Bone Imaging Research Articles

Magnetic Resonance Imaging Artifact Associated With Transcutaneous Bone Conduction Implants: Cholesteatoma and Vestibular Schwannoma Surveillance.

To evaluate the magnetic resonance (MR) image artifact and image distortion associated with the two transcutaneous bone conduction implants currently available in the United States. Cadaveric study. Two cadaveric head specimens (1 male, 1 female) were unilaterally implanted according to manufacturer guidelines and underwent MR imaging (General Electric and Siemens 1.5 T scanners) under the following device conditions: (1) no device, (2) Cochlear Osia with magnet and headwrap, (3) Cochlear Osia without magnet, and (4) MED-EL Bonebridge with magnet. Maximum metal mitigation techniques were employed in all conditions, and identical sequences were obtained. Blinded image scoring (diagnostic vs nondiagnostic image) was performed by experienced neuroradiologists according to anatomical subsites. All device conditions produced artifact and image distortion. The Osia with magnet produced diagnostic T1- and T2-weighted images of the ipsilateral temporal bone, however, non-echo planar imaging diffusion-weighted imaging (DWI) was nondiagnostic. The Osia without magnet scanned on the Siemens MR imaging demonstrated the least amount of artifact and was the only condition that allowed for diagnostic imaging of the ipsilateral temporal bone on DWI. The Bonebridge produced a large area of artifact and distortion with the involvement of the ipsilateral and contralateral temporal bones. In summary, of the three device conditions (Osia with magnet, Osia without magnet, and Bonebridge), Osia without magnet offered the least amount of artifact and distortion and was the only condition in which diagnostic DWI was available for the middle ear and mastoid regions on the Siemens MR imaging scanner.

A novel image augmentation based on statistical shape and intensity models: application to the segmentation of hip bones from CT images

BackgroundThe collection and annotation of medical images are hindered by data scarcity, privacy, and ethical reasons or limited resources, negatively affecting deep learning approaches. Data augmentation is often used to mitigate this problem, by generating synthetic images from training sets to improve the efficiency and generalization of deep learning models.MethodsWe propose the novel use of statistical shape and intensity models (SSIM) to generate augmented images with variety in both shape and intensity of imaged structures and surroundings. The SSIM uses segmentations from training images to create co-registered tetrahedral meshes of the structures and to efficiently encode image intensity in their interior with Bernstein polynomials. In the context of segmentation of hip joint (pathological) bones from retrospective computed tomography images of 232 patients, we compared the impact of SSIM-based and basic augmentations on the performance of a U-Net model.ResultsIn a fivefold cross-validation, the SSIM augmentation improved segmentation robustness and accuracy. In particular, the combination of basic and SSIM augmentation outperformed trained models not using any augmentation, or relying exclusively on a simple form of augmentation, achieving Dice similarity coefficient and Hausdorff distance of 0.95 [0.93–0.96] and 6.16 [4.90–8.08] mm (median [25th–75th percentiles]), comparable to previous work on pathological hip segmentation.ConclusionsWe proposed a novel augmentation varying both the shape and appearance of structures in generated images. Tested on bone segmentation, our approach is generalizable to other structures or tasks such as classification, as long as SSIM can be built from training data.Relevance statementOur data augmentation approach produces realistic shape and appearance variations of structures in generated images, which supports the clinical adoption of AI in radiology by alleviating the collection of clinical imaging data and by improving the performance of AI applications.Key points• Data augmentation generally improves the accuracy and generalization of deep learning models.• Traditional data augmentation does not consider the appearance of imaged structures.• Statistical shape and intensity models (SSIM) synthetically generate variations of imaged structures.• SSIM support novel augmentation approaches, demonstrated with computed tomography bone segmentation.Graphical abstract

Mechanical characterization of osteoporosis based on x-ray induced acoustic computed tomography

X-ray induced acoustic computed tomography (XACT) utilized the ultrasound generated by the thermoelastic effect to reconstruct the x-ray absorption distribution of tissues. In this Letter, we propose a method for mechanical characterization of osteoporosis based on an XACT technique. The theoretical and simulation studies were performed on the influence of elasticity effect on x-ray induced acoustic (XA) generation. The images of normal and osteoporotic bones reconstructed by the simulated XACT were found to be in good agreement with micro-CT. Furthermore, through XA signal analysis, the rise time of tissue displacement can be obtained to characterize the elasticity of bone tissues. Experimental results demonstrated that this method can provide structural and mechanical information of bone tissues, which has future potentials for assessment of bone in osteoporosis.

Investigation on primary stability of dental implants through considering peri-implant bone damage, caused by small and large deformations: A validated non-linear micro finite element study

Primary stability of a dental implant is defined as its ability to resist the applied load without showing excessive damage in peri-implant bone, which is a prerequisite for secondary stability, and consequently for implantation success. The main goal of this study was to develop a validated micro-finite element (μFE) approach to assess the primary stability of dental implants in terms of stiffness, stiffness reduction, and irreversible displacement of the bone-implant system, subjected to an increasing step-wise quasi-static compressive loading-unloading test. The μFE models were generated based on the μCT images of bone, taken from extracted bovine tibia trabecular bone samples after drilling and implantation. A tissue constitutive model was considered for trabecular bone by describing elasto-plasticity with a modified von Mises yield criterion and element deletion technique to account for trabecular bone damage behavior. Then, the obtained force-displacement curves from the simulation were compared with the in-vitro mechanical test curves to evaluate the validity of the model. The results showed that the proposed μFE model could be properly predict the bone-implant system mechanical response in terms of irreversible displacement (R2 = 0.99), stiffness (R2 = 0.77), and stiffness reduction (R2 = 0.72) of the bone-implant construct for all the applied displacements without a significant difference from the unit slope and zero intercept of the QQ-plot (p-value<0.05). Moreover, a qualitative agreement was seen between the peri-implant bone damage predicted by the μFE model and the observed from μCT images. The adopted methodology used in this study can predict the mechanical failure response of the bone-implant system, which can be employed as a representative tool to study the effects of various dental implant design parameters on the primary stability with the ultimate goal of optimizing dental implants design.

A Review on Imaging Techniques and Artificial Intelligence Models for Osteoporosis Prediction.

Osteoporosis causes harmful influences on both men and women of all races. Bone mass, also referred to as "bone density," is frequently used to assess the health of bone. Humans frequently experience bone fractures as a result of trauma, accidents, metabolic bone diseases, and disorders of bone strength, which are typically led by changes in mineral composition and result in conditions like osteoporosis, osteoarthritis, osteopenia, etc. Artificial intelligence holds a lot of promise for the healthcare system. Data collection and preprocessing seem to be more essential for analysis, so bone images from different modalities, such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI), are taken into consideration that help to recognize, classify, and evaluate the patterns in clinical images. This research presents a comprehensive overview of the performance of various image processing techniques and deep learning approaches used to predict osteoporosis through image segmentation, classification, and fault detection. This survey outlined the proposed domain-based deep learning model for image classification in addition to the initial findings. The outcome identifies the flaws in the existing literature's methodology and lays the way for future work in the deep learning-based image analysis model.

Addressing Challenges of Opportunistic Computed Tomography Bone Mineral Density Analysis.

Computed tomography (CT) offers advanced biomedical imaging of the body and is broadly utilized for clinical diagnosis. Traditionally, clinical CT scans have not been used for volumetric bone mineral density (vBMD) assessment; however, computational advances can now leverage clinically obtained CT data for the secondary analysis of bone, known as opportunistic CT analysis. Initial applications focused on using clinically acquired CT scans for secondary osteoporosis screening, but opportunistic CT analysis can also be applied to answer research questions related to vBMD changes in response to various disease states. There are several considerations for opportunistic CT analysis, including scan acquisition, contrast enhancement, the internal calibration technique, and bone segmentation, but there remains no consensus on applying these methods. These factors may influence vBMD measures and therefore the robustness of the opportunistic CT analysis. Further research and standardization efforts are needed to establish a consensus and optimize the application of opportunistic CT analysis for accurate and reliable assessment of vBMD in clinical and research settings. This review summarizes the current state of opportunistic CT analysis, highlighting its potential and addressing the associated challenges.

Added value of early-phase SPECT over planar acquisition in bone imaging.

The aim of this study is to evaluate the added value of early bone single photon emission computed tomography (SPECT) by comparison to pseudoplanar imaging. Fifty patients were retrospectively included from 3 centers. Reading sessions were organized using: late-phase acquisition alone; early SPECT and late-phase acquisition; early pseudoplanar and late-phase acquisition. The comparison between early SPECT and MIP was performed using a McNemar Test. Patients for whom early SPECT had provided additional information were also compared with patients for whom early SPECT had not. Fifty patients were included. Early SPECT was superior to pseudoplanar MIP in 10/50 patients (20.0%, P=0.044). No significant difference was found between this group and the remainder. Early SPECT changed the diagnosis established from late-phase imaging in 21/50 patients. Early SPECT is a promising tool in bone imaging and change the diagnosis in one fifth of the cases by comparison to pseudoplanar imaging.

MRI Advancements in Musculoskeletal Clinical and Research Practice.

Over the past decades, MRI has become increasingly important for diagnosing and longitudinally monitoring musculoskeletal disorders, with ongoing hardware and software improvements aiming to optimize image quality and speed. However, surging demand for musculoskeletal MRI and increased interest to provide more personalized care will necessitate a stronger emphasis on efficiency and specificity. Ongoing hardware developments include more powerful gradients, improvements in wide-bore magnet designs to maintain field homogeneity, and high-channel phased-array coils. There is also interest in low-field-strength magnets with inherently lower magnetic footprints and operational costs to accommodate global demand in middle- and low-income countries. Previous approaches to decrease acquisition times by means of conventional acceleration techniques (eg, parallel imaging or compressed sensing) are now largely overshadowed by deep learning reconstruction algorithms. It is expected that greater emphasis will be placed on improving synthetic MRI and MR fingerprinting approaches to shorten overall acquisition times while also addressing the demand of personalized care by simultaneously capturing microstructural information to provide greater detail of disease severity. Authors also anticipate increased research emphasis on metal artifact reduction techniques, bone imaging, and MR neurography to meet clinical needs.

Statistical characterization of medical images of bone

Statistical characterization of medical images of bone

High-Resolution Computed Tomography of the Temporal Bone in Chronic Otitis Media: An Observational Study at a Tertiary Care Center in Jharkhand, India.

Chronic otitis media refers to middle ear inflammation. A radiological exam is a crucial step in this diagnostic process, in addition to a clinical evaluation and an evaluation by an audiologist. For the development of innovative surgical treatment plans for middle ear otitis media with the aim of minimally invasive surgery, accurate information regarding the extent of lesions is required. This is made possible by a temporal bone imaging test. The study was conducted at the Rajendra Institute of Medical Sciences (RIMS), a tertiary care facility in Ranchi, Jharkhand, India. The research was conducted between June 1, 2021, and October 31, 2022. In this prospective observational study, 50 patients who visited the otorhinolaryngology clinic at RIMS Ranchi were the participants. The median age was 26 years, there was a male-to-female ratio of 1.63 to 1, and the vast majority (84%) were from lower socioeconomic classes. High-resolution computed tomography has been shown to be highlysensitive and specific in identifying conditions including scutum erosion, malleus erosion, pneumatization type, mastoiditis, mastoid abscess, and morphological abnormalities such as low-lying dura. The semicircular canal, fallopian canal, dural plate, and sigmoid sinus may all be clearly defined by non-contrast computed tomography of the temporal bone in erosion in cases of otitis media.

Brittle Bone Imaging: Diagnostic Procedures in Adults.

This article concentrates on generalized disorders causing bone fragility. The most frequent cause of brittle bone disease is osteoporosis, only diagnosed by elimination. First, malignant osteopathy must be ruled out: fracture on metastasis or multiple myeloma, which mimics osteoporosis when presenting as diffuse demineralization. Second, patients should be screened for signs of hyperparathyroidism or osteomalacia. Much more rarely, many other pathologies can lead to brittle bone by demineralization or associated increased bone density.The radiologist needs to know the clinical context and notably the patient's age (childhood or adulthood) at discovery, and known pathologies and ongoing or previous treatments: corticotherapy, bisphosphonates, denosumab, or prior radiotherapy in the affected region. Diagnostic hypotheses are guided by the type of demineralization and the presence of lytic, mixed, or densifying areas. Lastly, the aspect and location of fractures and association with other bone abnormalities refine the diagnosis.

Technical concepts on blood pool phase SPECT (acquisition, reconstruction).

Guidelines for bone scintigraphy are well established and recommend the use of planar early phase images to investigate a number of clinical indications. With recent advances in gamma camera technology the use of SPECT/CT imaging in the early phases is now possible, offering the potential of improved diagnostic confidence and prognostic value. To date little work has been carried out to optimize the acquisition of early phase bone images using SPECT/CT with most of the available studies acquiring SPECT images after the traditional planar images to allow comparison of the two techniques. Imaging durations of 7 to 10 minutes have been commonly used. However, the use of iterative reconstruction algorithms has been investigated with rapid SPECT imaging to allow imaging durations as low as 4 minutes. The use of CZT based systems with increased sensitivity and improved energy and spatial resolution also offers the potential to reduce imaging times. The optimization of projection measurement order has been investigated as a method of reducing image artefacts as a result of changing tracer distribution during the SPECT acquisition. In this article we consider the current state of early phase SPECT imaging and possible areas for future investigation as well as recommendations for departments looking to adopt blood pool SPECT imaging as part of their routine clinical practice.

Comparative study of 18F-DCFPyL PET/CT and 99mTc-MDP SPECT/CT bone imaging for the detection of bone metastases in prostate cancer.

This study aimed to compare the diagnostic efficiency of 18F-DCFPyL PET/CT imaging and 99mTc-MDP SPECT/CT bone imaging for the detection of bone metastases in prostate cancer. A retrospective analysis was conducted on 31 patients with confirmed prostate cancer between September 2020 and September 2022 at China-Japan Union Hospital of Jilin University. All patients underwent 18F-DCFPyL PET/CT and 99mTc-MDP SPECT/CT bone imaging. The gold standard was the pathology or Best Valuable Comparator (BVC) result based on clinical follow-up. Diagnostic performance indicators, including sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV), were analyzed at both the patient and lesion levels. The paired sample chi-square test was used to compare the two imaging methods. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated for each method. The AUC values were compared using the Z-test, and a p-value < 0.05 was considered statistically significant. Of the 31 prostate cancer patients, 18 were diagnosed with bone metastases, with a total of 84 bone metastatic lesions. At the patient level, 18F-DCFPyL PET/CT imaging showed superior diagnostic performance compared to 99mTc-MDP SPECT/CT bone imaging in all indicators: sensitivity (100% vs. 77.8%, p < 0.01), specificity (92.3% vs. 69.2%, p < 0.05), accuracy (96.8% vs. 74.2%, p < 0.01), PPV (94.7% vs. 77.8%, p < 0.01), and NPV (100% vs. 69.2%, p < 0.01). The AUC values for 18F-DCFPyL PET/CT imaging and 99mTc-MDP SPECT/CT bone imaging were 0.962 and 0.735 (Z = 2.168, p < 0.05). At the lesion level, 18F-DCFPyL PET/CT imaging showed superior diagnostic performance compared to 99mTc-MDP SPECT/CT bone imaging in all indicators: sensitivity (97.6% vs. 72.6%, p < 0.01), specificity (95.7% vs. 73.9%, p < 0.01), accuracy (97.2% vs. 72.9%, p < 0.01), PPV (98.8% vs. 91.0%, p < 0.01), and NPV (91.7% vs. 42.5%, p < 0.01). The AUC values for 18F-DCFPyL PET/CT imaging and 99mTc-MDP SPECT/CT bone imaging were 0.966 and 0.733 (Z = 3.541, p < 0.001). Compared with 99mTc-MDP SPECT/CT bone imaging, 18F-DCFPyL PET/CT imaging demonstrated higher diagnostic efficiency for bone metastases in prostate cancer, and it can more accurately determine the presence of bone metastases. It is an important supplement to imaging examination for prostate cancer patients and has great potential and broad application prospects.

Constrained one-step material decomposition reconstruction of head CT data from a silicon photon-counting prototype.

Spectral CT material decomposition provides quantitative information but is challenged by the instability of the inversion into basis materials. We have previously proposed the constrained One-Step Spectral CT Image Reconstruction (cOSSCIR) algorithm to stabilize the material decomposition inversion by directly estimating basis material images from spectral CT data. cOSSCIR was previously investigated on phantomdata. This study investigates the performance of cOSSCIR using head CT datasets acquired on a clinical photon-counting CT (PCCT) prototype. This is the first investigation of cOSSCIR for large-scale, anatomically complex, clinical PCCT data. The cOSSCIR decomposition is preceded by a spectrum estimation and nonlinear counts correction calibration step to address nonideal detectoreffects. Head CT data were acquired on an early prototype clinical PCCT system using an edge-on silicon detector with eight energy bins. Calibration data of a step wedge phantom were also acquired and used to train a spectral model to account for the source spectrum and detector spectral response, and also to train a nonlinear counts correction model to account for pulse pileup effects. The cOSSCIR algorithm optimized the bone and adipose basis images directly from the photon counts data, while placing a grouped total variation (TV) constraint on the basis images. For comparison, basis images were also reconstructed by a two-step projection-domain approach of Maximum Likelihood Estimation (MLE) for decomposing basis sinograms, followed by filtered backprojection (MLE + FBP) or a TV minimization algorithm (MLE + TVmin ) to reconstruct basis images. We hypothesize that the cOSSCIR approach will provide a more stable inversion into basis images compared to two-step approaches. To investigate this hypothesis, the noise standard deviation in bone and soft-tissue regions of interest (ROIs) in the reconstructed images were compared between cOSSCIR and the two-step methods for a range of regularization constraintsettings. cOSSCIR reduced the noise standard deviation in the basis images by a factor of two to six compared to that of MLE + TVmin , when both algorithms were constrained to produce images with the same TV. The cOSSCIR images demonstrated qualitatively improved spatial resolution and depiction of fine anatomical detail. The MLE + TVmin algorithm resulted in lower noise standard deviation than cOSSCIR for the virtual monoenergetic images (VMIs) at higher energy levels and constraint settings, while the cOSSCIR VMIs resulted in lower noise standard deviation at lower energy levels and overall higher qualitative spatial resolution. There were no statistically significant differences in the mean values within the bone region of images reconstructed by the studied algorithms. There were statistically significant differences in the mean values within the soft-tissue region of the reconstructed images, with cOSSCIR producing mean values closer to the expectedvalues. The cOSSCIR algorithm, combined with our previously proposed spectral model estimation and nonlinear counts correction method, successfully estimated bone and adipose basis images from high resolution, large-scale patient data from a clinical PCCT prototype. The cOSSCIR basis images were able to depict fine anatomical details with a factor of two to six reduction in noise standard deviation compared to that of the MLE + TVmin two-stepapproach.

Bone Abnormalities Detection and Classification Using Deep Learning-Vgg16 Algorithm

Abstract: This paper presents a deep learning-based approach for the detection and classification of bone abnormalities using the Vgg16 algorithm. Bone abnormalities, such as fractures, tumors, and infections, are critical conditions that require accurate and timely diagnosis. Traditional diagnostic methods heavily rely on expert interpretation of medical images, leading to potential human errors and subjectivity. To address this challenge, we propose a novel framework that leverages deep learning techniques to automatically detect and classify bone abnormalities from radiographic images. Our framework employs the Vgg16 convolutional neural network architecture, which has shown remarkable performance in image classification tasks. We train the network using a large dataset of labeled bone images, including normal and abnormal cases. The training process involves fine-tuning the pre-trained Vgg16 model on our specific task to enhance its ability to extract relevant features from bone images. We also employ data augmentation techniques to improve the model's generalization capability. For evaluation, we conduct extensive experiments on a diverse dataset of bone radiographs. Our results demonstrate the effectiveness of our proposed approach in accurately detecting and classifying bone abnormalities. The Vgg16-based model achieves a high accuracy rate of X% in differentiating between normal and abnormal bone conditions, outperforming traditional methods by a significant margin. Furthermore, our model shows robustness against variations in image quality, such as noise and artifacts.

Comparing CT-Like Images Based on Ultra-Short Echo Time and Gradient Echo T1-Weighted MRI Sequences for the Assessment of Vertebral Disorders Using Histology and True CT as the Reference Standard.

Several magnetic resonance (MR) techniques have been suggested for radiation-free imaging of osseous structures. To compare the diagnostic value of ultra-short echo time and gradient echo T1-weighted MRI for the assessment of vertebral pathologies using histology and computed tomography (CT) as the reference standard. Prospective. Fifty-nine lumbar vertebral bodies harvested from 20 human cadavers (donor age 73 ± 13 years; 9 male). Ultra-short echo time sequence optimized for both bone (UTEb) and cartilage (UTEc) imaging and 3D T1-weighted gradient-echo sequence (T1GRE) at 3 T; susceptibility-weighted imaging (SWI) gradient echo sequence at 1.5 T. CT was performed on a dual-layer dual-energy CT scanner using a routine clinical protocol. Histopathology and conventional CT were acquired as standard of reference. Semi-quantitative and quantitative morphological features of degenerative changes of the spines were evaluated by four radiologists independently on CT and MR images independently and blinded to all other information. Features assessed were osteophytes, endplate sclerosis, visualization of cartilaginous endplate, facet joint degeneration, presence of Schmorl's nodes, and vertebral dimensions. Vertebral disorders were assessed by a pathologist on histology. Agreement between T1GRE, SWI, UTEc, and UTEb sequences and CT imaging and histology as standard of reference were assessed using Fleiss' κ and intra-class correlation coefficients, respectively. For the morphological assessment of osteophytes and endplate sclerosis, the overall agreement between SWI, T1GRE, UTEb, and UTEc with the reference standard (histology combined with CT) was moderate to almost perfect for all readers (osteophytes: SWI, κ range: 0.68-0.76; T1GRE: 0.92-1.00; UTEb: 0.92-1.00; UTEc: 0.77-0.85; sclerosis: SWI, κ range: 0.60-0.70; T1GRE: 0.77-0.82; UTEb: 0.81-0.92; UTEc: 0.61-0.71). For the visualization of the cartilaginous endplate, UTEc showed the overall best agreement with the reference standard (histology) for all readers (κ range: 0.85-0.93). Morphological assessment of vertebral pathologies was feasible and accurate using the MR-based bone imaging sequences compared to CT and histopathology. T1GRE showed the overall best performance for osseous changes and UTEc for the visualization of the cartilaginous endplate. 1 TECHNICAL EFFICACY: Stage 2.

Diffusion weighted imaging of extremity bone tumors-inter-reader analysis and incremental value over conventional MR imaging.

To determine whether the addition of diffusion-weighted imaging (DWI) to conventional MRI improves diagnostic accuracy of bone tumor characterization with the hypothesis that the DWI has incremental value in the diagnosis of osseous tumors. In this multireader cross-sectional validation study, four musculoskeletal radiologists evaluated osseous tumors blinded to final diagnosis in two rounds-first without DWI or apparent diffusion coefficient (ADC) maps, then months later with these available. Each reader recorded a binary result as to whether the lesion is benign or malignant. Intraclass correlation (ICC) and Conger's κ were used. Diagnostic performance measures including area under the receiver operating curve (AUC) were reported. 133 osseous tumors of the extremities (76 benign, 57 malignant) were tested. Blinded to DWI, average reader sensitivity, specificity, positive-predictive value, and negative-predictive value were 0.83, 0.92, 0.94, and 0.82, respectively. With DWI, the values were 0.85, 0.92, 0.94, and 0.83, respectively. Interreader agreement was good for both rounds (0.67 and 0.71, respectively, p-value > 0.05). Average reader confidence was 4.1 and 4.4, respectively (p-value < 0.001). ADC values and DWI/ADC ratios showed significant differences between benign and malignant tumors. DWI and ADC show statistically significantly different values of benign from malignant osseous tumors and mildly increased radiologist confidence with similar interreader reliability. However, given similar diagnostic accuracy, conventional MR imaging is adequate for bone tumor characterization and incremental value of DWI is limited. This paper is the first of its kind to report the use of DWI/ADC ratio for the diagnosis of bone tumors.

Analysis of risk factors for non-fusion of bone graft in anterior cervical discectomy and fusion: A clinical retrospective study

BackgroundBone graft fusion is a major concern among surgeons after Anterior Cervical Discectomy and Fusion (ACDF) surgery as non-fusion may lead to further physical and drug therapies. MethodsThe related risk elements of non-fusion of bone graft in ACDF surgery were retrospectively assessed. Patients receiving ACDF operation in our hospital from January 2015 to December 2019 were retrospectively analyzed. According to the criteria, 107 study subjects were recruited with a total of 164 surgical segments. The general information of patients, bone graft materials, imaging parameters, and clinical efficacy was recorded. T-test, chi-square test and binary logistic regression evaluation were employed to explore the risk factors of bone graft nonunion. ResultsLow housefield unit (HU) value, diabetes, allogeneic bone, and hydroxyapatite (HA) artificial bone could be risk factors for bone graft fusion in ACDF surgery. Further multivariate analysis was performed and confirmed those related factors of bone graft non-fusion including low HU value (non-fusion rate: 32.53% [27/83], OR = 5.024, p = 0.025), diabetes (non-fusion rate: 53.33% [8/15], OR = 4.776, p = 0.031), allogeneic bone (18.57% [13/70], OR = 3.964, p = 0.046), and artificial bone (68.29% [28/41], OR = 50.550, p < 0.01). ConclusionBy looking at bone graft fusion, selecting autologous iliac bone is an ideal selection to avoid non-fusion of bone graft in ACDF. Diabetes was more important predictor of bone graft nonunion than low HU value. Larger sample size and longer follow-up are required to further confirm these findings in the future.

Quasi-dendritic sulfonate-based organic small molecule for high-quality NIR-II bone-targeted imaging

The visualization of bone imaging in vivo is of great significance for the understanding of some bone-related diseases or physiological processes. Herein, a bone-targeted NIR-II small molecule (TTQF-SO3), which was modified with multiple sulfonate groups, was successfully fabricated for the second near-infrared (NIR-II) bone imaging. In vitro studies revealed that TTQF-SO3 showed high affinity for hydroxyapatite and excellent macrophage accumulation ability. In in vivo assays, TTQF-SO3 displayed high bone uptake ability and high NIR-II bone imaging quality, demonstrating the specific bone-targeting ability of the sulfonate-containing probe. In addition, the noninvasive NIR-II imaging detection in bone calcium loss was successfully verified in osteoporosis mice models. Moreover, the negative charge characteristic of TTQF-SO3 showed efficient lymphoid enrichment in living mice by intravenous injection. Overall, these warrant that our TTQF-SO3 is an optimal bone-targeted diagnostic agent for high-quality NIR-II imaging, highlighting its potential promise for clinical translation.Graphical

Design of a novel dual-polarized microwave sensor for human bone fracture detection using reactive impedance surfaces

This paper presents a novel miniaturized dual-polarized transceiver sensor system for detecting fractures in human bone tissues. The system features a patch antenna and a Reactive Impedance Surface (RIS) layer that reduces its size by 30% compared to conventional designs, resulting in enhanced fracture detection accuracy. Additionally, the system includes a dielectric plano-concave lens that adapts to the human body and improves impedance matching for optimal performance. The lens contains via holes filled with a lossy dielectric material similar to human fat tissue, which concentrates electromagnetic (EM) power and increases penetration depth for more effective crack detection. To detect fractures, two identical sensors are placed opposite each other on the tissue and moved simultaneously. The amount of EM power collected by the receiver sensor is measured using S-parameters; the transmission coefficient (S21) phases and contrast between the crack and surrounding tissue are used to construct images of fractured bones. Full-wave simulations and experimental measurements on a semi-solid human arm mimicking phantom demonstrate the proposed dual-polarized sensor's ability to detect the location and orientation of narrow cracks in the millimeter range. The system exhibits reliable performance across different human bodies.