Continuous glucose monitoring (CGM) by suitable portable sensors plays a central role in the treatment of diabetes, a disease currently affecting more than 350 million people worldwide. Noninvasive CGM (NI-CGM), in particular, is appealing for reasons related to patient comfort (no needles are used) but challenging. NI-CGM prototypes exploiting multisensor approaches have been recently proposed to deal with physiological and environmental disturbances. In these prototypes, signals measured noninvasively (e.g., skin impedance, temperature, optical skin properties, etc.) are combined through a static multivariate linear model for estimating glucose levels. In this work, by exploiting a dataset of 45 experimental sessions acquired in diabetic subjects, we show that regularisation-based techniques for the identification of the model, such as the least absolute shrinkage and selection operator (better known as LASSO), Ridge regression, and Elastic-Net regression, improve the accuracy of glucose estimates with respect to techniques, such as partial least squares regression, previously used in the literature. More specifically, the Elastic-Net model (i.e., the model identified using a combination of $l_1$ and $l_2$ norms) has the best results, according to the metrics widely accepted in the diabetes community. This model represents an important incremental step toward the development of NI-CGM devices effectively usable by patients.

One of the fields of applied mathematics is related to model analysis. Biomedical systems are suitable candidates for this field because of their importance in life sciences including therapeutics. Here we deal with the analysis of a model recently proposed by Espinoza-Valdez et al. (2010) for the kidney vasculature developed via angiogenesis. The graph theory allows one to model quantitatively a vascular arterial tree of the kidney in sense that (1) the vertex represents a vessels bifurcation, whereas (2) each edge stands for a vessel including physiological parameters. The analytical model is based on the two processes of sprouting and splitting angiogeneses, the concentration of the vascular endothelial growth factor (VEGF), and the experimental data measurements of the rat kidneys. The fractal dimension depends on the probability of sprouting angiogenesis in the development of the arterial vascular tree of the kidney, that is, of the distribution of blood vessels in the morphology generated by the analytical model. The fractal dimension might determine whether a suitable renal vascular structure is capable of performing physiological functions under appropriate conditions. The analysis can describe the complex structures of the development vasculature in kidney.

Histological images from the longitudinal section of four diseased coronary arteries were used, for the first time, to study the pulsatile blood flow distribution within the lumen of the arteries by means of computational fluid dynamics (CFD). Results indicate a strong dependence of the hemodynamics on the morphology of atherosclerotic lesion. Distinctive flow patterns appear in different stenosed regions corresponding to the specific geometry of any artery. Results show that the stenosis affects the wall shear stress (WSS) locally along the diseased arterial wall as well as other adjacent walls. The maximum magnitude of WSS is observed in the throat of stenosis. Moreover, high oscillatory shear index (OSI) is observed along the stenosed wall and the high curvature regions. The present study is capable of providing information on the shear environment in the longitudinal section of the diseased coronary arteries, based on the models created from histological images. The computational method may be used as an effective way to predict plaque forming regions in healthy arterial walls.

This paper illustrates the application of the discrete wavelet transform (DWT) for wandering and noise suppression in electrocardiographic (ECG) signals. A novel one-step implementation is presented, which allows improving the overall denoising process. In addition an exhaustive study is carried out, defining threshold limits and thresholding rules for optimal wavelet denoising using this presented technique. The system has been tested using synthetic ECG signals, which allow accurately measuring the effect of the proposed processing. Moreover, results from real abdominal ECG signals acquired from pregnant women are presented in order to validate the presented approach.

Artifactual sources of resting-state (RS) FMRI can originate from head motion, physiology, and hardware. Of these sources, motion has received considerable attention and was found to induce corrupting effects by differentially biasing correlations between regions depending on their distance. Numerous corrective approaches have relied on the identification and censoring of high-motion time points and the use of the brain-wide average time series as a nuisance regressor to which the data are orthogonalized (Global Signal Regression, GSReg). We replicate the previously reported head-motion bias on correlation coefficients and then show that while motion can be the source of artifact in correlations, the distance-dependent bias is exacerbated by GSReg. Put differently, correlation estimates obtained after GSReg are more susceptible to the presence of motion and by extension to the levels of censoring. More generally, the effect of motion on correlation estimates depends on the preprocessing steps leading to the correlation estimate, with certain approaches performing markedly worse than others. For this purpose, we consider various models for RS FMRI preprocessing and show that the local white matter regressor (WMe_LOCAL), a subset of ANATICOR, results in minimal sensitivity to motion and reduces by extension the dependence of correlation results on censoring.

In recent years, bone graft substitutes have been increasingly used in the medical field, for example, in order to promote new bone formation. Microcomputed tomography (μ-CT) is an image-guided technique used in medicine as well as in materials science, enabling the characterization of biomaterials with high spatial resolution. X-ray-based methods provide density information; however, the question how far conclusions on chemical structures can be inferred from any kind of CT information has not been intensively investigated yet. In the present study, a bone sample consisting of autogenous bone derived cells (ABCs) and bovine bone mineral (BBM) was investigated by μ-CT and Raman spectroscopic imaging, that is, by two nondestructive imaging methods. Thereby, the image data were compared by means of regression analysis and digital image processing methods. It could be found that 51.8% of the variance of gray level intensities, as a result of μ-CT, can be described by different Raman spectra of particular interest for bone composition studies by means of a multiple linear regression. With the better description of μ-CT images by the linear model, a better distinction of different bone components is possible. Therefore, the method shown can be applied to improve CT-image-based bone modeling in the future.

We propose filtering the PET sinograms with a constraint curvature motion diffusion. The edge-stopping function is computed in terms of edge probability under the assumption of contamination by Poisson noise. We show that the Chi-square is the appropriate prior for finding the edge probability in the sinogram noise-free gradient. Since the sinogram noise is uncorrelated and follows a Poisson distribution, we then propose an adaptive probabilistic diffusivity function where the edge probability is computed at each pixel. The filter is applied on the 2D sinogram prereconstruction. The PET images are reconstructed using the Ordered Subset Expectation Maximization (OSEM). We demonstrate through simulations with images contaminated by Poisson noise that the performance of the proposed method substantially surpasses that of recently published methods, both visually and in terms of statistical measures.

Diffuse optical tomogrpahy (DOT) is to find optical coefficients of tissue using near infrared light. DOT as an inverse problem is described and the studies about unique determination of optical coefficients are summarized. If a priori information of the optical coefficient is known, DOT is reformulated to find a perturbation of the optical coefficients inverting the Born expansion which is an infinite series expansion with respect to the perturbation and the a priori information. Numerical methods for DOT are explained as methods inverting first- or second-order Born approximation or the Born expansion itself.

Corticomuscular activity modeling based on multiple data sets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding human motor control systems. In this paper, we propose modeling corticomuscular activity by combining partial least squares (PLS) and canonical correlation analysis (CCA). The proposed method takes advantage of both PLS and CCA to ensure that the extracted components are maximally correlated across two data sets and meanwhile can well explain the information within each data set. This complementary combination generalizes the statistical assumptions beyond both PLS and CCA methods. Simulations were performed to illustrate the performance of the proposed method. We also applied the proposed method to concurrent EEG and EMG data collected in a Parkinson’s disease (PD) study. The results reveal several highly correlated temporal patterns between EEG and EMG signals and indicate meaningful corresponding spatial activation patterns. In PD subjects, enhanced connections between occipital region and other regions are noted, which is consistent with previous medical knowledge. The proposed framework is a promising technique for performing multisubject and bimodal data analysis.

Osteoporosis is a disease in which low bone mass and microarchitectural deterioration of bone tissue lead to enhanced bone fragility and susceptibility to fracture. Due to the complex anatomy of the vertebral body, the difficulties associated with obtaining bones for in vitro experiments, and the limitations on the control of the experimental parameters, finite element models have been developed to analyze the biomechanical properties of the vertebral body. We developed finite element models of the L2 vertebra, which consisted of the endplates, the trabecular lattice, and the cortical shell, for three age-related grades (young, middle, and old) of osteoporosis. The compressive strength and stiffness results revealed that we had developed a valid model that was consistent with the results of previous experimental and computational studies. The von-Mises stress, which was assumed to predict the risk of a burst fracture, was also determined for the three age groups. The results showed that the von-Mises stress was substantially higher under relatively high levels of compressive loading, which suggests that patients with osteoporosis should be cautious of fracture risk even during daily activities.

In this study, we establish a quantitative model to define the stretching of brain tissue, especially in ventricular walls, corpus callosum (CC) and corticospinal (CS) fiber tracts, and to investigate the correlation between stretching and regional cerebral blood flow (rCBF) before and after ventriculoperitoneal shunt operations. A nonlinear image registration method was used to calculate the degree of displacement and stretching of axonal fiber tracts based on the medical images of six hydrocephalus patients. Also, the rCBF data from the literature was analyzed and correlated with the strain level quantified in the present study. The results showed substantial increased displacement and strain levels in the ventricular walls as well as in the CC and CS fiber tracts on admission. Following shunt operations the displacement as well as the strain levels reduced substantially. A linear correlation was found to exist between strain level and the rCBF. The reduction in postoperative strain levels correlated with the improvement of rCBF. All patients improved clinically except for one patient due to existing dementia. These new quantitative data provide us with new insight into the mechanical cascade of events due to tissue stretching, thereby provide us with more knowledge into understanding of the role of brain tissue and axonal stretching in some of the hydrocephalus clinical symptoms.

This paper proposes a three-dimensional mathematical model of the biomechanical interactions between backpack and bearer during load carriage. The model considers both the coupled pack motions, which follow the torso, and also the longitudinal compliance and damping in the backpack suspension. The pack interaction forces and moments, acting on the bearer, are determined from kinematic relationships, equations of motion, and a dynamic pack suspension model. The parameters of the pack suspension model were identified from test data obtained using a load carriage test rig. Output from the load carriage mathematical model has been compared with measurement data during human gait and conclusions drawn with regard to the validity of the proposed approach.

We know that three system parameters, a center of X-ray source, an isocenter, and a center of linear detectors, are very difficult to be calibrated in industrial CT system. So there are often the offset of an isocenter and the deflection of linear detectors. When still using the FBP (filtered backprojection) algorithm under this condition, CT image artifacts will happen and then can seriously affect test results. In this paper, we give the appearances and forming mechanism of these artifacts and propose the reconstruction algorithm including a deflection angle of linear detectors. The numerical experiments with simulated data have validated that our propose algorithm can correct CT images artifacts without data rebinning.