electrical impedance tomography (EIT) is a promising technique for rapid and continuous bedside monitoring of lung function. Accurate and reliable EIT reconstruction of ventilation requires patient-specific shape information. However, this shape information is often not available and current EIT reconstruction methods typically have limited spatial fidelity. This study sought to develop a statistical shape model (SSM) of the torso and lungs and evaluate whether patient-specific predictions of torso and lung shape could enhance EIT reconstructions in a Bayesian framework. torso and lung finite element surface meshes were fitted to computed tomography data from 81 participants, and a SSM was generated using principal component analysis and regression analyses. Predicted shapes were implemented in a Bayesian EIT framework and were quantitatively compared to generic reconstruction methods. Five principal shape modes explained 38% of the cohort variance in lung and torso geometry, and regression analysis yielded nine total anthropometrics and pulmonary function metrics that significantly predicted these shape modes. Incorporation of SSM-derived structural information enhanced the accuracy and reliability of the EIT reconstruction as compared to generic reconstructions, demonstrated by reduced relative error, total variation, and Mahalanobis distance. As compared to deterministic approaches, Bayesian EIT afforded more reliable quantitative and visual interpretation of the reconstructed ventilation distribution. However, no conclusive improvement of reconstruction performance using patient specific structural information was observed as compared to the mean shape of the SSM. The presented Bayesian framework builds towards a more accurate and reliable method for ventilation monitoring via EIT.

High-quality gaze signals are crucial in many biomedical fields that utilize them. However, the limited studies on gaze signal filtering can hardly address the outliers and non-Gaussian noise in gaze data simultaneously. Our objective is to design a generic filtering framework capable of reducing the noise and eliminating outliers of the gaze signal. In this study, we design an eye-movement modality-based zonotope set-membership filtering framework (EM-ZSMF) to suppress the noise and outliers of the gaze signal. This framework consists of an eye-movement modality recognition model (EG-NET), an eye-movement modality-based gaze movement model (EMGM), and a zonotope set-membership filter (ZSMF). The eye-movement modality determines the EMGM, and the ZSMF combined with the EMGM completes the filtering of the gaze signal. Moreover, this study establishes an eye-movement modality and gaze filtering dataset (ERGF) that can be utilized for the evaluation of future work integrating eye-movement modality with gaze signal filtering. The eye-movement modality recognition experiments demonstrated that our proposed EG-NET achieved the best Cohen's kappa compared with previous studies. The gaze data filtering experiments showed that the proposed EM-ZSMF reduced the gaze signal noise and eliminated outliers effectively, and achieved the best performance (RMSEs and RMS) compared with previous methods. The proposed EM-ZSMF effectively recognizes eye-movement modalities, reduces gaze signal noise and, eliminates outliers. To the best of the authors' knowledge, this is the first attempt to simultaneously solve the problem of non-Gaussian noise and outliers in gaze signals. The proposed framework has the potential for application in any eye image-based eye trackers and contributes to the development of eye-tracking technology.

Innovations in complementary metal-oxide semiconductor (CMOS) single-photon avalanche diode (SPAD) technology has featured in the development of next-generation instruments for point-based time-resolved fluorescence spectroscopy (TRFS). These instruments provide hundreds of spectral channels, allowing the collection of fluorescence intensity and fluorescence lifetime information over a broad spectral range at a high spectral and temporal resolution. We present Multichannel Fluorescence Lifetime Estimation, MuFLE, an efficient computational approach to exploit the unique multi-channel spectroscopy data with an emphasis on simultaneous estimation of the emission spectra, and the respective spectral fluorescence lifetimes. In addition, we show that this approach can estimate the individual spectral characteristics of fluorophores from a mixed sample.

Overuse musculoskeletal injuries, often precipitated by walking or running with heavy loads, are the leading cause of lost-duty days or discharge during basic combat training (BCT) in the U.S. military. The present study investigates the impact of stature and load carriage on the running biomechanics of men during BCT. We collected computed tomography images and motion-capture data for 21 young, healthy men of short, medium, and tall stature (n = 7 in each group) running with no load, an 11.3-kg load, and a 22.7-kg load. We then developed individualized musculoskeletal finite-element models to determine the running biomechanics for each participant under each condition, and used a probabilistic model to estimate the risk of tibial stress fracture during a 10-week BCT regimen. Under all load conditions, we found that the running biomechanics were not significantly different among the three stature groups. However, compared to no load, a 22.7-kg load significantly decreased the stride length, while significantly increasing the joint forces and moments at the lower extremities, as well as the tibial strain and stress-fracture risk. Load carriage but not stature significantly affected the running biomechanics of healthy men. We expect that the quantitative analysis reported here may help guide training regimens and reduce the risk of stress fracture.

Hemorrhagic stroke is a leading threat to human's health. The fast-developing microwave-induced thermoacoustic tomography (MITAT) technique holds potential to do brain imaging. However, transcranial brain imaging based on MITAT is still challenging due to the involved huge heterogeneity in speed of sound and acoustic attenuation of human skull. This work aims to address the adverse effect of the acoustic heterogeneity using a deep-learning-based MITAT (DL-MITAT) approach for transcranial brain hemorrhage detection. We establish a new network structure, a residual attention U-Net (ResAttU-Net), for the proposed DL-MITAT technique, which exhibits improved performance as compared to some traditionally used networks. We use simulation method to build training sets and take images obtained by traditional imaging algorithms as the input of the network. We present ex-vivo transcranial brain hemorrhage detection as a proof-of-concept validation. By using an 8.1-mm thick bovine skull and porcine brain tissues to perform ex-vivo experiments, we demonstrate that the trained ResAttU-Net is capable of efficiently eliminating image artifacts and accurately restoring the hemorrhage spot. It is proved that the DL-MITAT method can reliably suppress false positive rate and detect a hemorrhage spot as small as 3 mm. We also study effects of several factors of the DL-MITAT technique to further reveal its robustness and limitations. The proposed ResAttU-Net-based DL-MITAT method is promising for mitigating the acoustic inhomogeneity issue and performing transcranial brain hemorrhage detection. This work provides a novel ResAttU-Net-based DL-MITAT paradigm and paves a compelling route for transcranial brain hemorrhage detection as well as other transcranial brain imaging applications.

We present the first wearable sensor designed for frequent monitoring of muscle atrophy and validate performance upon canonical phantoms. Our approach relies on Faraday's law of induction and exploits the dependence of magnetic flux density on cross-sectional area. We employ wrap-around transmit and receive coils that stretch to fit changing limb sizes using conductive threads (e-threads) in a novel zig zag pattern. Changes in the loop size result in changes in the magnitude and phase of the transmission coefficient between loops. Simulation and in vitro measurement results are in excellent agreement. As a proof-of-concept, a cylindrical calf model for an average-sized subject is considered. The frequency of 60 MHz is selected via simulation for optimal limb size resolution in magnitude and phase while remaining in the inductive mode of operation. We can monitor muscle volume loss of up to 51%, with an approximate resolution of 0.17 dB and 1.58° per 1% volume loss. In terms of muscle circumference, we achieve resolution of 0.75 dB and 6.7° per centimeter. Thus, we can monitor small-scale changes in overall limb size. This is the first known approach for monitoring muscle atrophy with a sensor designed to be worn. Additionally, this work brings forward innovations in creating stretchable electronics from e-threads (as opposed to inks, liquid metal, or polymer). The proposed sensor will provide improved monitoring for patients suffering from muscle atrophy. The stretching mechanism can be seamlessly integrated into garments which creates unprecedented opportunities for future wearable devices.