An atlas-based multimodal registration method for 2-dimension images with discrepancy structures was proposed in this paper. Atlas was utilized for complementing the discrepancy structure information in multimodal medical images. The scheme includes three steps: floating image to atlas registration, atlas to reference image registration, and field-based deformation. To evaluate the performance, a frame model, a brain model, and clinical images were employed in registration experiments. We measured the registration performance by the squared sum of intensity differences. Results indicate that this method is robust and performs better than the direct registration for multimodal images with discrepancy structures. We conclude that the proposed method is suitable for multimodal images with discrepancy structures.
Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system. We have developed a novel method for the classification of benign and malignant micro-calcification using an improved Fisher Linear Discriminant Analysis (LDA) approach for the linear transformation of segmented micro-calcification data in combination with a Support Vector Machine (SVM) variant to classify between the two classes. The results indicate an average accuracy equal to 96% which is comparable to state-of-the art methods in the literature.
A brain-computer interface (BCI) system allows direct communication between the brain and the external world. Common spatial pattern (CSP) has been used effectively for feature extraction of data used in BCI systems. However, many studies show that the performance of a BCI system using CSP largely depends on the filter parameters. The filter parameters that yield most discriminating information vary from subject to subject and manually tuning of the filter parameters is a difficult and time-consuming exercise. In this paper, we propose a new automated filter tuning approach for motor imagery electroencephalography (EEG) signal classification, which automatically and flexibly finds the filter parameters for optimal performance. We have evaluated the performance of our proposed method on two public benchmark datasets. Compared to the existing conventional CSP approach, our method reduces the average classification error rate by 2.89% and 3.61% for BCI Competition III dataset IVa and BCI Competition IV dataset I, respectively. Moreover, our proposed approach also achieved lowest average classification error rate compared to state-of-the-art methods studied in this paper. Thus, our proposed method can be potentially used for developing improved BCI systems, which can assist people with disabilities to recover their environmental control. It can also be used for enhanced disease recognition such as epileptic seizure detection using EEG signals.
A semi-automated processing approach was developed to assess the effects of early postnatal environmental tobacco smoke (ETS) on the cardiorespiratory control of newborn lambs. The system consists of several steps beginning with artifact rejection, followed by the selection of stationary segments, and ending with feature extraction. This approach was used in six lambs exposed to 20 cigarettes/day for the first 15 days of life, while another six control lambs were exposed to room air. On postnatal day 16, electrocardiograph and respiratory signals were obtained from a 6-h polysomnographic recording. The effects of postnatal ETS exposure on heart rate variability, respiratory rate variability, and cardiorespiratory interrelations were explored. The unique results suggest that early postnatal ETS exposure increases respiratory rate variability and decreases the coupling between cardiac and respiratory systems. Potentially harmful consequences in early life include unstable breathing and decreased adaptability of cardiorespiratory function, particularly during early life challenges, such as prematurity or viral infection.
This study investigates the inter-tester repeatability of an upper limb direct kinematic (ULDK) model specifically for the reporting of elbow flexion-extension (FE) during overhead sporting movements, such as cricket bowling. The ULDK model consists of an upper arm and a forearm connected with a 6° of freedom elbow joint. The ULDK model was assessed for inter-tester repeatability by calculating elbow FE during cricket bowling in two sessions, with unique testers applying the kinematic marker set in each session. Analysis of both elbow FE time-varying waveforms (statistical parametric mapping?=?0% time different) and extracted discrete events (no statistical differences, strong correlations >?0.9) support that this model is inter-tester repeatable at assessing elbow FE within the context of cricket bowling. This model is recommended as a framework in future studies for measuring elbow kinematics during other overhead sporting tasks, with recommendations for further participant-specific considerations.
Vascular morphology characterization is useful for disease diagnosis, risk stratification, treatment planning, and prediction of treatment durability. To quantify the dynamic surface geometry of tubular-shaped anatomic structures, we propose a simple, rigorous Lagrangian cylindrical coordinate system to monitor well-defined surface points. Specifically, the proposed system enables quantification of surface curvature and cross-sectional eccentricity. Using idealized software phantom examples, we validate the method’s ability to accurately quantify longitudinal and circumferential surface curvature, as well as eccentricity and orientation of eccentricity. We then apply the method to several medical imaging data sets of human vascular structures to exemplify the utility of this coordinate system for analyzing morphology and dynamic geometric changes in blood vessels throughout the body.
This paper presents a support fuzzy adaptive system for a hybrid proportional derivative controller that will refine its parameters during postprandial periods to enhance performance. Even though glucose controllers have improved over the last decade, tuning them and keeping them tuned are still major challenges. Changes in a patient’s lifestyle, stress, exercise, or other activities may modify their blood glucose system, making it necessary to retune or change the insulin dosing algorithm. This paper presents a strategy to adjust the parameters of a proportional derivative controller using the so-called safety auxiliary feedback element loop for type 1 diabetic patients. The main parameters, such as the insulin on board limit and proportional gain are tuned using postprandial performance indexes and the information given by the controller itself. The adaptive and robust performance of the control algorithm was assessed “in silico” on a cohort of virtual patients under challenging realistic scenarios considering mixed meals, circadian variations, time-varying uncertainties, sensor errors, and other disturbances. The results showed that an adaptive strategy can significantly improve the performance of postprandial glucose control, individualizing the tuning by directly taking into account the intra-patient variability of type 1 patients.
Graphical Abstract title: Postprandial glycaemia improvement via fuzzy adaptive control A fuzzy inference engine was implemented within a clinically tested artificial pancreas control system. The aim of the fuzzy system was to adapt controller parameters to improve postprandial blood glucose control while ensuring safety. Results show a significant improvement over time of the postprandial glucose response due to the adaptation, thus demonstrating the usefulness of the fuzzy adaptive system.
A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising.
In this paper, a detail-enhanced multimodality medical image fusion algorithm is proposed by using proposed multi-scale joint decomposition framework (MJDF) and shearing filter (SF). The MJDF constructed with gradient minimization smoothing filter (GMSF) and Gaussian low-pass filter (GLF) is used to decompose source images into low-pass layers, edge layers, and detail layers at multiple scales. In order to highlight the detail information in the fused image, the edge layer and the detail layer in each scale are weighted combined into a detail-enhanced layer. As directional filter is effective in capturing salient information, so SF is applied to the detail-enhanced layer to extract geometrical features and obtain directional coefficients. Visual saliency map-based fusion rule is designed for fusing low-pass layers, and the sum of standard deviation is used as activity level measurement for directional coefficients fusion. The final fusion result is obtained by synthesizing the fused low-pass layers and directional coefficients. Experimental results show that the proposed method with shift-invariance, directional selectivity, and detail-enhanced property is efficient in preserving and enhancing detail information of multimodality medical images.
Soft tissue deformation modelling forms the basis of development of surgical simulation, surgical planning and robotic-assisted minimally invasive surgery. This paper presents a new methodology for modelling of soft tissue deformation based on reaction-diffusion mechanics via neural dynamics. The potential energy stored in soft tissues due to a mechanical load to deform tissues away from their rest state is treated as the equivalent transmembrane potential energy, and it is distributed in the tissue masses in the manner of reaction-diffusion propagation of nonlinear electrical waves. The reaction-diffusion propagation of mechanical potential energy and nonrigid mechanics of motion are combined to model soft tissue deformation and its dynamics, both of which are further formulated as the dynamics of cellular neural networks to achieve real-time computational performance. The proposed methodology is implemented with a haptic device for interactive soft tissue deformation with force feedback. Experimental results demonstrate that the proposed methodology exhibits nonlinear force-displacement relationship for nonlinear soft tissue deformation. Homogeneous, anisotropic and heterogeneous soft tissue material properties can be modelled through the inherent physical properties of mass points.
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.
The reconstruction of bioluminescence tomography (BLT) is severely ill-posed due to the insufficient measurements and diffuses nature of the light propagation. Predefined permissible source region (PSR) combined with regularization terms is one common strategy to reduce such ill-posedness. However, the region of PSR is usually hard to determine and can be easily affected by subjective consciousness. Hence, we theoretically developed a filtered maximum likelihood expectation maximization (fMLEM) method for BLT. Our method can avoid predefining the PSR and provide a robust and accurate result for global reconstruction. In the method, the simplified spherical harmonics approximation (SPN) was applied to characterize diffuse light propagation in medium, and the statistical estimation-based MLEM algorithm combined with a filter function was used to solve the inverse problem. We systematically demonstrated the performance of our method by the regular geometry- and digital mouse-based simulations and a liver cancer-based in vivo experiment.
This study describes a new model of the force generated by tibialis anterior muscle with three new features: single-fiber action potential, twitch force, and pennation angle. This model was used to investigate the relative effects and interaction of ten age-associated neuromuscular parameters. Regression analysis (significance level of 0.05) between the neuromuscular properties and corresponding simulated force produced at the footplate was performed. Standardized slope coefficients were computed to rank the effect of the parameters. The results show that reduction in the average firing rate is the reason for the sharp decline in the force and other factors, such as number of muscle fibers, specific force, pennation angle, and innervation ratio. The fast fiber ratio affects the simulated force through two significant interactions. This study has ranked the individual contributions of the neuromuscular factors to muscle strength decline of the TA and identified firing rate decline as the biggest cause followed by decrease in muscle fiber number and specific force. The strategy for strength preservation for the elderly should focus on improving firing rate.
For any implantable device size and efficiency are critical properties. Thus, a linear motor for a Total Artificial Heart was optimized with focus on driver electronics and control strategies. Hardware requirements were defined from power supply and motor setup. Four full bridges were chosen for the power electronics. Shunt resistors were set up for current measurement. Unipolar and bipolar switching for power electronics control were compared regarding current ripple and power losses. Here, unipolar switching showed smaller current ripple and required less power to create the necessary motor forces. Based on calculations for minimal power losses Lorentz force was distributed to the actor’s four coils. The distribution was determined as ratio of effective magnetic flux through each coil, which was captured by a force test rig. Static and dynamic measurements under physiological conditions analyzed interaction of control and hardware and all efficiencies were over 89%. In conclusion, the designed electronics, optimized control strategy and applied current distribution create the required motor force and perform optimal under physiological conditions. The developed driver electronics and control offer optimized size and efficiency for any implantable or portable device with multiple independent motor coils.
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.
Bone cells sense mechanical load, which is essential for bone growth and remodeling. In a fracture, this mechanism is compromised. Electromagnetic stimulation has been widely used to assist in bone healing, but the underlying mechanisms are largely unknown. A recent hypothesis suggests that electromagnetic stimulation could influence tissue biomechanics; however, a detailed quantitative understanding of EM-induced biomechanical changes in the bone is unavailable. This paper used a muscle/bone model to study the biomechanics of the bone under EM exposure. Due to the dielectric properties of the muscle/bone interface, a time-varying magnetic field can generate both compressing and shear stresses on the bone surface, where many mechanical sensing cells are available for cellular mechanotransduction. I calculated these stresses and found that the shear stress is significantly greater than the compressing stress. Detailed parametric analysis suggests that both the compressing and shear stresses are dependent on the geometrical and electrical properties of the muscle and the bone. These stresses are also functions of the orientation of the coil and the frequency of the magnetic field. It is speculated that the EM field could apply biomechanical influence to fractured bone, through the fine-tuning of the controllable field parameters.
In order to solve the problem of the short lifespan of the neural electrode caused by micro motion, this study designed a novel neural electrode based on lumped compliance compliant mechanism to control different modes of micro-motion in a more effective way. According to the mathematical modeling of the novel neural electrode, the equivalent bending stiffness and equivalent tensile (compression) stiffness were calculated. The results of the finite element analysis based on the mathematical modeling revealed that the novel neural electrode showed excellent micro-motion-attenuation capability. The static analysis results showed that the novel design dramatically reduced the maximum displacement of the brain in 51% and the maximum stress in 41% under longitudinal micro-motion environment. It also effectively reduced the 5.1% maximum stress while maintaining the maximum displacement under lateral micro-motion environment. The experimental results based on the tissue injury evaluation system also confirmed that the novel electrode is more effective in micro-motion attenuation than the reference one. In detail, the strain of the brain tissue caused by the implantation of the neural electrode was decreased by 1.26 to 27.84% at the insertion depth of 3 mm, and 0.522 to 17.24% at the insertion depth of 4.5 mm, which has convinced the effectiveness of the design.
Finite element models in conjunction with adequate constitutive relations are pivotal in several physiological and medical applications related to both native and engineered tissues, allowing to predict the tissue response under various loading states. In order to get reliable results, however, the validation of the constitutive models is crucial. Therefore, the main purpose of this work is to provide an experimental-computational approach to the biomechanical investigation of soft tissues such as the dermis. This is accomplished by implementing and validating three widely adopted hyperelastic constitutive models (the Ogden, the Holzapfel, and the Gasser-Ogden-Holzapfel laws) supposed to be adequate to reproduce human reticular dermis mechanical behavior. Biaxial experimental data have represented the basis for the determination of the respective material parameters identified thanks to the definition of a cost function accounting for the discrepancy between experimental and predicted data. Afterwards, the experimental tests have been reproduced through finite element simulations. Hence, the constitutive laws have been validated comparing experimental and numerical outcomes in terms of displacements of four reference points and stress-strain relations. Hence, an experimental-numerical framework is proposed for the investigation of collagenous tissues, which could become more accurate with larger and independent experimental datasets.
