The advent of multifunctional nanoparticle has enabled numerous innovative strategies in diagnostics, imaging, and cancer therapy. Despite the intense research efforts in developing new nanoparticles and surface bonding ligands, one major obstacle in achieving highly effective treatment, including minimizing detrimental side effects, is the inability to deliver drug-carrying nanoparticles from the injection point directly to the tumor site. The present study seeks to employ a direct nanodrug delivery methodology to feed multifunctional nanoparticles directly to tumor vasculatures, sparing healthy tissue. An important aspect to examine is how the interactions between such nanoparticles and relatively large red blood cells would affect the transport and delivery efficiency of nanodrugs. So, a novel computer simulation model has been developed to study nanoparticle transport in a representative human hepatic artery system, subject to shear-induced diffusion of nanoparticles due to hydrodynamic interactions with red blood cells. The particle-size effect was also evaluated by comparing the dynamics of nanoparticles with microspheres. Results from computer simulations under physiologically realistic conditions indicate that shear-induced diffusion has a significant effect on nanoparticle transport, even in large arteries. Nevertheless, as documented, direct nanodrug delivery to tumor-feeding hepatic artery branches is feasible.
Respiratory inductance plethysmography (RIP) sensor is an inexpensive, non-invasive, easy-to-use transducer for collecting respiratory movement data. Studies have reported that the RIP signal’s amplitude and frequency can be used to discriminate respiratory diseases. However, with the conventional approach of RIP data analysis, respiratory muscle effort cannot be estimated. In this paper, the estimation of the respiratory muscle effort through RIP signal was proposed. A complementary ensemble empirical mode decomposition method was used, to extract hidden signals from the RIP signals based on the frequency bands of the activities of different respiratory muscles. To validate the proposed method, an experiment to collect subjects’ RIP signal under thoracic breathing (TB) and abdominal breathing (AB) was conducted. The experimental results for both the TB and AB indicate that the proposed method can be used to loosely estimate the activities of thoracic muscles, abdominal muscles, and diaphragm.
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.
Remote patient monitoring should reduce mortality rates, improve care, and reduce costs. We present an overview of the available technologies for the remote monitoring of chronic obstructive pulmonary disease (COPD) patients, together with the most important medical information regarding COPD in a language that is adapted for engineers. Our aim is to bridge the gap between the technical and medical worlds and to facilitate and motivate future research in the field. We also present a justification, motivation, and explanation of how to monitor the most important parameters for COPD patients, together with pointers for the challenges that remain. Additionally, we propose and justify the importance of electrocardiograms (ECGs) and the arterial carbon dioxide partial pressure (PaCO2) as two crucial physiological parameters that have not been used so far to any great extent in the monitoring of COPD patients. We cover four possibilities for the remote monitoring of COPD patients: continuous monitoring during normal daily activities for the prediction and early detection of exacerbations and life-threatening events, monitoring during the home treatment of mild exacerbations, monitoring oxygen therapy applications, and monitoring exercise. We also present and discuss the current approaches to decision support at remote locations and list the normal and pathological values/ranges for all the relevant physiological parameters. The paper concludes with our insights into the future developments and remaining challenges for improvements to continuous remote monitoring systems.
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.
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.
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.
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.
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.
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.
Electrical impedance tomography (EIT) attempts to reveal the conductivity distribution of a domain based on the electrical boundary condition. This is an ill-posed inverse problem; its solution is very unstable. Total variation (TV) regularization is one of the techniques commonly employed to stabilize reconstructions. However, it is well known that TV regularization induces staircase effects, which are not realistic in clinical applications. To reduce such artifacts, modified TV regularization terms considering a higher order differential operator were developed in several previous studies. One of them is called total generalized variation (TGV) regularization. TGV regularization has been successively applied in image processing in a regular grid context. In this study, we adapted TGV regularization to the finite element model (FEM) framework for EIT reconstruction. Reconstructions using simulation and clinical data were performed. First results indicate that, in comparison to TV regularization, TGV regularization promotes more realistic images.
Graphical abstract Reconstructed conductivity changes located on selected vertical lines. For each of the reconstructed images as well as the ground truth image, conductivity changes located along the selected left and right vertical lines are plotted. In these plots, the notation GT in the legend stands for ground truth, TV stands for total variation method, and TGV stands for total generalized variation method. Reconstructed conductivity distributions from the GREIT algorithm are also demonstrated.
People with transtibial amputation often experience skin breakdown due to the pressures and shear stresses that occur at the limb-socket interface. The purpose of this research was to create a transtibial finite element model (FEM) of a contemporary prosthesis that included complete socket geometry, two frictional interactions (limb-liner and liner-socket), and an elastomeric liner. Magnetic resonance imaging scans from three people with characteristic transtibial limb shapes (i.e., short-conical, long-conical, and cylindrical) were acquired and used to develop the models. Each model was evaluated with two loading profiles to identify locations of focused stresses during stance phase. The models identified five locations on the participants’ residual limbs where peak stresses matched locations of mechanically induced skin issues they experienced in the 9 months prior to being scanned. The peak contact pressure across all simulations was 98 kPa and the maximum resultant shear stress was 50 kPa, showing reasonable agreement with interface stress measurements reported in the literature. Future research could take advantage of the developed FEM to assess the influence of changes in limb volume or liner material properties on interface stress distributions.
Chronic scapholunate ligament (SL) injuries are difficult to treat and can lead to wrist dysfunction. Whilst several tendon reconstruction techniques have been employed in the management of SL instability, SL gap reappearance after surgery has been reported. Using a finite element model and cadaveric study data, we investigated the performance of the Corella, scapholunate axis (SLAM) and modified Brunelli tenodesis (MBT) techniques. Scapholunate dorsal and volar gap and angle were obtained following virtual surgery undertaken using each of the three reconstruction methods with the wrist positioned in flexion, extension, ulnar deviation and radial deviation, in addition to the ulnar-deviated clenched fist and neutral positions. From the study, it was found that, following simulated scapholunate interosseous ligament rupture, the Corella technique was better able to restore the SL gap and angle close to the intact ligament for all wrist positions investigated, followed by SLAM and MBT. The results suggest that for the tendon reconstruction techniques, the use of multiple junction points between scaphoid and lunate may be of benefit.
Glioma brain tumors exhibit considerably aggressive behavior leading to high mortality rates. Mathematical modeling of tumor growth aims to explore the interactions between glioma cells and tissue microenvironment, which affect tumor evolution. Leveraging this concept, we present a three-dimensional model of glioma spatio-temporal evolution based on existing continuum approaches, yet incorporating novel factors of the phenomenon. The proposed model involves the interactions between different tumor cell phenotypes and their microenvironment, investigating how tumor growth is affected by complex biological exchanges. It focuses on the separate and combined effect of vital nutrients and cellular wastes on tumor expansion, leading to the formation of cell populations with different metabolic, proliferative, and diffusive profiles. Several simulations were performed on a virtual and a real glioma, using combinations of proliferation and diffusion rates for different evolution times. The model results were validated on a glioma model available in the literature and a real case of tumor progression. The experimental observations indicate that our model estimates quite satisfactorily the expansion of each region and the overall tumor growth. Based on the individual results, the proposed model may provide an important research tool for patient-specific simulation of different tumor evolution scenarios and reliable estimation of glioma evolution.
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.
Vibroarthrography is a radiation-free and inexpensive method of assessing the condition of knee cartilage damage during extension-flexion movements. Acoustic sensors were placed on the patella and medial tibial plateau (two accelerometers) as well as on the lateral tibial plateau (a piezoelectric disk) to measure the structure-borne noise in 59 asymptomatic knees and 40 knees with osteoarthritis. After semi-automatic segmentation of the acoustic signals, frequency features were generated for the extension as well as the flexion phase. We propose simple and robust features based on relative high-frequency components. The normalized nature of these frequency features makes them insusceptible to influences on the signal gain, such as attenuation by fat tissue and variance in acoustic coupling. We analyzed their ability to serve as classification features for detection of knee osteoarthritis, including the effect of normalization and the effect of combining frequency features of all three sensors. The features permitted a distinction between asymptomatic and non-healthy knees. Using machine learning with a linear support vector machine, a classification specificity of approximately 0.8 at a sensitivity of 0.75 could be achieved. This classification performance is comparable to existing diagnostic tests and hence qualifies vibroarthrography as an additional diagnostic tool.
Reconstructing magnetic resonance images from undersampled k-space data is a challenging problem. This paper introduces a novel method of image reconstruction from undersampled k-space data based on the concept of singularizing operators and a novel singular k-space model. Exploring the sparsity of an image in the k-space, the singular k-space model (SKM) is proposed in terms of the k-space functions of a singularizing operator. The singularizing operator is constructed by combining basic difference operators. An algorithm is developed to reliably estimate the model parameters from undersampled k-space data. The estimated parameters are then used to recover the missing k-space data through the model, subsequently achieving high-quality reconstruction of the image using inverse Fourier transform. Experiments on physical phantom and real brain MR images have shown that the proposed SKM method constantly outperforms the popular total variation (TV) and the classical zero-filling (ZF) methods regardless of the undersampling rates, the noise levels, and the image structures. For the same objective quality of the reconstructed images, the proposed method requires much less k-space data than the TV method. The SKM method is an effective method for fast MRI reconstruction from the undersampled k-space data.
