Towards A Wireless Image Sensor for Real-Time Fluorescence Microscopy in Cancer Therapy.

We present a mm-sized, ultrasonically powered lensless CMOS image sensor as a progress towards wireless fluorescence microscopy. Access to biological information within the tissue has the potential to provide insights guiding diagnosis and treatment across numerous medical conditions including cancer therapy. This information, in conjunction with current clinical imaging techniques that have limitations in obtaining images continuously and lack wireless compatibility, can improve continual detection of multicell clusters deep within tissue. The proposed platform incorporates a 2.4×4.7 mm2 integrated circuit (IC) fabricated in TSMC 0.18 µm, a micro laser diode (µLD), a single piezoceramic and off-chip storage capacitors. The IC consists of a 36×40 array of capacitive trans-impedance amplifier-based pixels, wireless power management and communication via ultrasound and a laser driver all controlled by a Finite State Machine. The piezoceramic harvests energy from the acoustic waves at a depth of 2 cm to power up the IC and transfer 11.5 kbits/frame via backscattering. During Charge-Up, the off-chip capacitor stores charge to later supply a high-power 78 mW µLD during Imaging. Proof of concept of the imaging front end is shown by imaging distributions of CD8 T-cells, an indicator of the immune response to cancer, ex vivo, in the lymph nodes of a functional immune system (BL6 mice) against colorectal cancer consistent with the results of a fluorescence microscope. The overall system performance is verified by detecting 140 µm features on a USAF resolution target with 32 ms exposure time and 389 ms ultrasound backscattering.

Towards A Wireless Image Sensor for Real-Time Fluorescence Microscopy in Cancer Therapy.

We present a mm-sized, ultrasonically powered lensless CMOS image sensor as a progress towards wireless fluorescence microscopy. Access to biological information within the tissue has the potential to provide insights guiding diagnosis and treatment across numerous medical conditions including cancer therapy. This information, in conjunction with current clinical imaging techniques that have limitations in obtaining images continuously and lack wireless compatibility, can improve continual detection of multicell clusters deep within tissue. The proposed platform incorporates a 2.4×4.7 mm2 integrated circuit (IC) fabricated in TSMC 0.18 µm, a micro laser diode (µLD), a single piezoceramic and off-chip storage capacitors. The IC consists of a 36×40 array of capacitive trans-impedance amplifier-based pixels, wireless power management and communication via ultrasound and a laser driver all controlled by a Finite State Machine. The piezoceramic harvests energy from the acoustic waves at a depth of 2 cm to power up the IC and transfer 11.5 kbits/frame via backscattering. During Charge-Up, the off-chip capacitor stores charge to later supply a high-power 78 mW µLD during Imaging. Proof of concept of the imaging front end is shown by imaging distributions of CD8 T-cells, an indicator of the immune response to cancer, ex vivo, in the lymph nodes of a functional immune system (BL6 mice) against colorectal cancer consistent with the results of a fluorescence microscope. The overall system performance is verified by detecting 140 µm features on a USAF resolution target with 32 ms exposure time and 389 ms ultrasound backscattering.

Fast non-iterative algorithm for 3D point-cloud holography.

Recently developed iterative and deep learning-based approaches to computer-generated holography (CGH) have been shown to achieve high-quality photorealistic 3D images with spatial light modulators. However, such approaches remain overly cumbersome for patterning sparse collections of target points across a photoresponsive volume in applications including biological microscopy and material processing. Specifically, in addition to requiring heavy computation that cannot accommodate real-time operation in mobile or hardware-light settings, existing sampling-dependent 3D CGH methods preclude the ability to place target points with arbitrary precision, limiting accessible depths to a handful of planes. Accordingly, we present a non-iterative point cloud holography algorithm that employs fast deterministic calculations in order to efficiently allocate patches of SLM pixels to different target points in the 3D volume and spread the patterning of all points across multiple time frames. Compared to a matched-performance implementation of the iterative Gerchberg-Saxton algorithm, our algorithm's relative computation speed advantage was found to increase with SLM pixel count, reaching >100,000x at 512 × 512 array format.

A Sparse Sampling Sensor Front-End IC for Low Power Continuous SpO 2 & HR Monitoring.

Photoplethysmography (PPG) is an attractive method to acquire vital signs such as heart rate and blood oxygenation and is frequently used in clinical and at-home settings. Continuous operation of health monitoring devices demands a low power sensor that does not restrict the device battery life. Silicon photodiodes (PD) and LEDs are commonly used as interface devices in PPG sensors; however, using of flexible organic devices can enhance the sensor conformality and reduce the cost of fabrication. In most PPG sensors, most of system power consumption is concentrated in powering LEDs, traditionally consuming mWs. Using organic devices further increases this power demand since these devices exhibit larger parasitic capacitances and typically need higher drive voltages.This work presents a sensor IC for continuous SpO 2 and HR monitoring that features an on-chip reconstruction-free sparse sampling algorithm to reduce the overall system power consumption by  âˆ¼ 70% while maintaining the accuracy of the output information. The designed frontend is compatible with a wide range of devices from silicon PDs to organic PDs with parasitic capacitances up to 10 nF. Implemented in a 40 nm HV CMOS process, the chip occupies 2.43 mm 2 and consumes 49.7 µW and 15.2 µW of power in continuous and sparse sampling modes respectively. The performance of the sensor IC has been verified in vivo with both types of devices and the results are compared against a clinical grade reference. Less than 1 bpm and 1% mean absolute errors were achieved in both continuous and sparse modes of operation.

Drowsiness Detection with Wireless, User-Generic, Dry Electrode Ear EEG.

Drowsiness monitoring can reduce workplace and driving accidents. To enable a discreet device for drowsiness monitoring and detection, this work presents a drowsiness user-study with an in-ear EEG system, which uses two user-generic, dry electrode earpieces and a wireless interface for streaming data. Twenty-one drowsiness trials were recorded across five human users and drowsiness detection was implemented with three classifier models: logistic regression, support vector machine (SVM), and random forest. To estimate drowsiness detection performance across usage scenarios, these classifiers were validated with user-specific, leave-one-trial-out, and leave-one-user-out training. To our knowledge, this is the first wireless, multi-channel, dry electrode in-ear EEG to be used for drowsiness monitoring. With user-specific training, a SVM achieved a detection accuracy of 95.9%. When evaluating a never-before-seen user, a similar SVM achieved a 94.5% accuracy, comparable to the best performing state-of-the-art wet electrode in-ear and scalp EEG systems.

Phased Array Beamforming Methods for Powering Biomedical Ultrasonic Implants.

Millimeter-scale implants using ultrasound (US) for power and communication have been proposed for a range of deep-tissue applications, including neural recording and stimulation. However, published implementations have shown high sensitivity to misalignment with the external US transducer. Ultrasonic beamforming using a phased array to these implants can improve tolerance to misalignment, reduce implant volume, and allow multiple implants to be operated simultaneously in different locations. This article details the design of a custom planar phased array US system, which is capable of steering and focusing US power within a 3-D volume. Analysis and simulation is performed to determine the choice of array element pitch, with special attention given to maximizing the power available at the implant while meeting FDA limits for diagnostic US. Time reversal (TR) is proposed as a computationally simple approach to beamforming that is robust despite scattering and inhomogeneity of the acoustic medium. This technique is demonstrated both in active drive and pulse-echo modes, and it is experimentally compared with other beamforming techniques by measuring energy transfer efficiency. Simultaneous power delivery to multiple implants is also demonstrated.

Towards an Implantable Fluorescence Image Sensor for Real-Time Monitoring of Immune Response in Cancer Therapy.

Real-time monitoring of cellular-level changes inside the body provides key information regarding disease progression and therapy assessment for critical care including cancer therapy. Current state-of-the-art oncological imaging methods impose unnecessary latencies to detect small cell foci. Invasive methods such as biopsies, on the other hand, cause disruption if deployed on a repeated basis. Therefore, they are not practical for real-time assessments of the tumor tissue. This work presents a proof-of-concept design for an implantable fluorescence lensless image sensor to address the pervasive challenge of real-time tracking of the immune response in immunotherapy. The 2.4x4.7 mm2 integrated circuit (IC) prototype consists of a 36 by 40 pixel array, a laser driver and a power management unit harvesting power and transferring 11.5 kbits/frame through a wireless ultrasound link while implanted 2 cm deep inside the body. Compared to prior art, this is the first full-fledged wireless system implementing chip-scale fluorescence microscopy to the best of our knowledge.Clinical relevance- This prototype can be used to personalize immunotherapy for the 50% of cancer patients who do not initially respond to the therapy.

A micromirror array with annular partitioning for high-speed random-access axial focusing.

Dynamic axial focusing functionality has recently experienced widespread incorporation in microscopy, augmented/virtual reality (AR/VR), adaptive optics and material processing. However, the limitations of existing varifocal tools continue to beset the performance capabilities and operating overhead of the optical systems that mobilize such functionality. The varifocal tools that are the least burdensome to operate (e.g. liquid crystal, elastomeric or optofluidic lenses) suffer from low (≈100 Hz) refresh rates. Conversely, the fastest devices sacrifice either critical capabilities such as their dwelling capacity (e.g. acoustic gradient lenses or monolithic micromechanical mirrors) or low operating overhead (e.g. deformable mirrors). Here, we present a general-purpose random-access axial focusing device that bridges these previously conflicting features of high speed, dwelling capacity and lightweight drive by employing low-rigidity micromirrors that exploit the robustness of defocusing phase profiles. Geometrically, the device consists of an 8.2 mm diameter array of piston-motion and 48-µm-pitch micromirror pixels that provide 2π phase shifting for wavelengths shorter than 1100 nm with 10-90% settling in 64.8 µs (i.e., 15.44 kHz refresh rate). The pixels are electrically partitioned into 32 rings for a driving scheme that enables phase-wrapped operation with circular symmetry and requires <30 V per channel. Optical experiments demonstrated the array's wide focusing range with a measured ability to target 29 distinct resolvable depth planes. Overall, the features of the proposed array offer the potential for compact, straightforward methods of tackling bottlenecked applications, including high-throughput single-cell targeting in neurobiology and the delivery of dense 3D visual information in AR/VR.

Unsupervised Online Learning for Long-Term High Sensitivity Seizure Detection.

Current seizure detection systems rely on machine learning classifiers that are trained offline and subsequently require manual retraining to maintain high detection accuracy over long periods of time. For a true deploy-and-forget implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to the neural signal drifts over time. This work proposes SOUL: Stochastic-gradient-descent-based Online Unsupervised Logistic regression classifier, which provides continuous unsupervised online model updates that was initially trained with labels offline. SOUL was tested on two datasets, the CHB-MIT scalp EEG dataset, and a long (>250 hours) human ECoG dataset from the University of Melbourne. SOUL achieves an average cumulative sensitivity of 97.5% and 97.9% for the two datasets respectively, while maintaining <1.2 false alarms per day. When compared with state-of-the-art, a moderate sensitivity improvement of 1-3% is observed on the majority of subjects and a large sensitivity improvement of >12% is observed on three subjects with <1% impact on specificity.

High Throughput Ultrasonic Multi-implant Readout Using a Machine-Learning Assisted CDMA Receiver.

Untethered, wireless peripheral nerve recording for prosthetic control requires multi-implant communications at high data rates. This work presents a multiple-access ultrasonic uplink data communication channel comprised of 4 free-floating implants and a single-element external transducer. Using code-division multiple access (CDMA), overall channel data rates of up to 784 kbps were measured, and a machine-learning assisted decoder improved BER by >100x. Compared with prior art, this work incorporates the largest number of implants at the highest data rate and spectral efficiency reported.

Optimizing Volumetric Efficiency and Backscatter Communication in Biosensing Ultrasonic Implants.

Ultrasonic backscatter communication has gained popularity in recent years with the advent of deep-tissue sub-mm scale biosensing implants in which piezoceramic (piezo) resonators are used as acoustic antennas. Miniaturization is a key design goal for such implants to reduce tissue displacement and enable minimally invasive implantation techniques. Here, we provide a systematic design approach for the implant piezo geometry and operation frequency to minimize the overall volume of the implant. Optimal geometry of the implant piezo for backscatter communication is discussed and contrasted with that of power harvesting. A critical design aspect of an ultrasonic backscatter communication link is the response of the piezo acoustic reflection coefficient Γ with respect to the variable shunt impedance, ZE, of the implant uplink modulator. Due to the complexity of the piezo governing equations and multi-domain, electro-acoustical nature of the piezo, Γ(ZE) has often been characterized numerically and the implant uplink modulator has been designed empirically resulting in sub-optimal performance in terms of data rate and linearity. Here, we present a SPICE friendly end-to-end equivalent circuit model of the channel as a piezo-IC co-simulation tool that incorporates inherent path losses present in a typical ultrasonic backscatter channel. To provide further insight into the channel response, we present experimentally validated closed form expressions for Γ(ZE) under various boundary conditions. These expressions couple Γ to the commonly used Thevenin equivalent circuit model of the piezo, facilitating systematic design and synthesis of ultrasonic backscatter uplink modulators.

Wireless User-Generic Ear EEG.

In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. The earpiece is designed for improved ear canal contact across a wide population of users and is fabricated in a low-cost and scalable manufacturing process based on standard techniques such as vacuum forming, plasma-treatment, and spray coating. A 2.5 × 2.5 cm2 wireless recording module is designed to record and stream data wirelessly to a host computer. Performance was evaluated on three human subjects over three months and compared with clinical-grade wet scalp EEG recordings. Recordings of spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the auditory steady-state response (ASSR), are presented. This is the first wireless in-ear EEG to our knowledge to incorporate a dry multielectrode, user-generic design. The user-generic ear EEG recorded a mean alpha modulation of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG systems.

A wireless millimetre-scale implantable neural stimulator with ultrasonically powered bidirectional communication.

Clinically approved neural stimulators are limited by battery requirements, as well as by their large size compared with the stimulation targets. Here, we describe a wireless, leadless and battery-free implantable neural stimulator that is 1.7 mm3 and that incorporates a piezoceramic transducer, an energy-storage capacitor and an integrated circuit. An ultrasonic link and a hand-held external transceiver provide the stimulator with power and bidirectional communication. The stimulation protocols were wirelessly encoded on the fly, reducing power consumption and on-chip memory, and enabling protocol complexity with a high temporal resolution and low-latency feedback. Uplink data indicating whether stimulation occurs are encoded by the stimulator through backscatter modulation and are demodulated at the external transceiver. When embedded in ex vivo porcine tissue, the integrated circuit efficiently harvested ultrasonic power, decoded downlink data for the stimulation parameters and generated current-controlled stimulation pulses. When cuff-mounted and acutely implanted onto the sciatic nerve of anaesthetized rats, the device conferred repeatable stimulation across a range of physiological responses. The miniaturized neural stimulator may facilitate closed-loop neurostimulation for therapeutic interventions.

A wireless and artefact-free 128-channel neuromodulation device for closed-loop stimulation and recording in non-human primates.

Closed-loop neuromodulation systems aim to treat a variety of neurological conditions by delivering and adjusting therapeutic electrical stimulation in response to a patient's neural state, recorded in real time. Existing systems are limited by low channel counts, lack of algorithmic flexibility, and the distortion of recorded signals by large and persistent stimulation artefacts. Here, we describe an artefact-free wireless neuromodulation device that enables research applications requiring high-throughput data streaming, low-latency biosignal processing, and simultaneous sensing and stimulation. The device is a miniaturized neural interface capable of closed-loop recording and stimulation on 128 channels, with on-board processing to fully cancel stimulation artefacts. In addition, it can detect neural biomarkers and automatically adjust stimulation parameters in closed-loop mode. In a behaving non-human primate, the device enabled long-term recordings of local field potentials and the real-time cancellation of stimulation artefacts, as well as closed-loop stimulation to disrupt movement preparatory activity during a delayed-reach task. The neuromodulation device may help advance neuroscientific discovery and preclinical investigations of stimulation-based therapeutic interventions.

Toward true closed-loop neuromodulation: artifact-free recording during stimulation.

Closed-loop and responsive neuromodulation systems improve open-loop neurostimulation by responding directly to measured neural activity and providing adaptive, on-demand therapy. To be effective, these systems must be able to simultaneously record and stimulate neural activity, a task made difficult by persistent stimulation artifacts that distort and obscure underlying biomarkers. To enable simultaneous stimulation and recording, several techniques have been proposed. These techniques involve artifact-preventing system configurations, resilient recording front-ends, and back-end signal processing for removing recorded artifacts. Co-designing and integrating these artifact cancellation techniques will be key to enabling neuromodulation systems to stimulate and record at the same time. Here, we review the state-of-the-art for these techniques and their role in achieving artifact-free neuromodulation.

Powering and communication for OMNI: A distributed and modular closed-loop neuromodulation device.

A distributed, modular, intelligent, and efficient neuromodulation device, called OMNI, is presented. It supports closed-loop recording and stimulation on 256 channels from up to 4 physically distinct neuromodulation modules placed in any configuration around the brain, hence offering the capability of addressing neural disorders that are presented at the network level. The specific focus of this paper is the communication and power distribution network that enables the modular and distributed nature of the device.

Equalization for intracortical microstimulation artifact reduction.

We present a method for decreasing the duration of artifacts present during intra-cortical microstimulation (ICMS) recordings by using techniques developed for digital communications. We replace the traditional monophasic or biphasic current stimulation pulse with a patterned pulse stream produced by a Zero Forcing Equalizer (ZFE) filter after characterizing the artifact as a communications channel. The results find that using the ZFE stimulus has the potential to reduce artifact width by more than 70%. Considerations for the hardware implementation of the equalizer are presented.