Microwave-induced thermoacoustic imaging (M-TAI) allows the visualization of macroscopic and microscopic structures of bio-tissues. However, it suffers from severe inherent artifacts that might misguide the subsequent diagnostics and treatments of diseases. To overcome this limitation, we propose an optimized excitation strategy. In detail, the strategy integrates dynamically compound specific absorption rate (SAR) and co-planar configuration of polarization state, incident wave vector and imaging plane. Starting from the theoretical analysis, we interpret the underlying mechanism supporting the superiority of the optimized excitation strategy to achieve an effect equivalent to homogenizing the deposited electromagnetic energy in bio-tissues. The following numerical simulations demonstrate that the strategy enables better preservation of the conductivity weighting of samples while increasing Pearson correlation coefficient. Furthermore, the in vitro and in vivo M-TAI experiments validate the effectiveness and robustness of this optimized excitation strategy in artifact suppression, allowing the simultaneous identification of both boundary and inside fine structures within bio-tissues. All the results suggest that the optimized excitation strategy can be expanded to diverse scenarios, inspiring more suitable strategies that remarkably suppress the inherent artifacts in M-TAI.

Our previous research introduced a novel Flat-ted Grating Folded Waveguide (FGFW) slow wave structure (SWS) for G-band traveling wave tubes (TWT). Compared to conventional folded waveguide (FW), FGFW demonstrates better EM characteristics. To further enhance performance, this paper proposes the ridge-enhanced flat-ted grating folded waveguide (RE-FGFW), which significantly increases interaction impedance and saturated output power. The RE-FGFW demonstrates superior interaction impedance and amplification performance in TWT applications compared to both the FGFW and FW.

The gridless inductive output tube (IOT) is a versatile variant of the conventional IOT. It primarily offers high-power operation in the MW-range, and a compact, flexible design. The electron beam is density-modulated at the cathode to achieve bunching with a non-intercepting anode. This paper presents an analysis on the emission gating of a sheet electron beam designed for a high-power gridless IOT. Here, the focusing electrode is utilized for this modulation by applying a potential difference, relative to the cathode. The field intensity required for emission is calculated using a modified Fowler-Norheim equation, which accounts for the transition into the space-charge limited regime, and compensates for the geometric effects of the emission surface. The beam voltage is 80 kV, beam current is 36.5A, and perveance is$1.6 \times 10^{-6}\mathrm{A}/\mathrm{V}^{3/2}$. The beam is confined with a magnetic field of 0.7 T. The compressed beam dimensions are$2.54\ \text{mm} \times 37.6\ \text{mm}$. The simulated current density at the cathode is 20.27 A/ cm2with a peak electric field of the order of$10^{6}\mathrm{V}/\mathrm{m}$. The simulation results show agreement with the Fowler-Nordheim model for the electric field intensity required to operate this beam.

Microwave-induced thermoacoustic imaging (MTAI) using low-energy and long-wavelength microwave photons has great potential in detecting deep-seated diseases due to its unique ability of visualizing intrinsic electric properties of tissue in high resolution. However, the low contrast in conductivity between a target (e.g., a tumor) and the surroundings sets a fundamental limit for achieving a high imaging sensitivity, which significantly hinders its biomedical applications. To overcome this limit, we develop a split ring resonator (SRR) topology based MTAI (SRR-MTAI) approach to achieve highly sensitive detection by precise manipulation and efficient delivery of microwave energy. The in vitro experiments show that SRR-MTAI demonstrates an ultrahigh sensitivity of distinguishing a 0.4% difference in saline concentrations and a 2.5-fold enhancement of detecting a tissue target which mimicks a tumor embedded at a depth of 2 cm. The in vivo animal experiments conducted indicate that the imaging sensitivity between a tumor and the surrounding tissue is increased by 3.3-fold using SRR-MTAI. The dramatic enhancement in imaging sensitivity suggests that SRR-MTAI has the potential to open new avenues for MTAI to tackle a variety of biomedical problems that were impossible previously.

Coronary calcification is a strong indicator of coronary artery disease and a key determinant of the outcome of percutaneous coronary intervention. We propose a fully automated method to segment and quantify coronary calcification in intravascular OCT (IVOCT) images based on convolutional neural networks (CNN). All possible calcified plaques were segmented from IVOCT pullbacks using a spatial-temporal encoder-decoder network by exploiting the 3D continuity information of the plaques, which were then screened and classified by a DenseNet network to reduce false positives. A novel data augmentation method based on the IVOCT image acquisition pattern was also proposed to improve the performance and robustness of the segmentation. Clinically relevant metrics including calcification area, depth, angle, thickness, volume, and stent-deployment calcification score, were automatically computed. 13844 IVOCT images with 2627 calcification slices from 45 clinical OCT pullbacks were collected and used to train and test the model. The proposed method performed significantly better than existing state-of-the-art 2D and 3D CNN methods. The data augmentation method improved the Dice similarity coefficient for calcification segmentation from 0.615±0.332 to 0.756±0.222, reaching human-level inter-observer agreement. Our proposed region-based classifier improved image-level calcification classification precision and F1-score from 0.725±0.071 and 0.791±0.041 to 0.964±0.002 and 0.883±0.008, respectively. Bland-Altman analysis showed close agreement between manual and automatic calcification measurements. Our proposed method is valuable for automated assessment of coronary calcification lesions and in-procedure planning of stent deployment.

Cataract causes more than half of all blindness worldwide. The most effective treatment is surgery, where cataract is often replaced by intraocular lens (IOL). Beyond saving vision, toric IOL implantation is becoming increasingly popular to correct corneal astigmatism. It is important to precisely position and align the axis of IOL during surgery to achieve optimal post-operative astigmatism correction. Comparing with conventional manual marking, automated markerless IOL alignment can be faster, more accurate and non-invasive. Here we propose a framework for computer-assisted intraoperative IOL positioning and alignment based on detection and tracking. Firstly, the iris boundary was segmented and the eye center was determined. A statistical sampling method was developed to segment iris and generate training labels, and both conventional algorithms and deep convolutional neural network (CNN) methods were evaluated. Then, regions of interests (ROIs) containing high density of scleral capillaries were used for tracking eye rotations. Both correlation filter and CNN methods were evaluated for tracking. Cumulative errors during long-term tracking were corrected using a reference image. Validation studies against manual labeling using 7 clinical cataract surgical videos demonstrated that the proposed algorithm achieved an average position error around 0.2 mm, an axis alignment error of$^{\circ}$, and a frame rate of > 25 FPS, and can be potentially used intraoperatively for markerless IOL positioning and alignment during cataract surgery.

An efficient neural network based learning control scheme is proposed to solve the trajectory tracking controI problem of robot manipulators. The proposed approach has four distinctive characteristics: 1) good tracking performance can be achieved during the first learning trial; 2) learning algorithm for adjusting neural network weights is independent of the manipulator dynamic model, thus displays strong robustness to torque disturbances and model parameter uncertainty; 3) no acceleration measurement or estimation is needed; and 4) real-time implementation with a higher sampling rate is readily possible. Simulation results on a 3 degree-of-freedom manipulator are presented to show its validity.