62.6% underwent invasive ventilation, with decreasing requirement during the waves (p=0.002). The adjusted Cox model showed no difference in the Hazard Ratio for mortality among the waves. In the propensity-matched analysis the hospital mortality rate was reduced by 11% in the third wave (p=0.044). Conclusions - With application of best practice as known by the time of the first three waves of the pandemic, our study failed to identify a significant improvement in mortality rate when comparing the different waves of the COVID-19 pandemic, notwithstanding, the sub-analyses showed a trend in mortality reduction in the third wave. Rather, our study identified a possible positive effect of dexamethasone on mortality rate reduction and the increased risk of death related to bacterial infections in the three waves

Digital volume correlation can be used to estimate local strains in natural and augmented vertebrae: An organ-level study

Digital Volume Correlation (DVC) has become popular for measuring the strain distribution inside bone structures. A number of methodological questions are still open: the reliability of DVC to investigate augmented bone tissue, the variability of the errors between different specimens of the same type, the distribution of measurement errors inside a bone, and the possible presence of preferential directions. To address these issues, five augmented and five natural porcine vertebrae were subjected to repeated zero-strain micro-CT scan (39 μm voxel size). The acquired images were processed with two independent DVC approaches (a local and a global one), considering different computation sub-volume sizes, in order to assess the strain measurement uncertainties. The systematic errors generally ranged within ±100 microstrain and did not depend on the computational sub-volume. The random error was higher than 1000 microstrain for the smallest sub-volume and rapidly decreased: with a sub-volume of 48 voxels the random errors were typically within 200 microstrain for both DVC approaches. While these trends were rather consistent within the sample, two individual specimens had unpredictably larger errors. For this reason, a zero-strain check on each specimen should always be performed before any in-situ micro-CT testing campaign. This study clearly shows that, when sufficient care is dedicated to preliminary methodological work, different DVC computation approaches allow measuring the strain with a reduced overall error (approximately 200 microstrain). Therefore, DVC is a viable technique to investigate strain in the elastic regime in natural and augmented bones

From bed to bench: how in silico medicine can help ageing research.

Driven by the raising ethical concerns surrounding animal experimentation, there is a growing interest for non-animal methods, in vitro or in silico technologies that can be used to reduce, refine, and replace animal experimentation. In addition, animal experimentation is being critically revised in regard to its ability to predict clinical outcomes. In this manuscript we describe an initial exploration where a set of in vivo imaging based subject-specific technologies originally developed to predict the risk of femoral strength and hip fracture in osteoporotic patients, were adapted to assess the efficacy of bone drugs pre-clinically on mice. The CT2S technology we developed generates subject-specific models based on Computed Tomography that can separate fractured and non-fractured patients with an accuracy of 82%. When used in mouse experiments the use of in vivo imaging and modelling was found to improve the reproducibility of Bone Mineral Content measurements to a point where up to 63% less mice would be required to achieve the same statistical power of a conventional cross-sectional study. We also speculate about a possible approach where animal-specific and patient-specific models could be used to better translate the observation made on animal models into predictions of response in humans

Strain uncertainties from two digital volume correlation approaches in prophylactically augmented vertebrae: local analysis on bone and cement-bone microstructures

Combination of micro-focus computed tomography (micro-CT) in conjunction with in situ mechanical testing and digital volume correlation (DVC) can be used to access the internal deformation of materials and structures. DVC has been exploited over the past decade to measure complex deformation fields within biological tissues and bone-biomaterial systems. However, before adopting it in a clinically-relevant context (i.e. bone augmentation in vertebroplasty), the research community should focus on understanding the reliability of such method in different orthopaedic applications involving the use of biomaterials. The aim of this study was to evaluate systematic and random errors affecting the strain computed with two different DVC approaches (a global one, “ShIRT-FE”, and a local one, “DaVis-DC”) in different microstructures within augmented vertebrae, such as trabecular bone, cortical bone and cement-bone interdigitation. The results showed that systematic error was insensitive to the size of the computation sub-volume used for the DVC correlation. Conversely, the random error (which was generally the largest component of error) was lower for a 48-voxel (1872 μm) sub-volume (64–221 microstrain for ShIRT-FE, 88–274 microstrain for DaVis-DC), than for a 16-voxel (624 μm) sub-volume (359–1203 microstrain for ShIRT-FE, 960–1771 microstrain for DaVis-DC) for the trabecular and cement regions. Overall, the local random error did not appear to be influenced by either bone microarchitecture or presence of biomaterial. For the 48-voxel sub-volume the global approach was less sensitive to the gradients in grey-values at the cortical surface (random error below 200 microstrain), while the local approach showed errors up to 770 microstrain. Mean absolute error (MAER) and standard deviation of error (SDER) were also calculated and substantially improved when compared to recent literature for the cement-bone interface. The multipass approach for DaVis-DC further reduced the random error for the largest volume of interest. The random error did not follow any recognizable pattern with the six strain components and only ShiRT-FE seemed to produce lower random errors in the normal strains. In conclusion this study has provided, for the first time, a preliminary indication of the reliability and limitations for the application of DVC in estimating the micromechanics of bone and cement-bone interface in augmented vertebrae

Effect of repeated in vivo microCT imaging on the properties of the mouse tibia

In longitudinal studies, in vivo micro-Computed Tomography (microCT) imaging is used to investigate bone changes over time due to interventions in mice. However, ionising radiation can provoke significant variations in bone morphometric parameters. In a previous study, we evaluated the effect of reducing the integration time on the properties of the mouse tibia measured from microCT images. A scanning procedure (100 ms integration time, 256 mGy nominal radiation dose) was selected as the best compromise between image quality and radiation dose induced on the animal. In this work, the effect of repeated in vivo scans has been evaluated using the selected procedure. The right tibia of twelve female C57BL/6 (six wild type, WT, six ovariectomised, OVX) and twelve BALB/c (six WT, six OVX) mice was scanned every two weeks, starting at week 14 of age. At week 24, mice were sacrificed and both tibiae were scanned. Standard trabecular and cortical morphometric parameters were calculated. The spatial distribution of densitometric parameters (e.g. bone mineral content) was obtained by dividing each tibia in 40 partitions. Stiffness and strength in compression were estimated using homogeneous linear elastic microCT-based micro-Finite Element models. Differences between right (irradiated) and left (non-irradiated control) tibiae were evaluated for each parameter. The irradiated tibiae had higher Tb.Th (+3.3%) and Tb.Sp (+11.6%), and lower Tb.N (-14.2%) compared to non-irradiated tibiae, consistently across both strains and intervention groups. A reduction in Tb.BV/TV (-14.9%) was also observed in the C57BL/6 strain. In the OVX group, a small reduction was also observed in Tt.Ar (-5.0%). In conclusion, repeated microCT scans (at 256 mGy, 5 scans, every two weeks) had limited effects on the mouse tibia, compared to the expected changes induced by bone treatments. Therefore, the selected scanning protocol is acceptable for measuring the effect of bone interventions in vivo

ChronoMID—Cross-modal neural networks for 3-D temporal medical imaging data

ChronoMID—neural networks for temporally-varying, hence Chrono, Medical Imaging Data—makes the novel application of cross-modal convolutional neural networks (X-CNNs) to the medical domain. In this paper, we present multiple approaches for incorporating temporal information into X-CNNs and compare their performance in a case study on the classification of abnormal bone remodelling in mice. Previous work developing medical models has predominantly focused on either spatial or temporal aspects, but rarely both. Our models seek to unify these complementary sources of information and derive insights in a bottom-up, data-driven approach. As with many medical datasets, the case study herein exhibits deep rather than wide data; we apply various techniques, including extensive regularisation, to account for this. After training on a balanced set of approximately 70000 images, two of the models—those using difference maps from known reference points—outperformed a state-of-the-art convolutional neural network baseline by over 30pp (> 99% vs. 68.26%) on an unseen, balanced validation set comprising around 20000 images. These models are expected to perform well with sparse data sets based on both previous findings with X-CNNs and the representations of time used, which permit arbitrarily large and irregular gaps between data points. Our results highlight the importance of identifying a suitable description of time for a problem domain, as unsuitable descriptors may not only fail to improve a model, they may in fact confound it