Deep learning models for ischemic stroke lesion segmentation in medical images: A survey.

This paper provides a comprehensive review of deep learning models for ischemic stroke lesion segmentation in medical images. Ischemic stroke is a severe neurological disease and a leading cause of death and disability worldwide. Accurate segmentation of stroke lesions in medical images such as MRI and CT scans is crucial for diagnosis, treatment planning and prognosis. This paper first introduces common imaging modalities used for stroke diagnosis, discussing their capabilities in imaging lesions at different disease stages from the acute to chronic stage. It then reviews three major public benchmark datasets for evaluating stroke segmentation algorithms: ATLAS, ISLES and AISD, highlighting their key characteristics. The paper proceeds to provide an overview of foundational deep learning architectures for medical image segmentation, including CNN-based and transformer-based models. It summarizes recent innovations in adapting these architectures to the task of stroke lesion segmentation across the three datasets, analyzing their motivations, modifications and results. A survey of loss functions and data augmentations employed for this task is also included. The paper discusses various aspects related to stroke segmentation tasks, including prior knowledge, small lesions, and multimodal fusion, and then concludes by outlining promising future research directions. Overall, this comprehensive review covers critical technical developments in the field to support continued progress in automated stroke lesion segmentation.

Elastic parameter identification of three-dimensional soft tissue based on deep neural network.

In the field of virtual surgery and deformation simulation, the identification of elastic parameters of human soft tissues is a critical technology that directly affects the accuracy of deformation simulation. Current research on soft tissue deformation simulation predominantly assumes that the elasticity of tissues is fixed and already known, leading to the difficulty in populating with the elasticity measured or identified from specific tissues of real patients. Existing elasticity modeling efforts struggle to be implemented on irregularly structured soft tissues, failing to adapt to clinical surgical practices. Therefore, this paper proposes a new method for identifying human soft tissue elastic parameters based on the finite element method and the deep neural network, UNet. This method requires only the full-field displacement data of soft tissues under external loads to predict their elastic distribution. The performance and validity of the algorithm are assessed using test data and clinical data from rhinoplasty surgeries. Experiments demonstrate that the method proposed in this paper can achieve an accuracy of over 99% in predicting elastic parameters. Clinical data validation shows that the predicted elastic distribution can reduce the error in finite element deformation simulations by more than 80% at the maximum compared to the error with traditional uniform elastic parameters, effectively enhancing the computational accuracy in virtual surgery simulations and soft tissue deformation modeling.

Classification of recurrent major depressive disorder using a residual denoising autoencoder framework: Insights from large-scale multisite fMRI data.

BACKGROUND AND OBJECTIVE: Recurrent major depressive disorder (rMDD) has a high recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using functional magnetic resonance imaging (fMRI) can enhance understanding of brain activity and aid diagnosis and treatment of this disorder. METHODS: We developed a Residual Denoising Autoencoder (Res-DAE) framework for the classification of rMDD. The functional connectivity (FC) was extracted from fMRI data as features. The framework addresses site heterogeneity by employing the Combat method to harmonize feature distribution differences. A feature selection method based on Fisher scores was used to reduce redundant information in the features. A data augmentation strategy using a Synthetic Minority Over-sampling Technique algorithm based on Extended Frobenius Norm measure was incorporated to increase the sample size. Furthermore, a residual module was integrated into the autoencoder network to preserve important features and improve the classification accuracy. RESULTS: We tested our framework on a large-scale, multisite fMRI dataset, which includes 189 rMDD patients and 427 healthy controls. The Res-DAE achieved an average accuracy of 75.1 % (sensitivity = 69 %, specificity = 77.8 %) in cross-validation, thereby outperforming comparison methods. In a larger dataset that also includes first-episode depression (comprising 832 MDD patients and 779 healthy controls), the accuracy reached 70 %. CONCLUSIONS: We proposed a deep learning framework that can effectively classify rMDD and 33 identify the altered FC associated with rMDD. Our study may reveal changes in brain function 34 associated with rMDD and provide assistance for the diagnosis and treatment of rMDD.

Classification of MDD using a Transformer classifier with large-scale multisite resting-state fMRI data.

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide with high recurrence rate. Identifying MDD patients, particularly those with recurrent episodes with resting-state fMRI, may reveal the relationship between MDD and brain function. We proposed a Transformer-Encoder model, which utilized functional connectivity extracted from large-scale multisite rs-fMRI datasets to classify MDD and HC. The model discarded the Transformer's Decoder part, reducing the model's complexity and decreasing the number of parameters to adapt to the limited sample size and it does not require a complex feature selection process and achieves end-to-end classification. Additionally, our model is suitable for classifying data combined from multiple brain atlases and has an optional unsupervised pre-training module to acquire optimal initial parameters and speed up the training process. The model's performance was tested on a large-scale multisite dataset and identified brain regions affected by MDD using the Grad-CAM method. After conducting five-fold cross-validation, our model achieved an average classification accuracy of 68.61% on a dataset consisting of 1611 samples. For the selected recurrent MDD dataset, the model reached an average classification accuracy of 78.11%. Abnormalities were detected in the frontal gyri and cerebral cortex of MDD patients in both datasets. Furthermore, the identified brain regions in the recurrent MDD dataset generally exhibited a higher contribution to the model's performance.

Classification of recurrent major depressive disorder using a new time series feature extraction method through multisite rs-fMRI data.

BACKGROUND: Major depressive disorder (MDD) has a high rate of recurrence. Identifying patients with recurrent MDD is advantageous in adopting prevention strategies to reduce the disabling effects of depression. METHOD: We propose a novel feature extraction method that includes dynamic temporal information, and inputs the extracted features into a graph convolutional network (GCN) to achieve classification of recurrent MDD. We extract the average time series using an atlas from resting-state functional magnetic resonance imaging (fMRI) data. Pearson correlation was calculated between brain region sequences at each time point, representing the functional connectivity at each time point. The connectivity is used as the adjacency matrix and the brain region sequences as node features for a GCN model to classify recurrent MDD. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to analyze the contribution of different brain regions to the model. Brain regions making greater contribution to classification were considered to be the regions with altered brain function in recurrent MDD. RESULT: We achieved a classification accuracy of 75.8 % for recurrent MDD on the multi-site dataset, the Rest-meta-MDD. The brain regions closely related to recurrent MDD have been identified. LIMITATION: The pre-processing stage may affect the final classification performance and harmonizing site differences may improve the classification performance. CONCLUSION: The experimental results demonstrate that the proposed method can effectively classify recurrent MDD and extract dynamic changes of brain activity patterns in recurrent depression.

Angiogenesis and flap-related research: A bibliometric analysis.

Adequate blood supply, a prerequisite for flap survival after grafting, makes angiogenesis of the flap the biggest problem to be solved. Researches have been conducted around vascularisation in correlation with flap grafting. However, bibliometric analyses systematically examining this research field are lacking. As such, we herein sought to conduct comprehensive comparative analyses of the contributions of different researchers, institutions, and countries to this research space in an effort to identify trends and hotspots in angiogenesis and vascularisation in the context of flap grafting. Publications pertaining to angiogenesis and vascularisation in the context of flap grafting were retrieved from the Web of Science Core Collection. References were then analysed and plotted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. In total, 2234 papers that were cited 40 048 times (17.63 citations/paper) were included in this analysis. The greatest number of studies were from the United States, with these studies exhibiting both the highest number of citations (13 577) and the greatest overall H-index (60). For The institutions that published the greatest number of studies were WENZHOU MEDICAL UNIVERSITY (681), while UNIVERSITY OF ERLANGEN NUREMBERG has the highest number of citations (1458), and SHANGHAI JIAO TONG UNIVERSITY holds the greatest overall H-index (20). The greatest number of studies in this research space were published by Gao WY, while Horch RE was the most commonly cited researcher in the field. The VOS viewer software clustered relevant keywords into three clusters, with clusters 1, 2, 3, and 4 corresponding to studies in which the keywords 'anatomy', 'survival', 'transplantation', 'therapy' most frequently appeared. The most promising research hotspot-related terms in this field included 'autophagy', 'oxidative stress', 'ischemia/reperfusion injury', which exhibited a most recent average appearing year (AAY) of 2017 and after. Generally speaking, the results of this analysis indicate that the number of articles exploring angiogenesis and flap-related research has risen steadily, with the United States and China being the two countries publishing the greatest proportion of studies in this field. The overall focus of these studies has shifted away from 'infratest and tissue engineering' towards 'mechanisms'. In the future, particular attention should be paid to emerging research hotspots, which include 'ischemia/reperfusion injury' and treatments for promoting vascularization, such as 'platelet-rich plasma'. In light of these findings, funding agencies should continue increasing their investment in the exploration of the concrete mechanisms and interventional therapeutic relevance of angiogenesis during flap transplantation.

Integrating model explanations and hybrid priors into deep stacked networks for the "safe zone" prediction of acetabular cup.

BACKGROUND: Existing state-of-the-art "safe zone" prediction methods are statistics-based methods, image-matching techniques, and machine learning methods. Yet, those methods bring a tension between accuracy and interpretability. PURPOSE: To explore the model explanations and estimator consensus for "safe zone" prediction. MATERIAL AND METHODS: We collected the pelvic datasets from Orthopaedic Hospital, and a novel acetabular cup detection method is proposed for automatic ROI segmentation. Hybrid priors comprising both specific priors from data and general priors from experts are constructed. Specifically, specific priors are constructed based on the fine-tuned ResNet-101 convolutional neural networks (CNN) model, and general priors are constructed based on expert knowledge. Our method considers the model explanations and dynamic consensus through appending a SHapley Additive exPlanations (SHAP) module and a dynamic estimator stacking. RESULTS: The proposed method achieves an accuracy of 99.40% and an area under the curve of 0.9998. Experimental results show that our model achieves superior results to the state-of-the-art conventional ensemble classifiers and deep CNN models. CONCLUSION: This new screening model provides a new option for the "safe zone" prediction of acetabular cup.

Explainable Prediction of Dacryocystitis From Noninvasive Ocular Indicators Using Deep Stacked Network and The Shapley Additive Explanations Approach.

ABSTRACT: Dacryocystitis diagnosis is important for preventing rapid blurring and vision loss. Existing state-of-the-art methods focus on routine clinical examinations and objective scattering index-based statistical analysis. Such approaches are invasive operations or lack quantitative indicators, and their application is limited. in addition, little attention has been paid to the explainability and clinical utility of models. This paper proposes an explainable dacryocystitis prediction model from noninvasive ocular indicators. The proposed model is based on an deep stacked network with 4 improvements: a multivariable feature extraction module, obtaining comprehensive predictive factors including the quantitative ocular indictors, conventional texture features, and deep learning features from shallow to deep convolutional layers; a multifeature fusion and attribute selection module based on the ReliefF method, guiding the network to focus on useful information at variables; Decision curve analysis the model is introduced into the model to evaluates the risks and benefits; and appending a SHapley Additive exPlanations (SHAP) module to the framework to automatically and efficiently interpret the prediction of the models. By integrating the above improvements in series, the models' performances are gradually enhanced. Real labeled data samples are used to train and test the model, and our model achieves high accuracy and reliability.

Automatic Real-Time Space Registration Application for Simulating Dental and Maxillofacial Surgery.

ABSTRACT: Real-time surgical navigation systems are important for preoperative planning and intraoperative navigation. Automatic preoperative multimodal data registration and postoperative spatial registration are extremely crucial in such surgical navigation systems. However, existing automatic multimodal data registration methods have extremely limited application scope due to the lack of accuracy and speed. In addition, the registration results obtained by existing methods are practically lacking and are rarely applied in clinics. To address the above issues, this paper proposes a novel real-time teeth registration algorithm with computed tomography (CT) data and optical tracking scanning data. The proposed method is based on the weighted iterative closest point (ICP) algorithm with 3 improvements: (1) the multilayer spherical point set is generated inside the laser scanning marker sphere, (2) the weight decreases from inside to outside layer by layer, and (3) the weight of the voxel center point set is combined with the CT data of the marker sphere. Specifically, the proposed iCP registration method can overcome the limitation of surface point set registration and tackle the problem of high surface deformity of laser scanning marker spheres. For the registration result of CT and scanning data, the authors employ the real-time spatial registration algorithm based on optical tracking to complete the navigation of the simulated surgical instruments on the multimodal fusion image. The experimental results show that the proposed ICP algorithm reduces the mean square error by 1 order of magnitude and that our method has strong practical value.

Noninvasive Machine Learning Screening Model for Dacryocystitis Based on Ocular Surface Indicators.

BACKGROUND: Dacryocystitis is an orbital disease that can be easily misdiagnosed. The most common diagnostic tools for dacryocystitis are computed tomography, lacrimal duct angiography, and lacrimal tract irrigation. Yet, those are invasive methods, which are not conducive to extensive screening. OBJECTIVE: To explore the significance of ocular surface indicators and demographic data in the screening of dacryocystitis. MATERIALS AND METHODS: Data were prospectively collected from 56 patients with dacryocystitis (56 eyes) and 56 healthy individuals. Collected indicators included demographic information (gender, age), ocular surface data of tear meniscus height, objective scatter index (OSI), and clinical diagnosis. The model features were screened out by machine learning to establish a dacryocystitis screening model. RESULTS: Tear meniscus height, OSI_maximum Lyapunov exponent, basic OSI, median of OSI, mean of OSI, slope coefficient of OSI linear regression, coefficient of variation in OSI, interquartile range of OSI, and other 8 parameters were used as model parameters to establish a dacryocystitis screening model with an overall detection accuracy of 85.71%. CONCLUSIONS: This new screening model that is based on ocular surface indicators provides a new option for noninvasive screening of dacryocystitis.

3D object reassembly using region-pair-relation and balanced cluster tree.

BACKGROUND AND OBJECTIVE: Object reassembly is a key technology in scenarios such as surgical planning and broken object restoration. Based on previous research, this work intends to explore the general tasks of 3D object reassembly, including conventional object reconstruction and bone fracture reduction. METHODS: We introduce an efficient and robust region-pair-relation descriptor, which incorporates strong geometric constraints and remains invariant to rotation and translation. We segment the fractured objects using balanced cluster tree, and develop a coarse-to-fine method for object reassembly. The matching quality of potential region contact pairs at different depths is estimated recursively from the root of the tree. Once the best contact pairs are determined, the least squares method is implemented to obtain the matching results. In addition, we also provide a semi-interactive manipulation to deal with the complex objects. RESULTS: For most types of broken objects, our approach can generate high accuracy matching results within 10 s, with the cluster tree depth equals to 11. It allows the automatic reassembly of different-sized fragments. For bone fracture blocks with cancellous structures, a semi-interactive operation is integrated so that the precise matching can also be achieved in 30 s. CONCLUSION: The proposed framework can be expanded to various object reassembly tasks in either automated or semi-automated manner, including the fracture reduction problem which used to be an intensive manual process. Therefore, our work shows significant advantages in medical applications.

Three-Dimensional Mapping of Medial Wall in Unstable Pertrochanteric Fractures.

OBJECTIVE: Unstable pertrochanteric fractures are usually treated with internal fixation, and the integrity of the anteromedial cortex is an important factor for stability and healing. In this study, we described and analyzed the three-dimensional mapping technology and morphological characteristics of pertrochanteric fractures. METHODS: Fifty-nine pertrochanteric fractures (OTA/AO 2007 types 31A2) were retrospectively reviewed. Computed tomographic (CT) images for all fractures were superimposed on a standard template. Medial wall integrity was analyzed, and three-dimensional fracture maps were created. RESULTS: Pertrochanteric fractures always have a posterior defect in the medial cortex. The mean width of the defect, in our study, was 21.5 mm (SD: 6.1 mm, range: 10-40 mm), 56.3% (SD: 13.7%, range: 27.5-100%). Bone segments that contact by the anteromedial cortex were 16.5 mm (SD: 5.3 mm, range: 0-29 mm). CONCLUSION: The integrity of the anteromedial cortex should be considered during internal fixation of femoral trochanteric fractures. These morphological characteristics could be used to form postoperative cortical contact and improve stability of the fixation. Three-dimensional mapping technology can help establish a typical fracture model, thereby improving doctors' understanding of fracture characteristics.

Synchrotron Radiation-Based Three-Dimensional Visualization of Angioarchitectural Remodeling in Hippocampus of Epileptic Rats.

Characterizing the three-dimensional (3D) morphological alterations of microvessels under both normal and seizure conditions is crucial for a better understanding of epilepsy. However, conventional imaging techniques cannot detect microvessels on micron/sub-micron scales without angiography. In this study, synchrotron radiation (SR)-based X-ray in-line phase-contrast imaging (ILPCI) and quantitative 3D characterization were used to acquire high-resolution, high-contrast images of rat brain tissue under both normal and seizure conditions. The number of blood microvessels was markedly increased on days 1 and 14, but decreased on day 60 after seizures. The surface area, diameter distribution, mean tortuosity, and number of bifurcations and network segments also showed similar trends. These pathological changes were confirmed by histological tests. Thus, SR-based ILPCI provides systematic and detailed views of cerebrovascular anatomy at the micron level without using contrast-enhancing agents. This holds considerable promise for better diagnosis and understanding of the pathogenesis and development of epilepsy.

Synchrotron radiation micro-tomography for high-resolution neurovascular network morphology investigation.

There has been increasing interest in using high-resolution micro-tomography to investigate the morphology of neurovascular networks in the central nervous system, which remain difficult to characterize due to their microscopic size as well as their delicate and complex 3D structure. Synchrotron radiation X-ray imaging, which has emerged as a cutting-edge imaging technology with a high spatial resolution, provides a novel platform for the non-destructive imaging of microvasculature networks at a sub-micrometre scale. When coupled with computed tomography, this technique allows the characterization of the 3D morphology of vasculature. The current review focuses on recent progress in developing synchrotron radiation methodology and its application in probing neurovascular networks, especially the pathological changes associated with vascular abnormalities in various model systems. Furthermore, this tool represents a powerful imaging modality that improves our understanding of the complex biological interactions between vascular function and neuronal activity in both physiological and pathological states.

3D Computed Tomography Mapping of Thoracolumbar Vertebrae Fractures.

BACKGROUND Fractures of the thoracolumbar (TL) spine represent 90% of all spinal fractures, followed by cervical and lumbar spine fractures. This study aimed to create fracture maps of the traumatic thoracolumbar (TL) fracture vertebral body (T12-L2) through the use of CT mapping as a big data visualization method to reveal recurrent patterns and characteristics of traumatic TL fractures. MATERIAL AND METHODS A consecutive series of 174 fractured vertebrae (T12-L2) was used to create three-dimensional (3D) reconstruction images, which were superimposed and oriented to fit a model vertebral template by aligning specific bio-landmarks and reducing reconstructed fracture fragments. Fracture lines were found and traced to create a fracture map of the vertebral body. RESULTS Our study consisted of 165 patients with an average age of 47 years. A total of 174 fractured vertebrae were collected, consisting of 59 T12 vertebral fractures, 60 L1 vertebral fractures, and 55 L2 vertebral fractures. Two-dimensional (2D) maps, 3D maps, and heat maps showed that the fracture lines tended to be concentrated in the upper third and anterior third of the vertebral body, as well as being distributed in annular wedges along the anterior and lateral sides of the vertebral body. When compared with T12, the distribution of fracture lines in L1 and especially in L2 was more scattered and disorganized. CONCLUSIONS Fracture maps revealed recurrent patterns and characteristics of the traumatic TL fracture vertebral body, which improves understanding of TL fractures, as well as helping to increase opportunities for follow-up research and aid clinical decision-making.

Unilateral Osteotomy of Lumbar Facet Joint Induces a Mouse Model of Lumbar Facet Joint Osteoarthritis.

STUDY DESIGN: The lumbar facet joint (LFJ) osteoarthritis (OA) model that highly mimics the clinical conditions was established and evaluated. OBJECTIVE: Here, we innovatively constructed and evaluated the aberrant mechanical loading-related LFJ OA model. SUMMARY OF BACKGROUND DATA: LFJ is the only true synovial joint in a functional spinal unit in mammals. The LFJ osteoarthritis is considered to contribute 15% to 45% of low back pain. The establish of animal models highly mimicking the clinical conditions is a useful tool for the investigation of LFJ OA. However, the previously established animal models damaged the LFJ structure directly, which did not demonstrate the effect of aberrant mechanical loading on the development of LFJ osteoarthritis. METHODS: In the present study, an animal model for LFJ degeneration was established by the unilateral osteotomy of LFJ (OLFJ) in L4/5 unit to induce the spine instability. Then, the change of contralateral LFJ was evaluated by morphological and molecular biological techniques. RESULTS: We showed that the OLFJ induced instability accelerated the cartilage degeneration of the contralateral LFJ. Importantly, the SRµCT elucidated that the three-dimensional structure of the subchondral bone changed in contralateral LFJ, indicated as the abnormity of bone volume/total volume ratio (BV/TV), trabecular pattern factor (Tb. Pf), and the trabecular thickness (Tb. Th). Immunostaining further demonstrated the uncoupled osteoclastic bone resorption, and bone formation in the subchondral bone of contralateral LFJ, indicated as increased activity of osteoclast, osteoblast, and Type H vessels. CONCLUSION: We develop a novel LFJ OA model demonstrating the effect of abnormal mechanical instability on the degeneration of LFJ. This LFJ degeneration model that highly mimics the clinical conditions is a valuable tool to investigate the LFJ osteoarthritis. LEVEL OF EVIDENCE: N/A.

Spinal Peripheral Primitive Neuroectodermal Tumors: A Radiological Analysis of Ten Cases.

AIM: To summarize the imaging features of spinal peripheral primitive neuroectodermal tumor (spPNET) patients. MATERIAL AND METHODS: The computed tomography and magnetic resonance imaging features of 10 spPNET patients, four men and six women, were retrospectively analyzed, and their clinicopathological data were reviewed. RESULTS: The mean age of the patients was 24.7 years (range, 3-44 years). Ten spPNET lesions were found in the ten patients, including six extradural and four intradural extramedullary lesions. Radiologically, spPNET typically presented as heterogeneous isointense lesions with a heterogeneously enhanced pattern. A "vault wall-like growth" pattern, a linear enhancement pattern, and vertebral bone involvement tended to be found in the extradural lesions, whereas a ring enhancement pattern was found in the extramedullary intradural lesions. Positive Ki-67 expression might be related to necrosis, bone destruction, and hemorrhage. CONCLUSION: A well-defined spinal mass showing isointensity/attenuation with heterogeneous enhancement accompanied by other imaging features may be suggestive of spPNET and should be added to the list of differential diagnosis.

Ciliary parathyroid hormone signaling activates transforming growth factor-ß to maintain intervertebral disc homeostasis during aging.

Degenerative disc disease (DDD) is associated with intervertebral disc degeneration of spinal instability. Here, we report that the cilia of nucleus pulposus (NP) cells mediate mechanotransduction to maintain anabolic activity in the discs. We found that mechanical stress promotes transport of parathyroid hormone 1 receptor (PTH1R) to the cilia and enhances parathyroid hormone (PTH) signaling in NP cells. PTH induces transcription of integrin αvß6 to activate the transforming growth factor (TGF)-ß-connective tissue growth factor (CCN2)-matrix proteins signaling cascade. Intermittent injection of PTH (iPTH) effectively attenuates disc degeneration of aged mice by direct signaling through NP cells, specifically improving intervertebral disc height and volume by increasing levels of TGF-ß activity, CCN2, and aggrecan. PTH1R is expressed in both mouse and human NP cells. Importantly, knockout PTH1R or cilia in the NP cells results in significant disc degeneration and blunts the effect of PTH on attenuation of aged discs. Thus, mechanical stress-induced transport of PTH1R to the cilia enhances PTH signaling, which helps maintain intervertebral disc homeostasis, particularly during aging, indicating therapeutic potential of iPTH for DDD.

Automatic liver vessel segmentation using 3D region growing and hybrid active contour model.

This paper proposes a new automatic method for liver vessel segmentation by exploiting intensity and shape constraints of 3D vessels. The core of the proposed method is to apply two different strategies: 3D region growing facilitated by bi-Gaussian filter for thin vessel segmentation, and hybrid active contour model combined with K-means clustering for thick vessel segmentation. They are then integrated to generate final segmentation results. The proposed method is validated on abdominal computed tomography angiography (CTA) images, and obtains an average accuracy, sensitivity, specificity, Dice, Jaccard, and RMSD of 98.2%, 68.3%, 99.2%, 73.0%, 66.1%, and 2.56 mm, respectively. Experimental results show that our method is capable of segmenting complex liver vessels with more continuous and complete thin vessel details, and outperforms several existing 3D vessel segmentation algorithms.

Three-dimensional visualization and pathologic characteristics of cartilage and subchondral bone changes in the lumbar facet joint of an ovariectomized mouse model.

BACKGROUND CONTEXT: Low back pain (LBP) is more prevalent among postmenopausal women than men. Ovariectomy (OVX) is an established animal model that mimics the estrogen deficiency of postmenopausal women. Little is known about the three-dimensional (3D) morphologic properties of cartilage and subchondral bone changes in the lumbar facet joint (LFJ) of an OVX mouse model. PURPOSE: The purpose of this study was to characterize the 3D morphologic change of cartilage and subchondral bone in the LFJ of an OVX mouse model. STUDY DESIGN: Three-dimensional visualization and a histologic study on degenerative changes in cartilage and subchondral bone in the LFJ of an OVX mouse model were conducted. MATERIALS AND METHODS: Ovariectomy is performed to mimic postmenopausal changes in adult female mice. We present an imaging tool for 3D visualization of the pathologic characteristics of cartilage and subchondral bone changes LFJ degradation using propagation-based phase-contrast computed tomography (PPCT). The samples were further dissected, fixed, and stained for histologic examination. RESULTS: Propagation-based phase-contrast computed tomography imaging provides a 3D visualization of altered cartilage with a simultaneous high detail of the subchondral bone abnormalities in an OVX LFJ model. A quantitative analysis demonstrated that the cartilage volume, the surface area, and thickness were decreased in the OVX group compared with the control group (p<.05). Meanwhile, these decreases were accompanied by an obvious destruction of the subchondral bone surface and a loss of trabecular bone in the OVX group (p<.05). The delineation of the 3D pathologic changes in the PPCT imaging was confirmed by a histopathologic method with Safranin-O staining. Tartrate-resistant acid phosphatase staining revealed an increased number of osteoclasts in the subchondral bone of the OVX mice compared with that of the control group. CONCLUSIONS: These results demonstrated that a mouse model of OVX-induced LFJ osteoarthritis (OA)-like changes was successfully established and showed a good resemblance to the human OA pathology. Propagation-based phase-contrast computed tomography has great potential to becomea powerful 3D imaging method to comprehensively characterize LFJ OA and to effectively monitor therapeutics. Moreover, degenerative LFJ possesses a severe morphologic change in the subchondral bone, may be the source of postmenopausal LBP, and has the potential to be a novel therapeutic target for LBP treatment.