The Classification of Lumbar Spondylolisthesis X-Ray Images Using Convolutional Neural Networks.

We aimed to develop and validate a deep convolutional neural network (DCNN) model capable of accurately identifying spondylolysis or spondylolisthesis on lateral or dynamic X-ray images. A total of 2449 lumbar lateral and dynamic X-ray images were collected from two tertiary hospitals. These images were categorized into lumbar spondylolysis (LS), degenerative lumbar spondylolisthesis (DLS), and normal lumbar in a proportional manner. Subsequently, the images were randomly divided into training, validation, and test sets to establish a classification recognition network. The model training and validation process utilized the EfficientNetV2-M network. The model's ability to generalize was assessed by conducting a rigorous evaluation on an entirely independent test set and comparing its performance with the diagnoses made by three orthopedists and three radiologists. The evaluation metrics employed to assess the model's performance included accuracy, sensitivity, specificity, and F1 score. Additionally, the weight distribution of the network was visualized using gradient-weighted class activation mapping (Grad-CAM). For the doctor group, accuracy ranged from 87.9 to 90.0% (mean, 89.0%), precision ranged from 87.2 to 90.5% (mean, 89.0%), sensitivity ranged from 87.1 to 91.0% (mean, 89.2%), specificity ranged from 93.7 to 94.7% (mean, 94.3%), and F1 score ranged from 88.2 to 89.9% (mean, 89.1%). The DCNN model had accuracy of 92.0%, precision of 91.9%, sensitivity of 92.2%, specificity of 95.7%, and F1 score of 92.0%. Grad-CAM exhibited concentrations of highlighted areas in the intervertebral foraminal region. We developed a DCNN model that intelligently distinguished spondylolysis or spondylolisthesis on lumbar lateral or lumbar dynamic radiographs.

Puncture and Drainage Surgery for Intracerebral Hemorrhage Guided by 3D Printing Puncture Guide Plate.

OBJECTIVES: Accurate puncture is the key to ensure the effect of puncture and drainage surgery for intracerebral hemorrhage. It usually uses CT to guide the drainage tube to reach the center of the hematoma cavity, which has the problems of inaccurate positioning using 2D images and high requirements for surgeon's experience in brain anatomy and imaging diagnosis. The aim of this study was to use a 3D printing puncture guide plate to guide the puncture and drainage surgery for intracerebral hemorrhage. METHODS: The CT images were imported into 3D Slicer software to reconstruct 3D models of the head skin and intracerebral hematoma. The target was set in the center of the hematoma and the puncture path from the target to the entry point was designed, the 3D model of puncture guide plate was constructed and saved as stereolithography format file, which was imported into 3D printer to print. During surgery, the drainage tube was placed in the center of the hematoma guided by the 3D printing puncture guide plate, and the blood clot was extracted by the suction syringe. RESULTS: Eight patients with hypertensive intracerebral hemorrhage were treated with puncture and drainage surgery guided by 3D printing puncture guide plate. The average operation time of the 8 surgeries was 17.63 minutes. The drainage tubes were all precisely placed in the center of the hematoma, and the blood clots were all successfully extracted. The positioning errors of the 8 drainage tubes were between 1.76 mm and 2.68 mm, and the mean value was 2.10±0.32 mm. The hematoma clearance rate of the 8 patients was between 74.18% and 96.73%, and the mean value was 85.14±6.71%. CONCLUSIONS: The puncture and drainage surgery for intracerebral hemorrhage guided by 3D printing puncture guide plate helps to quickly and effortlessly localize intracerebral hematoma and achieves satisfactory hematoma clearance rate.

Predicting the Progress of Tuberculosis by Inflammatory Response-Related Genes Based on Multiple Machine Learning Comprehensive Analysis.

Background: Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, affects approximately one-quarter of the global population and is considered one of the most lethal infectious diseases worldwide. The prevention of latent tuberculosis infection (LTBI) from progressing into active tuberculosis (ATB) is crucial for controlling and eradicating TB. Unfortunately, currently available biomarkers have limited effectiveness in identifying subpopulations that are at risk of developing ATB. Hence, it is imperative to develop advanced molecular tools for TB risk stratification. Methods: The TB datasets were downloaded from the GEO database. Three machine learning models, namely LASSO, RF, and SVM-RFE, were used to identify the key characteristic genes related to inflammation during the progression of LTBI to ATB. The expression and diagnostic accuracy of these characteristic genes were subsequently verified. These genes were then used to develop diagnostic nomograms. In addition, single-cell expression clustering analysis, immune cell expression clustering analysis, GSVA analysis, immune cell correlation, and immune checkpoint correlation of characteristic genes were conducted. Furthermore, the upstream shared miRNA was predicted, and a miRNA-genes network was constructed. Candidate drugs were also analyzed and predicted. Results: In comparison to LTBI, a total of 96 upregulated and 26 downregulated genes related to the inflammatory response were identified in ATB. These characteristic genes have demonstrated excellent diagnostic performance and significant correlation with many immune cells and immune sites. The results of the miRNA-genes network analysis suggested a potential role of hsa-miR-3163 in the molecular mechanism of LTBI progressing into ATB. Moreover, retinoic acid may offer a potential avenue for the prevention of LTBI progression to ATB and for the treatment of ATB. Conclusion: Our research has identified key inflammatory response-related genes that are characteristic of LTBI progression to ATB and hsa-miR-3163 as a significant node in the molecular mechanism of this progression. Our analyses have demonstrated the excellent diagnostic performance of these characteristic genes and their significant correlation with many immune cells and immune checkpoints. The CD274 immune checkpoint presents a promising target for the prevention and treatment of ATB. Furthermore, our findings suggest that retinoic acid may have a role in preventing LTBI from progressing to ATB and in treating ATB. This study provides a new perspective for differential diagnosis of LTBI and ATB and may uncover potential inflammatory immune mechanisms, biomarkers, therapeutic targets, and effective drugs in the progression of LTBI into ATB.

Evaluation of kasugamycin as a chiral selector in capillary electrophoresis.

The discovery of novel chiral selectors always fascinates us. This work describes the chiral separation performances of a new chiral selector (kasugamycin, KAS) in capillary electrophoresis (CE) for six pairs of stereoisomers, including ephedrine and pseudoephedrine, quinine and quinidine, cinchonine and cinchonidine, and amlodipine, promethazine and ofloxacin enantiomers. Kasugamycin, an aminoglycoside antibiotic in agriculture, shows significant biological activity against rice blast with low toxicity. As it turns out, this new chiral selector possesses good CE compatibility and stereoselectivity towards model analytes. In this work, we systematically investigated several separation parameters including kasugamycin concentration, buffer pH, separation voltage and the composition of the buffer solution. A detailed discussion about the chiral recognition mechanism was made based on Statistical Product and Service Solution (SPSS) analysis, NMR experiments (1D and 2D) and molecular modeling. This is the first time that kasugamycin is utilized as a chiral selector in CE, and the development of new chiral selectors from agricultural or veterinary antibiotics deserves more attention.

Production and Anti-Inflammatory Performance of PVA Hydrogels Loaded with Curcumin Encapsulated in Octenyl Succinic Anhydride Modified Schizophyllan as Wound Dressings.

Amphiphilic polysaccharides can be used as wall materials and applied to encapsulate hydrophobic active chemicals; moreover, there is significant demand for novel medical high-molecular-weight materials with various functions. In order to prepare amphiphilic schizophyllan (SPG), octenyl succinic anhydride (OSA) was chosen to synthesize OSA-modified schizophyllan (OSSPG) using an esterified reaction. The modification of OSSPG was demonstrated through FT-IR and thermal analysis. Moreover, it was found that OSSPG has a better capacity for loading curcumin, and the loading amount was 20 µg/mg, which was 2.6 times higher than that of SPG. In addition, a hydrogel made up of PVA, borax, and C-OSSPG (OSSPG loaded with curcumin) was prepared by means of the one-pot method, based on the biological effects of curcumin and the immune-activating properties of SPG. The mechanical properties and biological activity of the hydrogel were investigated. The experimental results show that the dynamic cross-linking of PVA and borax provided the C-OSSPG/BP hydrogel dressing with exceptional self-healing properties, and it was discovered that the C-OSSPG content increased the hydrogel's swelling and moisturizing properties. In fibroblast cell tests, the cells treated with hydrogel had survival rates of 80% or above. Furthermore, a hydrogel containing C-OSSPG could effectively promote cell migration. Due to the excellent anti-inflammatory properties of curcumin, the hydrogel also significantly reduces the generation of inflammatory factors, such as TNF-α and IL-6, and thus has a potential application as a wound dressing medicinal material.

Ultra-wideband flexible radar-infrared bi-stealth absorber based on a patterned graphene.

In this work, an ultra-wideband flexible radar absorber with low infrared emissivity for a radar-infrared bi-stealth application utilizing multilayer patterned graphene is proposed. The proposed absorber consists of three layers of graphene films with different patterns, flexible substrates, lightweight foam, and a ground layer. The flexible graphene films, rather than the conventional lumped resistors, are adopted as omnidirectional resistors to achieve dual polarization and flexibility. On the top of the absorber, an infrared shielding layer (IRSL) consists of patterned Indium tin oxide (ITO) separated by a thin foam layer. Due to the low-pass characteristics and the high filling ratio of the top ITO layer, the infrared emissivity of the whole structure is reduced effectively while the radar absorption property is slightly affected. As a result, the 90% absorption band is from 1.96 GHz to 20.72 GHz (fractional bandwidth 165.4%), with a low infrared emissivity of about 0.35. Besides, a miniaturized unit is achieved with the period of 0.079 λl at the lowest absorption frequency, and the oblique angle incidence response is up to 45° for TE mode and 60° for TM mode. A plane and a bending prototype are fabricated and measured, respectively. The screen-printing technology is adopted to print the graphene resistive films, and the measurement results agree well with the simulation.

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.

Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.

Putting Families at the Center: the Role of Family System in Employee Work-Family Conflict and Voice Behavior.

Work-family conflict has become one of the most prominent challenges of modern-day work and a prominent research topic. However, the "family" in the work-family interface has been undertheorized, while research focuses on the workplace factors and individual characteristics in relation to work-family conflict (WFC). Placing the family at the center of theorizing, we adopt the Contextual Model of Family Stress (CMFS) as an overarching framework, which conceptualizes the family as a complex system comprising the family members, the environment in which they are situated, and their interactions with the environment and with one another. Guided by CMFS, we theorized WFC as a disturbance to the family's structural and psychological contexts, which creates strain on the family well-being. Furthermore, we argued that family strain could produce strain and stress back to the focal workers, which reduces their voice behaviors at work. We further argue that workers' work-family segmentation preference will shape their experience of WFC and moderate the indirect effect of WFC on employee voice behavior through family well-being. We collected data across two multi-wave, time-lagged surveys in America (M-Turk, N = 330) and in China (organization employees, N = 209). We found that employee-rated family well-being mediates the negative relationship between WFC and voice behavior, and the indirect relationship is stronger as the employees' preference for segmentation is higher. The results open up a promising avenue for more nuanced inquiry into the family system framework and its role in the work-family interface.

Application of Diffusion Tensor Imaging Based on Automatic Fiber Quantification in Alzheimer's Disease.

BACKGROUND: Neuroimaging suggests that white matter microstructure is severely affected in Alzheimer's disease (AD) progression. However, whether alterations in white matter microstructure are confined to specific regions and whether they can be used as potential biomarkers to distinguish normal control (NC) from AD are unknown. METHODS: In this cross-sectional study, 33 cases of AD and 25 cases of NC were recruited for automatic fiber quantification (AFQ). A total of 20 fiber bundles were equally divided into 100 segments for quantitative assessment of fractional anisotropy (FA), mean diffusivity (MD), volume and curvature. In order to further evaluate the diagnostic value, the maximum redundancy minimum (mRMR) and LASSO algorithms were used to select features, calculate the Radscore of each subject, establish logistic regression models, and draw ROC curves, respectively, to assess the predictive power of four different models. RESULTS: There was a significant increase in the MD values in AD patients compared with healthy subjects. The differences were mainly located in the left cingulum hippocampus (HCC), left uncinate fasciculus (UF) and superior longitudinal fasciculus (SLF). The point-wise level of 20 fiber bundles was used as a classification feature, and the MD index exhibited the best performance to distinguish NC from AD. CONCLUSION: These findings contribute to the understanding of the pathogenesis of AD and suggest that abnormal white matter based on DTI-based AFQ analysis is helpful to explore the pathogenesis of AD.

Why do followers feel inauthentic and withdraw? The joint effect of leader Machiavellianism and perceived collectivistic work climate.

OBJECTIVE: Although past research has shown the negative consequences of state inauthenticity (i.e., the experience of inauthenticity), what triggers state inauthenticity remains to be better understood. We focus on leader Machiavellianism (Mach), defined as the extent to which leaders engage in unethical and manipulative behaviors to attain their goals, as a predictor of follower state inauthenticity. Drawing on the social misfit argument, we examined a model in which leader Mach, jointly with a perceived collectivistic work climate, determines follower state inauthenticity and subsequent work withdrawal. METHOD: We used a vignette-based lab study (303 participants from the United States) and a daily diary field study (476 daily responses from 69 participants recruited from China). RESULTS: Across two studies, we found that follower state inauthenticity mediated the relationship between leader Mach and follower work withdrawal. The positive relationship between leader Mach and follower state inauthenticity was strengthened by a perceived collectivistic work climate. CONCLUSIONS: The present research underlines the importance of the social environment in influencing follower state inauthenticity at work and shifts research attention from the consequences of state inauthenticity to its predictors.

Brain network analyses of diffusion tensor imaging for brain aging.

The approach of graph-based diffusion tensor imaging (DTI) networks has been used to explore the complicated structural connectivity of brain aging. In this study, the changes of DTI networks of brain aging were quantitatively and qualitatively investigated by comparing the characteristics of brain network. A cohort of 60 volunteers was enrolled and equally divided into young adults (YA) and older adults (OA) groups. The network characteristics of critical nodes, path length (Lp), clustering coefficient (Cp), global efficiency (Eglobal), local efficiency (Elocal), strength (Sp), and small world attribute (σ) were employed to evaluate the DTI networks at the levels of whole brain, bilateral hemispheres and critical brain regions. The correlations between each network characteristic and age were predicted, respectively. Our findings suggested that the DTI networks produced significant changes in network configurations at the critical nodes and node edges for the YA and OA groups. The analysis of whole brains network revealed that Lp, Cp increased (p < 0.05, positive correlation), Eglobal, Elocal, Sp decreased (p < 0.05, negative correlation), and σ unchanged (p ≥ 0.05, non-correlation) between the YA and OA groups. The analyses of bilateral hemispheres and brain regions showed similar results as that of the whole-brain analysis. Therefore the proposed scheme of DTI networks could be used to evaluate the WM changes of brain aging, and the network characteristics of critical nodes exhibited valuable indications for WM degeneration.

Super-hydrophobic Cellulose Nanofiber Air Filter with Highly Efficient Filtration and Humidity Resistance.

High-air humidity, especially condensation into droplets under the influence of temperature, can pose a serious threat to air purification filters. This report introduces the use of methyltrimethoxysilane (MTMS) for the silanization hydrophobic modification of cellulose nanofibers (CNFs) and obtains an air filter with super-hydrophobicity (CA = 152.4°) and high-efficiency filtration of particulate matter (PM) through the freeze-drying technology. The antihumidity performance of CNFs filters that undergo hydrophobic modification in high-humidity air is improved. Especially in the case of high-humidity air forming condensed water droplets, the increase in the rate of filtration resistance of the hydrophobically modified CNFs filter is much lower than that of the unmodified filter. In addition, the water-vapor-transmission rate of the hydrophobically modified filter is improved. More importantly, adding MTMS can regulate the porous structure of CNFs filters and improve the filtration performance. The specific surface area and the porosity of the filter are 26.54 m2/g and 99.21%, respectively, and the filtering effects of PM1.0 and PM2.5 reach 99.31 and 99.75%, respectively, while a low-filtration resistance (42 Pa) and a quality factor of up to 0.122 Pa-1 are achieved. This work has improved the application potential of high-performance air-purification devices to remove particulate pollution and may provide useful insights to design next-generation air filters suitable for application in high-air humidity.

A robust automated surface-matching registration method for neuronavigation.

PURPOSE: The surface-matching registration method in the current neuronavigation completes the coarse registration mainly by manually selecting anatomical landmarks, which increases the registration time, makes the automatic registration impossible and sometimes results in mismatch. It may be more practical to use a fast, accurate, and automatic spatial registration method for the patient-to-image registration. METHODS: A coarse-to-fine spatial registration method to automatically register the patient space to the image space without placing any markers on the head of the patient was proposed. Three-dimensional (3D) keypoints were extracted by 3D Harris corner detector from the point clouds in the patient and image spaces, and used as input to the 4-points congruent sets (4PCS) algorithm which automatically registered the keypoints in the patient space with the keypoints in the image space without any assumptions about initial alignment. Coarsely aligned point clouds in the patient and image space were then fine-registered with a variant of the iterative closest point (ICP) algorithm. Two experiments were designed based on one phantom and five patients to validate the efficiency and effectiveness of the proposed method. RESULTS: Keypoints were extracted within 7.0 s with a minimum threshold 0.001. In the phantom experiment, the mean target registration error (TRE) of 15 targets on the surface of the elastic phantom in the five experiments was 1.17 ± 0.04 mm, and the average registration time was 17.4 s. In the clinical experiments, the mean TRE of the targets on the first, second, third, fourth, and fifth patient's head surface were 1.70 ± 0.32 mm, 1.83 ± 0.38 mm, 1.64 ± 0.3 mm, 1.67 ± 0.35 mm, and 1.72 ± 0.31 mm, respectively, and the average registration time was 21.4 s. Compared with the method only based on the 4PCS and ICP algorithm and the current clinical method, the proposed method has obvious speed advantage while ensuring the registration accuracy. CONCLUSIONS: The proposed method greatly improves the registration speed while guaranteeing the equivalent or higher registration accuracy, and avoids a tedious manual process for the coarse registration.

An Automatic Spatial Registration Method for Image-Guided Neurosurgery System.

OBJECTIVE: This study aimed to investigate the feasibility of an automatic marker-free patient-to-image spatial registration method based on the 4-points congruent sets (4PCS) and iterative closest point (ICP) algorithm for the image-guided neurosurgery system (IGNS). METHODS: A portable scanner was used to obtain the point cloud of the patient's entire head. The 4PCS algorithm, which is resilient to noise and outliers, automatically registered the point cloud in the patient space to the surface reconstructed from the patient's preoperative images in the image space without any assumptions about initial alignment. A variant of the ICP algorithm was then used to finish the fine registration. Two phantoms and 3 patients' experiments were performed to demonstrate the effectiveness of the proposed method. RESULTS: In the phantom experiments, the mean target registration error of 15 targets on the surface of the rigid and the elastic phantoms were 1.02 ±â€Š0.18 mm and 1.27 ±â€Š0.36 mm, respectively. In the clinical experiments, the mean target registration error of 7 targets on the first, second and third patient's head were 1.88 ±â€Š0.19 mm, 1.84 ±â€Š0.19 mm, and 1.89 ±â€Š0.18 mm, respectively, which was sufficient to meet clinical requirements. The registration accuracy and registration time using the proposed method are better than that using the method based on manually coarse registration and automatic fine registration. CONCLUSIONS: It is feasible to use the automatic spatial registration method based on the 4PCS and ICP algorithm for the IGNS. Moreover, it can replace the spatial registration method based on manually selected anatomical landmarks combined with the automatic fine registration in the currently used IGNS.

Multimodal image registration based on binary gradient angle descriptor.

PURPOSE: Multimodal image registration plays an important role in image-guided interventions/therapy and atlas building, and it is still a challenging task due to the complex intensity variations in different modalities. METHODS: The paper addresses the problem and proposes a simple, compact, fast and generally applicable modality-independent binary gradient angle descriptor (BGA) based on the rationale of gradient orientation alignment. The BGA can be easily calculated at each voxel by coding the quadrant in which a local gradient vector falls, and it has an extremely low computational complexity, requiring only three convolutions, two multiplication operations and two comparison operations. Meanwhile, the binarized encoding of the gradient orientation makes the BGA more resistant to image degradations compared with conventional gradient orientation methods. The BGA can extract similar feature descriptors for different modalities and enable the use of simple similarity measures, which makes it applicable within a wide range of optimization frameworks. RESULTS: The results for pairwise multimodal and monomodal registrations between various images (T1, T2, PD, T1c, Flair) consistently show that the BGA significantly outperforms localized mutual information. The experimental results also confirm that the BGA can be a reliable alternative to the sum of absolute difference in monomodal image registration. The BGA can also achieve an accuracy of [Formula: see text], similar to that of the SSC, for the deformable registration of inhale and exhale CT scans. Specifically, for the highly challenging deformable registration of preoperative MRI and 3D intraoperative ultrasound images, the BGA achieves a similar registration accuracy of [Formula: see text] compared with state-of-the-art approaches, with a computation time of 18.3 s per case. CONCLUSIONS: The BGA improves the registration performance in terms of both accuracy and time efficiency. With further acceleration, the framework has the potential for application in time-sensitive clinical environments, such as for preoperative MRI and intraoperative US image registration for image-guided intervention.

A Surface-Based Spatial Registration Method Based on Sense Three-Dimensional Scanner.

OBJECTIVE: The purpose of this study was to investigate the feasibility of a surface-based registration method based on a low-cost, hand-held Sense three-dimensional (3D) scanner in image-guided neurosurgery system. METHODS: The scanner was calibrated prior and fixed on a tripod before registration. During registration, a part of the head surface was scanned at first and the spatial position of the adapter was recorded. Then the scanner was taken off from the tripod and the entire head surface was scanned by moving the scanner around the patient's head. All the scan points were aligned to the recorded spatial position to form a unique point cloud of the head by the automatic mosaic function of the scanner. The coordinates of the scan points were transformed from the device space to the adapter space by a calibration matrix, and then to the patient space. A 2-step patient-to-image registration method was then performed to register the patient space to the image space. RESULTS: The experimental results showed that the mean target registration error of 15 targets on the surface of the phantom was 1.61±0.09 mm. In a clinical experiment, the mean target registration error of 7 targets on the patient's head surface was 2.50±0.31 mm, which was sufficient to meet clinical requirements. CONCLUSIONS: It is feasible to use the Sense 3D scanner for patient-to-image registration, and the low-cost Sense 3D scanner can take the place of the current used scanner in the image-guided neurosurgery system.

Smaller-loss planar SPP transmission line than conventional microstrip in microwave frequencies.

Transmission line is a basic component in all passive devices, integrated circuits, and systems. Microstrip is the most popular transmission line in the microwave and millimeter-wave frequencies, and has been widely used in current electronic devices, circuits, and systems. One of the important issues to be solved in such applications is the relatively large transmission loss of microstrip. Here, we propose a method to reduce the loss of microwave transmission line based on the designable wavenumber of spoof surface plasmon polaritons (SPPs). Using this characteristic, we analyze and experimentally demonstrate the low-loss feature of the SPP transmission line through the perturbation method and S-parameter measurements, respectively. Both simulation and experimental results show that the SPP transmission line has much smaller transmission loss than traditional microstrip with the same size in the microwave frequencies. Hence, the spoof SPP transmission line may make a big step forward in the low-loss circuits and systems.

A new markerless patient-to-image registration method using a portable 3D scanner.

PURPOSE: Patient-to-image registration is critical to providing surgeons with reliable guidance information in the application of image-guided neurosurgery systems. The conventional point-matching registration method, which is based on skin markers, requires expensive and time-consuming logistic support. Surface-matching registration with facial surface scans is an alternative method, but the registration accuracy is unstable and the error in the more posterior parts of the head is usually large because the scan range is limited. This study proposes a new surface-matching method using a portable 3D scanner to acquire a point cloud of the entire head to perform the patient-to-image registration. METHODS: A new method for transforming the scan points from the device space into the patient space without calibration and tracking was developed. Five positioning targets were attached on a reference star, and their coordinates in the patient space were measured prior. During registration, the authors moved the scanner around the head to scan its entire surface as well as the positioning targets, and the scanner generated a unique point cloud in the device space. The coordinates of the positioning targets in the device space were automatically detected by the scanner, and a spatial transformation from the device space to the patient space could be calculated by registering them to their coordinates in the patient space that had been measured prior. A three-step registration algorithm was then used to register the patient space to the image space. The authors evaluated their method on a rigid head phantom and an elastic head phantom to verify its practicality and to calculate the target registration error (TRE) in different regions of the head phantoms. The authors also conducted an experiment with a real patient's data to test the feasibility of their method in the clinical environment. RESULTS: In the phantom experiments, the mean fiducial registration error between the device space and the patient space, the mean surface registration error, and the mean TRE of 15 targets on the surface of each phantom were 0.34 ± 0.01 mm and 0.33 ± 0.02 mm, 1.17 ± 0.02 mm and 1.34 ± 0.10 mm, and 1.06 ± 0.11 mm and 1.48 ± 0.21 mm, respectively. When grouping the targets according to their positions on the head, high accuracy was achieved in all parts of the head, and the TREs were similar across different regions. The authors compared their method with the current surface registration methods that use only a part of the facial surface on the elastic phantom, and the mean TRE of 15 targets was 1.48 ± 0.21 mm and 1.98 ± 0.53 mm, respectively. In a clinical experiment, the mean TRE of seven targets on the patient's head surface was 1.92 ± 0.18 mm, which was sufficient to meet clinical requirements. CONCLUSIONS: The proposed surface-matching registration method provides sufficient registration accuracy even in the posterior area of the head. The 3D point cloud of the entire head, including the facial surface and the back of the head, can be easily acquired using a portable 3D scanner. The scanner does not need to be calibrated prior or tracked by the optical tracking system during scanning.

Anatomical landmarks for point-matching registration in image-guided neurosurgery.

BACKGROUND: Accurate patient to image registration is the core for successful image-guided neurosurgery. While skin adhesive markers (SMs) are widely used in point-matching registration, a proper implementation of anatomical landmarks (ALs) may overcome the inconvenience brought by the use of SMs. METHODS: Using nine ALs, a set of three configurations of different combinations of them is proposed. These configurations are defined according to the required positioning of the patient's head during surgery and the resulting distribution of the expected target registration error (TRE). These configurations were first evaluated by simulation experiment using the data of 20 patients from two hospitals, and then testing the applicability of them in eight real clinical surgeries of neuronavigation. RESULTS: The results of the simulation experiment showed that, by incorporating a fiducial registration error (FRE) of 3.5 mm measured in the clinical setting, the expected TRE in the whole skull was less than 2.5 mm, and the expected TRE in the whole brain was less than 1.75 mm when using all the nine ALs. A small TRE could also be achieved in the corresponding surgical field by using the other three configurations with less ALs. In the clinical experiment, the FLE ranges in the image and the patient space were 1.4-3.6 mm and 1.6-5.5 mm, respectively. The measured TRE and FRE were 3.1 ± 0.75 mm and 3.5 ± 0.17 mm, respectively. CONCLUSIONS: The AL configurations proposed in this investigation provide sufficient registration accuracy and can help to avoid the disadvantages of SMs if used clinically.

In situ fabrication of metal-organic hybrid gels in a capillary for online enrichment of trace analytes in aqueous samples.

In this work we demonstrated a facile method for in situ fabrication of the Fe(3+)-1,3,5-benzenetricarboxylic acid (BTC) metal-organic coordination polymer gel in a capillary, which was efficiently applied to the online enrichment of trace polycyclic aromatic hydrocarbons (PAHs) in environmental water and amphetamines drugs in urine.