Search

Multi-frequency Electrical Impedance Tomography (mfEIT) is an emerging biomedical imaging modality to reveal frequency-dependent conductivity distributions in biomedical ap-plications. Conventional model-based image reconstruction meth-ods suffer from low spatial resolution, unconstrained frequency correlation and high computational cost. Deep learning has been extensively applied in solving the EIT inverse problem in biomed-ical and industrial process imaging. However, most existing learning-based approaches deal with the single-frequency setup, which is inefficient and ineffective when extended to the multi-frequency setup. This paper presents a Multiple Measurement Vector (MMV) model based learning algorithm named MMV-Net to solve the mfEIT image reconstruction problem. MMV-Net considers the correlations between mfEIT images and unfolds the update steps of the Alternating Direction Method of Multipliers for the MMV problem (MMV-ADMM). The non-linear shrinkage operator associated with the weighted l2,1 regularization term of MMV-ADMM is generalized in MMV-Net with a cascade of a Spatial Self-Attention module and a Convolutional Long Short-Term Memory (ConvLSTM) module to better capture intra- and inter-frequency dependencies. The proposed MMV-Net was validated on our Edinburgh mfEIT Dataset and a series of comprehensive experiments. The results show superior image quality, convergence performance, noise robustness and computational efficiency against the conventional MMV-ADMM and the state-of-the-art deep learning methods.

The purpose of this study is to develop a new polysaccharide-based hydrogel. The Box-Behnken design was used to optimize the optimal synthesis conditions of the hydrogel, with the swelling parameters as indicators. The findings of rheologic tests confirm that free radical polymerization and the introduction of linear polymers improved the mechanical strength of the hydrogel. Combined with the characterization results, the gel mechanism of BSP-g-PAA/PVA DN hydrogel was proposed. The intermolecular association and entanglement increase, which effectively dissipates energy, thereby enhancing the mechanical properties of the hydrogel. In vitro blood compatibility experiments show that DN hydrogel has a low hemolysis rate and a good coagulation effect. The material is non-cytotoxic to L929 cells. The hepatic haemorrhage and mouse-tail amputation models of rats and mice were used to further evaluate the in vivo wound sealing and hemostatic properties of the hydrogel. The blood loss and hemostatic time were significantly lower than those of the control group, indicating that the hydrogel has excellent hemostatic effects. Therefore, the obtained BSP-g-PAA/PVA DN network hydrogel has good comprehensive properties and is expected to be used as a hemostatic material or a precursor of a drug carrier and a tissue engineering scaffold.

Spatially resolved transcriptomics (SRT) has greatly expanded our understanding of the spatial patterns of gene expression in histological tissue sections. However, most currently available platforms could not provide in situ single-cell spatial transcriptomics, limiting their biological applications. Here, to in silico reconstruct SRT at the single-cell resolution, we propose St2cell which combines deep learning-based frameworks with a novel convex quadratic programming (CQP)-based model. St2cell can thoroughly leverage information in high-resolution (HR) histological images, enabling the accurate segmentation of in situ single cells and identification of their transcriptomics. Applying St2cell on various SRT datasets, we demonstrated the reliability of reconstructed transcriptomics. The single-cell resolution provided by our proposed method greatly promoted the detection of elaborate spatial architectures and further facilitated the integration with single-cell RNA-sequencing data. Moreover, in a breast cancer tissue, St2cell identified general spatial structures and co-occurrence patterns of cell types in the tumor microenvironment. St2cell is also computationally efficient and easily accessible, making it a promising tool for SRT studies.

The workflow of prostate cancer diagnosis and grading is cumbersome and the results suffer from substantial inter-observer variability. Recent trials have shown potential in using machine learning to develop automated systems to address this challenge. Most automated deep learning systems for prostate cancer Gleason grading focused on supervised learning requiring demanding fine-grained pixel-level annotations.A weakly-supervised deep learning model with slide-level labels is presented in this study for the diagnosis and grading of prostate cancer with whole slide image (WSI). WSIs are first cropped into small patches and then processed with a deep learning model to extract patch-level features. A graph convolution network (GCN) is used to aggregate the features for classifications. Throughout the training process, the noisy labels are progressively filtered out to reduce inter-observer variations in clinical reports. Finally, multi-center independent test cohorts with 6,174 slides are collected to evaluate the prostate cancer diagnosis and grading performance of our model.The cancer diagnosis (2-level classification) results on two external test sets (n= 4,675, n= 844) show an area under the receiver operating characteristic curve (AUC) of 0.985 and 0.986. The Gleason grading (6-level classification) results reach 0.931 quadratic weighted kappa on the internal test set (n= 531). It generalizes well on the external test dataset (n= 844) with 0.801 quadratic weighted kappa with the reference standard set independently. The model enables pathological meaningful interpretability by visualizing the most attended lesions which are highly consistent with expert annotations.The proposed model incorporates a graph network in weakly supervised learning with only slide-level reports. A robust learning strategy is also employed to correct the label noise. It is highly accurate (0.985 AUC for diagnosis) and also interpretable with intuitive heatmap visualization. It can be unified with a digital pathology pipeline to deliver prostate cancer metrics for a pathology report.

Hydrogels are a fascinating class of materials popular in numerous fields, including tissue engineering, drug delivery, soft robotics, and sensors, thanks to their 3D network porous structure containing a significant amount of water. However, traditional hydrogels exhibit poor mechanical strength, limiting their practical applications. Thus, many researchers have focused on the development of mechanically enhanced hydrogels. This review describes the design considerations for constructing tough hydrogels and some of the latest strategies in recent years. These tough hydrogels have an up-and-coming prospect and bring great hope to the fields of biomedicine and others. Nonetheless, it is still no small challenge to realize hydrogel materials that are tough, multifunctional, intelligent, and with zero defects.

Imaging the bio-impedance distribution of a biological sample can provide understandings about the sample's electrical properties which is an important indicator of physiological status. This paper presents a multi-frequency electromagnetic tomography (mfEMT) technique for biomedical imaging. The system consists of 8 channels of gradiometer coils with adjustable sensitivity and excitation frequency. To exploit the frequency correlation among each measurement, we reconstruct multiple frequency data simultaneously based on the Multiple Measurement Vector (MMV) model. The MMV problem is solved by using a sparse Bayesian learning method that is especially effective for sparse distribution. Both simulations and experiments have been conducted to verify the performance of the method. Results show that by taking advantage of multiple measurements, the proposed method is more robust to noisy data for ill-posed problems compared to the commonly used single measurement vector model., Comment: This is an accepted paper which has been submitted to I2MTC 2020 on Nov. 2019

Inverse problems are essential to imaging applications. In this paper, we propose a model-based deep learning network, named FISTA-Net, by combining the merits of interpretability and generality of the model-based Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) and strong regularization and tuning-free advantages of the data-driven neural network. By unfolding the FISTA into a deep network, the architecture of FISTA-Net consists of multiple gradient descent, proximal mapping, and momentum modules in cascade. Different from FISTA, the gradient matrix in FISTA-Net can be updated during iteration and a proximal operator network is developed for nonlinear thresholding which can be learned through end-to-end training. Key parameters of FISTA-Net including the gradient step size, thresholding value and momentum scalar are tuning-free and learned from training data rather than hand-crafted. We further impose positive and monotonous constraints on these parameters to ensure they converge properly. The experimental results, evaluated both visually and quantitatively, show that the FISTA-Net can optimize parameters for different imaging tasks, i.e. Electromagnetic Tomography (EMT) and X-ray Computational Tomography (X-ray CT). It outperforms the state-of-the-art model-based and deep learning methods and exhibits good generalization ability over other competitive learning-based approaches under different noise levels., Comment: 11 pages

In order to detect the QRS complexes locally in the microcontroller-based embedded system, a novel algorithm with lower computation burden is proposed by phase space reconstruction and box-scoring calculation. The method depends on the geometrical property of the ECG signal phase portraits. According to the time-delay method, one segment of the sampled 1-D ECG signal is embedded into a 2-D phase space. The phase space is divided into $2^{M}\times 2^{M}$ grids, and each grid is called a box. The box gridding is implemented by numerically truncating the high $M$ -bits from the digital data. The phase portraits of the sampled ECG signal in the phase space are therefore divided by these boxes. Since the trajectory points corresponding to the R-waves are located relatively far away from the diagonal line, boxes that crossed by the trajectory are flagged and valued according to its distance to the diagonal line. Correspondingly, the box-scoring of the ECG segment is obtained by summarizing the values of the flagged boxes. The location of the R-waves can then be found by finding peak values in the box-scoring time series. The proposed algorithm is suitable for real-time QRS complex detection in microcontroller-based wearable ECG acquisition devices.

The hydrogel wound dressing is an ideal wound treatment because of its excellent biocompatibility, high moisture resistance and the ability to activate immune cells to speed wound healing. However, the biomedical applications of hydrogels are limited currently, due to their poor mechanical properties and insufficient adhesion. In order to expand the scope of its application, people have explored new methods of chemical and physical cross-linking, designed new composite hydrogels, and introduced effective energy dissipation mechanisms. In this review, we also discuss materials used to produce hydrogels, focused on TCM-based hydrogel materials with bioactive properties that promote healing. Moreover, methods for evaluating hydrogel wound dressings are discussed to guide the preparation of new hydrogels.

In this paper, the human body communication (HBC) and level crossing sampling (LCS) are combined to design electronics for a wearable electrocardiograph (ECG). The ECG signals acquired by capacitively coupled electrodes are sampled with LCS in place of conventional synchronous sampling. In order to transmit signals through HBC at low frequencies (100 kHz, 1 MHz), an electric field sensor with high input impedance is adopted as the front end of the HBC receiver. The HBC channel gain is enhanced by more than 30 dB with the electric field sensor. An LCS structure based on the send-on-delta concept is implemented with discrete components to convert the ECG signals into binary impulses. The converted impulses are modulated by an on–off keying modulator and then transmitted via the human body to the receiver. A prototype ECG waist belt is developed with commercially available components and experimentally evaluated. The results indicate that the acquired ECG waveforms exhibit good agreement with regular Ag/AgCl ECG methods. The heartbeat detection using a technique based on the Kadane's algorithm and the power consumption performance of the proposed system are also discussed.

This paper focuses on the development and experimental test of a coil gradiometer sensor and the measurement circuit. The coil gradiometer, which is characterized as simple, flexible and low-cost, consists of one excitation coil and two sensing coils which are symmetrically arranged. The differential configuration of the sensor enhances signal conditioning and suppresses common-mode interferences. The measurement circuit is based on AD8302, an integrated chip for gain and phase detection. To verify the sensitivity measurement range of the gradiometer for magnetic sensing, experiments were carried out for saline solution concentration of 0%-10% and metallic plate displacement. These preliminary experiments demonstrate that the proposed coil gradiometer sensor has wide-ranging applications as a non-contact and non-intrusive method.

A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.