Vertebroplasty is a common and effective treatment for symptomatic osteoporotic vertebral compression fractures. However, the cemented and adjacent vertebras have a risk of recollapse due to largely unassured mechanisms, among which excessive stiffness of bone cement may be an important risk factor. This study aimed to find the most appropriate range of bone cement stiffness by analyzing its biomechanical effects on the augmented and adjacent vertebras of individual patient after vertebroplasty. A three-dimensional finite element model of T11-L1 osteoligamentous vertebras was reconstructed according to individual computed tomography data and validated by post mortem human subject experiment in literatures. Bone cement of varying stiffness was injected into the trabecular core of the T12 vertebra simulatively. The maximum von Mises stresses on cancellous and cortical bones of T11-L1 vertebras were analyzed under the loading conditions of flexion, extension, bending, and torsion. For the adjacent T11 and L1 vertebras, the stepwise elevation of the bone cement elastic modulus increased the maximum von Mises stress on the cancellous bone, but its effect on cortical bone was negligible. For the augmented T12 vertebra, the stresses on cancellous bone increased slightly under the loading condition of lateral bending and remained no impact on cortical bone. The linear interpolation revealed that the most suitable range of cement elastic modulus is 833.1 and 1408.1 Mpa for this patient. Increased elastic modulus of bone cement may lead to a growing risk of recollapse for the cemented vertebra as well as the adjacent vertebras. Our study provides a fresh perspective in clinical optimization of individual therapy in vertebroplasty.
Needles are advanced tools commonly used in minimally invasive medical procedures. The accurate manoeuvrability of flexible needles through soft tissues is strongly determined by variations in tissue stiffness, which affects the needle-tissue interaction and thus causes needle deflection. This work presents a variable stiffness mechanism for percutaneous needles capable of compensating for variations in tissue stiffness and undesirable trajectory changes. It is composed of compliant segments and rigid plates alternately connected in series and longitudinally crossed by four cables. The tensioning of the cables allows the omnidirectional steering of the tip and the stiffness tuning of the needle. The mechanism was tested separately under different working conditions, demonstrating a capability to exert up to 3.6 N. Afterwards, the mechanism was integrated into a needle, and the overall device was tested in gelatine phantoms simulating the stiffness of biological tissues. The needle demonstrated the capability to vary deflection (from 11.6 to 4.4 mm) and adapt to the inhomogeneity of the phantoms (from 21 to 80 kPa) depending on the activation of the variable stiffness mechanism.
Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.
Graphical abstract We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.
We propose a multiscale texture features based on Laplacian-of Gaussian (LoG) filter to predict progression free (PFS) and overall survival (OS) in patients newly diagnosed with glioblastoma (GBM). Experiments use the extracted features derived from 40 patients of GBM with T1-weighted imaging (T1-WI) and Fluid-attenuated inversion recovery (FLAIR) images that were segmented manually into areas of active tumor, necrosis, and edema. Multiscale texture features were extracted locally from each of these areas of interest using a LoG filter and the relation between features to OS and PFS was investigated using univariate (i.e., Spearman’s rank correlation coefficient, log-rank test and Kaplan-Meier estimator) and multivariate analyses (i.e., Random Forest classifier). Three and seven features were statistically correlated with PFS and OS, respectively, with absolute correlation values between 0.32 and 0.36 and p?<?0.05. Three features derived from active tumor regions only were associated with OS (p?<?0.05) with hazard ratios (HR) of 2.9, 3, and 3.24, respectively. Combined features showed an AUC value of 85.37 and 85.54% for predicting the PFS and OS of GBM patients, respectively, using the random forest (RF) classifier. We presented a multiscale texture features to characterize the GBM regions and predict he PFS and OS. The efficiency achievable suggests that this technique can be developed into a GBM MR analysis system suitable for clinical use after a thorough validation involving more patients.
Graphical abstract Scheme of the proposed model for characterizing the heterogeneity of GBM regions and predicting the overall survival and progression free survival of GBM patients. (1) Acquisition of pretreatment MRI images; (2) Affine registration of T1-WI image with its corresponding FLAIR images, and GBM subtype (phenotypes) labelling; (3) Extraction of nine texture features from the three texture scales fine, medium, and coarse derived from each of GBM regions; (4) Comparing heterogeneity between GBM regions by ANOVA test; Survival analysis using Univariate (Spearman rank correlation between features and survival (i.e., PFS and OS) based on each of the GBM regions, Kaplan-Meier estimator and log-rank test to predict the PFS and OS of patient groups that grouped based on median of feature), and multivariate (random forest model) for predicting the PFS and OS of patients groups that grouped based on median of PFS and OS.
Extraction of a clean fetal electrocardiogram (ECG) from non-invasive abdominal recordings is one of the biggest challenges in fetal monitoring. An ECG allows for the interpretation of the electrical heart activity beyond the heart rate and heart rate variability. However, the low signal quality of the fetal ECG hinders the morphological analysis of its waveform in clinical practice. The time-sequenced adaptive filter has been proposed for performing optimal time-varying filtering of non-stationary signals having a recurring statistical character. In our study, the time-sequenced adaptive filter is applied to enhance the quality of multichannel fetal ECG after the maternal ECG is removed. To improve the performance of the filter in cases of low signal-to-noise ratio (SNR), we enhance the ECG reference signals by averaging consecutive ECG complexes. The performance of the proposed augmented time-sequenced adaptive filter is evaluated in both synthetic and real data from PhysioNet. This evaluation shows that the suggested algorithm clearly outperforms other ECG enhancement methods, in terms of uncovering the ECG waveform, even in cases with very low SNR. With the presented method, quality of the fetal ECG morphology can be enhanced to the extent that the ECG might be fit for use in clinical diagnostics.
Graphical abstract The extracted fetal ECG signals from non-invasive abdominal recordings still contain a substantial amount of noise. The time-sequenced adaptive filter provides a relatively accurate estimate of the underlying fetal ECG signal when the quality of the reference channels is enhanced prior to filtering.
Atherosclerosis is a type of cardiovascular disease which may cause stroke. It is due to the deposition of fatty plaque in the artery walls resulting in the reduction of elasticity gradually and hence restricting the blood flow to the heart. Hence, an early prediction of carotid plaque deposition is important, as it can save lives. This paper proposes a novel data mining framework for the assessment of atherosclerosis in its early stage using ultrasound images. In this work, we are using 1353 symptomatic and 420 asymptomatic carotid plaque ultrasound images. Our proposed method classifies the symptomatic and asymptomatic carotid plaques using bidimensional empirical mode decomposition (BEMD) and entropy features. The unbalanced data samples are compensated using adaptive synthetic sampling (ADASYN), and the developed method yielded a promising accuracy of 91.43%, sensitivity of 97.26%, and specificity of 83.22% using fourteen features. Hence, the proposed method can be used as an assisting tool during the regular screening of carotid arteries in hospitals.
The level of physical stress rules the adaptative response of peripheral nerves, which is crucial to assess their physiological and pathological states. To this aim, in this work, different computational approaches were presented to model the stress response of in vitro peripheral nerves undergoing longitudinal stretch. More specifically, the effects of geometrical simplifications were studied with respect to the amount of computational time needed to obtain relevant information. Similarly, the variation of compressibility of the peripheral nervous tissue was investigated with respect to the variation of longitudinal stress and transversal stretch variations, and with reference to the computational time needed for simulations. Finally, the effect of small dimensional changes was investigated to better understand whether the variation of time was only due to the amount of nodes or elements. In conclusion, since fast in silico models, able to assess the nerve stress, could be a strategic advantage in case of time constraints or on-line evaluation (e.g., surgical interventions), a synergistic use of these approaches was proposed as a possible strategy to decrease the computational time needed for simulations from minutes to seconds.
Graphical Abstract A synergistic approach involving both symmetry and tuning ofcompressibility allows the computational time to be considerably decreased
Neuronal excitability is determined in a complex way by several interacting factors, such as membrane dynamics, fibre geometry, electrode configuration, myelin impedance, neuronal terminations\(\dots \) This study aims to increase understanding in excitability, by investigating the impact of these factors on different models of myelinated and unmyelinated fibres (five well-known membrane models are combined with three electrostimulation models, that take into account the spatial structure of the neuron). Several excitability indices (rheobase, polarity ratio, bi/monophasic ratio, time constants\(\dots \)) are calculated during extensive parameter sweeps, allowing us to obtain novel findings on how these factors interact, e.g. how the dependency of excitability indices on the fibre diameter and myelin impedance is influenced by the electrode location and membrane dynamics. It was found that excitability is profoundly impacted by the used membrane model and the location of the neuronal terminations. The approximation of infinite myelin impedance was investigated by two implementations of the spatially extended non-linear node model. The impact of this approximation on the time constant of strength-duration plots is significant, most importantly in the Frankenhaeuser-Huxley membrane model for large electrode-neuron separations. Finally, a multi-compartmental model for C-fibres is used to determine the impact of the absence of internodes on excitability.
Graphical Abstract Electrostimulation models, obtained by combining five membrane models with three representations of the neuronal cable equation, are fed with electrode and stimulus input parameters. The dependency of neuronal excitability on the interaction of these input parameters is determined by deriving excitability indices from the spatiotemporal model response. The impact of the myelin impedance and the fibre diameter on neural excitability is also considered.
The imaging procedure shown in this paper has been developed for processing thermographic images, measuring the ocular surface temperature (OST) and visualizing the ocular thermal maps in a fast, reliable, and reproducible way. The strength of this new method is that the measured OSTs do not depend on the ocular geometry; hence, it is possible to compare the ocular profiles belonging to the same subject (right and left eye) as well as to different populations. In this paper, the developed procedure is applied on two subjects’ eyes: a healthy case and another affected by an ocular malignant lesion. However, the method has already been tested on a bigger group of subjects for clinical purpose. For demonstrating the potentiality of this method, both intra- and inter-examiner repeatability were investigated in terms of coefficients of repeatability (COR). All OST indices showed repeatability with small intra-examiner (%COR 0.06–0.80) and inter-examiner variability (%COR 0.03–0.94). Measured OSTs and thermal maps clearly showed the clinical condition of the eyes investigated. The subject with no ocular pathology had no significant difference (P value?=?0.25) between the OSTs of the right and left eye. On the contrary, the eye affected by a malignant lesion was significantly warmer (P value?<?0.0001) than the contralateral, where the lesion was located. This new procedure demonstrated its reliability; it is featured by simplicity, immediacy, modularity, and genericity. The latter point is extremely precious as thermography has been used, in the last decades, in different clinical applications.
