The synergy of synchrotron imaging and convolutional neural networks towards the detection of human micro-scale bone architecture and damage

Muscle structure assessment using synchrotron radiation X-ray micro-computed tomography in murine with cerebral ischemia

Muscles are crucial for balance and walking, activities which depend specifically on the lower extremity muscles. Therefore, the evaluation of stroke induced atrophy and paralysis is essential; however, determining the extent of damage in the days after its occurrence remains challenging. In this study, we evaluated ischemic stroke-induced soleus muscle damage in gerbils using synchrotron radiation X-ray micro-computed tomography (SR-µCT), comparing a control group (n = 3), animals 7 days after stroke (7 d, n = 3), and animals 14 days after stroke (14 d, n = 3). The left muscle was paralyzed, whereas the right muscle was not. Subsequently, we quantified the assessment by segmenting the soleus muscle based on the extracellular space/matrix and fiber region to determine the degree of damage. The muscle fiber-to-extracellular space/matrix ratio were significantly damaged due to paralysis on the left side (control vs. 14 d, P = 0.040). Muscle area was significantly different at 14 d between the left and right sides (P = 0.010). Additionally, the left local fascicle surface area, thickness, global pennation angle, and local fascicle angle were significantly different between the control and 14 d groups (P = 0.002, P = 0.007, P = 0.005, and P = 0.014 respectively). These findings underscore the potential of post-stroke animal studies in improving rehabilitation treatment for the central nervous system by assessing the degree of muscle recovery.

Effect of trabecular architectures on the mechanical response in osteoporotic and healthy human bone

Research at the mesoscale bone trabeculae arrangement yields intriguing results that, due to their clinical resolution, can be applied in clinical field, contributing significantly to the diagnosis of bone-related diseases. While the literature offers quantitative morphometric parameters for a thorough characterization of the mesoscale bone network, there is a gap in understanding relationships among them, particularly in the context of various bone pathologies. This research aims to bridge these gaps by offering a quantitative evaluation of the interplay among morphometric parameters and mechanical response at mesoscale in osteoporotic and non-osteoporotic bones. Bone mechanical response, dependent on trabecular arrangement, is defined by apparent stiffness, computationally calculated using the Gibson-Ashby model. Key findings indicate that: (i) in addition to bone density, measured using X-ray absorptiometry, trabecular connectivity density, trabecular spacing and degree of anisotropy are crucial parameters for characterize osteoporosis state; (ii) apparent stiffness values exhibit strong correlations with bone density and connectivity density; (iii) connectivity density and degree of anisotropy result the best predictors of mechanical response. Despite the inherent heterogeneity in bone structure, suggesting the potential benefit of a larger sample size in the future, this approach presents a valuable method to enhance discrimination between osteoporotic and non-osteoporotic samples.

Improving the performance of 3D image model compression based on optimized DEFLATE algorithm

This study focuses on optimizing and designing the Delayed-Fix-Later Awaiting Transmission Encoding (DEFLATE) algorithm to enhance its compression performance and reduce the compression time for models, specifically in the context of compressing NX three-dimensional (3D) image models. The DEFLATE algorithm, a dual-compression technique combining the LZ77 algorithm and Huffman coding, is widely employed for compressing multimedia data and 3D models. Three 3D models of varying sizes are selected as subjects for experimentation. The Wavelet algorithm, C-Bone algorithm, and DEFLATE algorithm are utilized for compression, with subsequent analysis of the compression ratio and compression time. The experimental findings demonstrate the DEFLATE algorithm's exceptional performance in compressing 3D image models. Notably, when compressing small and medium-sized 3D models, the DEFLATE algorithm exhibits significantly higher compression ratios compared to the Wavelet and C-Bone algorithms while also achieving shorter compression times. Compared to the Wavelet algorithm, the DEFLATE algorithm enhances the compression performance of 3D image models by 15% and boosts data throughput by 49%. While the compression ratio of the DEFLATE algorithm for large 3D models is comparable to that of the Wavelet and C-Bone algorithms, it notably reduces the actual compression time. Furthermore, the DEFLATE algorithm enhances data transmission reliability in NX 3D image model compression by 12.1% compared to the Wavelet algorithm. Therefore, the following conclusions are drawn: the DEFLATE algorithm serves as an excellent compression algorithm for 3D image models. It showcases significant advantages in compressing small and medium-sized models while remaining highly practical for compressing large 3D models. This study offers valuable insights for enhancing and optimizing the DEFLATE algorithm, and it serves as a valuable reference for future research on 3D image model compression.

Application of Microfracture Analysis to Fatigue Fractures in Materials through Non-Destructive Tests

Fatigue fractures in materials are the main cause of approximately 80% of all material failures, and it is believed that such failures can be predicted and mathematically calculated in a reliable manner. It is possible to establish prediction modalities in cases of fatigue fractures according to three fundamental variables in fatigue, such as volume, number of fracture cycles, as well as applied stress, with the integration of Weibull constants (length characteristic). In this investigation, mechanical fatigue tests were carried out on specimens smaller than 4 mm2, made of different industrial materials. Their subsequent analysis was performed through precision computed tomography, in search for microfractures. The measurement of these microfractures, along with their metrics and classifications, was recorded. A convolutional neural network trained with deep learning was used to achieve the detection of microfractures in image processing. The detection of microfractures in images with resolutions of 480 × 854 or 960 × 960 pixels is the primary objective of this network, and its accuracy is above 95%. Images that have microfractures and those without are classified using the network. Subsequently, by means of image processing, the microfracture is isolated. Finally, the images containing this feature are interpreted using image processing to obtain their area, perimeter, characteristic length, circularity, orientation, and microfracture-type metrics. All values are obtained in pixels and converted to metric units (μm) through a conversion factor based on image resolution. The growth of microfractures will be used to define trends in the development of fatigue fractures through the studies presented.

Zoledronate reduces loading-induced microdamage in cortical ulna of ovariectomized rats

As a daily physiological mechanism in bone, microdamage accumulation dissipates energy and helps to prevent fractures. However, excessive damage accumulation might bring adverse effects to bone mechanical properties, which is especially problematic among the osteoporotic and osteopenic patients treated by bisphosphonates. Some pre-clinical studies in the literature applied forelimb loading models to produce well-controlled microdamage in cortical bone. Ovariectomized animals were also extensively studied to assimilate human conditions of estrogen-related bone loss. In the present study, we combined both experimental models to investigate microdamage accumulation in the context of osteopenia and zoledronate treatment. Three-month-old normal and ovariectomized rats treated by saline or zoledronate underwent controlled compressive loading on their right forelimb to create in vivo microdamage, which was then quantified by barium sulfate contrast-enhanced micro-CT imaging. Weekly in vivo micro-CT scans were taken to evaluate bone (re)modeling and to capture microstructural changes over time. After sacrifice, three-point-bending tests were performed to assess bone mechanical properties. Results show that the zoledronate treatment can reduce cortical microdamage accumulation in ovariectomized rats, which might be explained by the enhancement of several bone structural properties such as ultimate force, yield force, cortical bone area and volume. The rats showed increased bone formation volume and surface after the generation of microdamage, especially for the normal and the ovariectomized groups. Woven bone formation was also observed in loaded ulnae, which was most significant in ovariectomized rats. Although all the rats showed strong correlations between periosteal bone formation and microdamage accumulation, the correlation levels were lower for the zoledronate-treated groups, potentially because of their lower levels of microdamage. The present study provides insights to further investigations of pharmaceutical treatments for osteoporosis and osteopenia. The same experimental concept can be applied in future studies on microdamage and drug testing.

Towards an Informed CNN for Bone SR-microCT Image Classification with an Unsupervised Patched-based Image Clustering

Bone microscale differences cannot be readily recognizable to humans from Synchrotron Radiation micro-Computed Tomography (SR-microCT) images. Premises are possible with Deep Learning (DL) imaging analysis. Despite this, more attention to high-level features leads models to require help identifying relevant details to support a decision. Within this context, we propose a method for classifying healthy, osteoporotic, and COVID-19 femoral heads SR-microCT images informing a vgg16 about the most subtle microscale differences using unsupervised patched-based clustering. Our strategy allows achieving up to 9.8% accuracy improvement in classifying healthy from osteoporotic images over uninformed methods, while 59.1% of accuracy between osteoporosis and COVID-19.Clinical relevance-We established a starting point for classifying healthy, osteoporotic, and COVID-19 femoral heads from SR-microCTs with human non-discriminative features, with 60.91% accuracy in healthy-osteporotic image classification.

Osteoporosis and Covid-19: Detected similarities in bone lacunar-level alterations via combined AI and advanced synchrotron testing

While advanced imaging strategies have improved the diagnosis of bone-related pathologies, early signs of bone alterations remain difficult to detect. The Covid-19 pandemic has brought attention to the need for a better understanding of bone micro-scale toughening and weakening phenomena. This study used an artificial intelligence-based tool to automatically investigate and validate four clinical hypotheses by examining osteocyte lacunae on a large scale with synchrotron image-guided failure assessment. The findings indicate that trabecular bone features exhibit intrinsic variability related to external loading, micro-scale bone characteristics affect fracture initiation and propagation, osteoporosis signs can be detected at the micro-scale through changes in osteocyte lacunar features, and Covid-19 worsens micro-scale porosities in a statistically significant manner similar to the osteoporotic condition. Incorporating these findings with existing clinical and diagnostic tools could prevent micro-scale damages from progressing into critical fractures.

Isolating the Role of Bone Lacunar Morphology on Static and Fatigue Fracture Progression through Numerical Simulations

Currently, the onset of bone damage and the interaction of cracks with the surrounding micro-architecture are still black boxes. With the motivation to address this issue, our research targets isolating lacunar morphological and densitometric effects on crack advancement under both static and cyclic loading conditions by implementing static extended finite element models (XFEM) and fatigue analyses. The effect of lacunar pathological alterations on damage initiation and progression is evaluated; the results indicate that high lacunar density considerably reduces the mechanical strength of the specimens, resulting as the most influencing parameter among the studied ones. Lacunar size has a lower effect on mechanical strength, reducing it by 2%. Additionally, specific lacunar alignments play a key role in deviating the crack path, eventually slowing its progression. This could shed some light on evaluating the effects of lacunar alterations on fracture evolution in the presence of pathologies.