Respiratory motion tracking of the thoracoabdominal surface based on defect-aware point cloud registration.

The performance of conventional lung puncture surgery is a complex undertaking due to the surgeon's reliance on visual assessment of respiratory conditions and the manual execution of the technique while the patient maintains breath-holding. However, the failure to correctly perform a puncture technique can lead to negative outcomes, such as the development of sores and pneumothorax. In this work, we proposed a novel approach for monitoring respiratory motion by utilizing defect-aware point cloud registration and descriptor computation. Through a thorough examination of the attributes of the inputs, we suggest the incorporation of a defect detection branch into the registration network. Additionally, we developed two modules with the aim of augmenting the quality of the extracted features. A coarse-to-fine respiratory phase recognition approach based on descriptor computation is devised for the respiratory motion tracking. The efficacy of the suggested registration method is demonstrated through experimental findings conducted on both publicly accessible datasets and thoracoabdominal point cloud datasets. We obtained state-of-the-art registration results on ModelNet40 datasets, with 1.584∘ on rotation mean absolute error and 0.016 mm on translation mean absolute error, respectively. The experimental findings conducted on a thoracoabdominal point cloud dataset indicate that our method exhibits efficacy and efficiency, achieving a frame matching rate of 2 frames per second and a phase recognition accuracy of 96.3%. This allows identifying matching frames from template point clouds that display different parts of a patient's thoracoabdominal surface while breathing regularly to distinguish breathing stages and track breathing.

Robust Cost Volume Generation Method for Dense Stereo Matching in Endoscopic Scenarios.

Stereo matching in binocular endoscopic scenarios is difficult due to the radiometric distortion caused by restricted light conditions. Traditional matching algorithms suffer from poor performance in challenging areas, while deep learning ones are limited by their generalizability and complexity. We introduce a non-deep learning cost volume generation method whose performance is close to a deep learning algorithm, but with far less computation. To deal with the radiometric distortion problem, the initial cost volume is constructed using two radiometric invariant cost metrics, the histogram of gradient angle and amplitude descriptors. Then we propose a new cross-scale propagation framework to improve the matching reliability in small homogenous regions without increasing the running time. The experimental results on the Middlebury Version 3 Benchmark show that the performance of the combination of our method and Local-Expansion, an optimization algorithm, ranks top among non-deep learning algorithms. Other quantitative experimental results on a surgical endoscopic dataset and our binocular endoscope show that the accuracy of the proposed algorithm is at the millimeter level which is comparable to the accuracy of deep learning algorithms. In addition, our method is 65 times faster than its deep learning counterpart in terms of cost volume generation.

Quasi-pixelwise motion compensation for 4-step phase-shifting profilometry based on a phase error estimation.

Phase-shifting profilometry (PSP) is widely used in 3D shape measurement due to its high accuracy. However, in dynamic scenarios, the motion of objects will introduce phase-shifting errors and result in measurement errors. In this paper, a novel compensation method based on 4-step phase-shifting profilometry is proposed to reduce motion-induced errors when objects undergo uniform or uniformly accelerated motion. We utilize the periodic characteristic of fringe patterns to estimate the phase errors from only four phase-shifting patterns and realize a pixel-wise error compensation. This method can also be applied to non-rigid deforming objects and help restore high-quality texture. Both simulation and experiments demonstrate that the proposed method can effectively improve the measurement accuracy and reduce surface ripples introduced by motion for a standard monocular structured-light system.

High-quality binary fringe generation by multi-scale optimization on intensity and phase.

The optimization-based binary fringe generation method can greatly improve the quality of projected fringes for structured light measurement. However, the existing phase-based and intensity-based methods are either sensitive to the projector defocus levels or cannot reduce the phase error efficiently, and these two methods only optimize the phase or intensity at a single defocus scale. A new method is proposed to optimize the intensity and phase of binary fringe on multiple defocus scales, to the best of our knowledge, which can effectively reduce the phase error and ensure its robustness. The algorithm is accelerated by block optimization. We also proposed a strategy based on objective index evaluation to simplify the process of selecting the best binary patch. Simulation and experiment results show that the binary fringe generated by the proposed method is of high-phasing quality while keeping robust to different defocus levels of the projector.

A Deep Brain Stimulation System with Low Power Consumption and Wide Output Range.

Deep brain stimulation (DBS) therapy has been widely used in clinical practice for the treatment of neurological diseases and has achieved significant therapeutic effect. In this paper, aiming at the social problem of drug addiction, we design an electrical stimulation system which can be used in animal experiments, carry out the memory extinction experiment of addiction in rats, and explore the effective electrical stimulation parameters. The DBS system consists of a rechargeable battery and a PCB stimulation circuit composed of discrete devices. In animal experiments, the power consumption of the circuit is 0.36mW in the electrical stimulation stage. Theoretically, the circuit can work continuously for more than 100 days with a 3.7V 250mAh lithium battery. The stimulation circuit is highly programmable and the output stimulation current ranges from 100µA to 5000µA with a 20µA current resolution.

An ASK Data Demodulator Circuit for Implantable Medical Devices Supporting a Minimum Modulation Depth of 0.034.

Amplitude shift keying (ASK) data demodulation method has been widely used for simultaneous wireless data and power transfer in implantable medical devices (IMDs). Small amplitude modulation depth (MD) is usually preferred as it helps promote energy harvesting efficiency. This paper presents an ASK data demodulator that has good immunity to disturbances and can demodulate ultra-low MD ASK signal. A three-stage amplifying structure (3SAS) is proposed, in which the common-mode level of each amplifier is set between the high and low levels of its input signal envelope to prevent amplifier gain saturation and maximize the amplification of the envelope difference. Two envelope detectors (EDs) are used before and after the 3SAS respectively. The first one is to obtain a coarse envelope for 3SAS input and the second one is to further suppress the residual carrier interference and get a fine envelope. The proposed demodulator is implemented in 0.18-µm high-voltage Bipolar-CMOS-DMOS (BCD) technology. The detectable MD is measured as low as 0.034%, showing that the proposed demodulator can work smoothly and robustly in some extreme cases of simultaneous data and power transferring.Clinical Relevance- The ASK data demodulator proposed in this paper supports ultra-low modulation depth. This reduces the bit error rate of the data link and keeps a highly power conversion efficiency for wireless power and data transfer in implantable medical devices.

An Energy-Efficient Implantable-Neural-Stimulator System with Wireless Charging and Dynamic Voltage Output.

Neural stimulators have become more and more widely used as an effective tool in neural therapies. To address power supply and consumption issues in this application, an energy-efficient Implantable-Neural-Stimulator system composed of a pulse generator and a wireless charger is proposed and implemented in 0.8µm 40V Bipolar-CMOS-DMOS (BCD) process. By adopting a Single Ended Primary Inductor Converter (SEPIC) and optimizing the switching frequency and the gate width of its power MOSFET, the stimulating output voltage range can cover 0~12V with a maximum output ripple of 0.31%. The proposed charger can charge the implantable battery wirelessly by an inductively coupled resonance circuit. In addition, it can adjust the charging voltage to keep it constantly only a little higher than the battery voltage, which reduces the charging headroom voltage and greatly improves the charging efficiency. The measured maximum power efficiencies of these two modules reach as high as 78.04% and 70.67%, respectively.

A wirelessly programmable chip for multi-channel neural stimulation.

This paper presents an implantable inductively powered mix-signal chip for neural stimulation, which is implemented in 0.35-µm high-voltage CMOS process. It features 16 stimulating channels and a maximum stimulating rate of 15Kbps. The frequency, amplitude and width of stimulating pulses are all wirelessly programmable. Charge balance is guaranteed by a flexible switch array at the stimulating output stage. And to reduce power consumption, a dual-voltage-source scheme is adopted also at the stimulating output stage. Measurement results show that the proposed chip consumes less than 1mA at typical working conditions.

[Experimental study on the stimulating effect and the tissue compatibility of a new type of implanted gastric electrical stimulator].

A new type of gastric electrical stimulator (GES) was introduced. After the stimulator was implanted in beagle dogs, its stimulating effects and the pathological changes at the implant site were observed to study the safety and efficacy of stimulator as well as the tissue compatibility of the materials used. The results showed that, this type of stimulator was safe and capable of inhibiting food intake of the dogs, and that the materials used had good tissue compatibility.