A Nonlinear Single Channel Gradient Insert for Prostate Diffusion Imaging.

Objective.Reliable prediction of gradient-induced peripheral nerve stimulation (PNS) and cardiac stimulation (CS) is important to ensure patient safety and maximize imaging performance in modern MRI scanners. Here we extend the dynamic convolution-based PNS prediction model to CS, and present theoretical analysis and numerical survey of general properties of the convolution model.Approach.CS convolution kernel was derived from the exponential model of the strength-duration curve of excitable tissue stimulation with representative stimulation parameters for a whole-body gradient coil. Self-consistency of the convolution method and the properties of the convolution output (response function) for a periodic trapezoidal wave were theoretically analyzed. PNS and CS response functions were computed for clinical 3T brain and pelvic imaging sequences for comparison.Main results.CS convolution kernel takes the form of a simple, decaying exponential function. For both PNS and CS kernels, the convolution model is consistent with the strength-duration curve when applied to a rectangular dG/dtpulse. The long time constant of a CS kernel tends to suppress stimulation by short dG/dtpulses, and makes dynamic CS response correlate more with gradient amplitude than slew rate. On a trapezoidal gradient pulse train, the maximum PNS or CS occurs at the end of the first full slope of the waveform, independent of the number of cycles. In light of the available evidence to the contrary, such independence indicates limitation of the convolution model which is strictly linear.Significance.The proposed CS convolution model can supplement existing PNS models to better assess patient safety of arbitrary gradient waveforms. General theoretical properties of the convolution model can help guide waveform design to minimize risks. While our method was demonstrated primarily on whole-body gradient systems, it can also inform PNS and CS prediction for anatomy-specific scanners employing fast and strong gradient fields.

Magnetic fields switching at kilohertz frequencies induce electric fields in the body, which can cause peripheral nerve stimulation (PNS). Although magnetostimulation has been extensively studied below 10 kHz, the behavior of PNS at higher frequencies remains poorly understood. This study aims to investigate PNS thresholds at frequencies up to 88.1 kHz and to explore deviations from the widely accepted hyperbolic strength-duration curve (SDC).PNS thresholds were measured in the head of 8 human volunteers using a solenoidal coil at 16 distinct frequencies, ranging from 200 Hz to 88.1 kHz. A hyperbolic SDC was used as a reference to compare the frequency-dependent behavior of PNS thresholds.Contrary to the predictions of the hyperbolic SDC, PNS thresholds did not decrease monotonically with frequency. Instead, thresholds reached a minimum near 25 kHz, after which they increased by an average of 39% from 25 kHz to 88.1 kHz across subjects. This pattern indicates a significant deviation from previously observed behavior at lower frequencies.Our results suggest that PNS thresholds exhibit a non-monotonic frequency dependence at higher frequencies, diverging from the traditional hyperbolic SDC. These findings offer critical data for refining neurodynamic models and provide insights for setting PNS safety limits in applications like MRI gradient coils and magnetic particle imaging (MPI). Further investigation is needed to understand the biological mechanisms driving these deviations beyond 25 kHz.Clinical impact—These findings call for further basic research into biological mechanisms underlying high frequency PNS threshold trends, and supports refinement of safety guidelines for MRI and MPI systems for clinical implementation.

Modern magnetic resonance imaging scanners with high-performance gradient systems have high maximum gradient strength (Gmax ) and slew rate (Smax ). Peripheral nerve stimulation (PNS) is often a more limiting factor for gradient waveform design than Gmax and Smax . Traditionally, the slew rate is derated globally to adhere to PNS limitations. In this work, the PNS limitation is already included in the gradient waveform design in the form of a time-varying slew rate, hence shortening the overall gradient duration. Spiral and echo-planar imaging trajectories were designed with a multitude of parameters, and it was demonstrated that trajectory durations from conventional to PNS-optimal design can be shortened by 8 and 3%, respectively. Including PNS-limits in the gradient waveform design can shorten the duration of gradient trajectories, thereby reducing associated artifacts.

Paired associative stimulation (PAS) is a stimulation technique combining transcranial magnetic stimulation (TMS) and peripheral nerve stimulation (PNS) that can induce plastic changes in the human motor system. A PAS protocol consisting of a high-intensity single TMS pulse given at 100% of stimulator output (SO) and high-frequency 100-Hz PNS train, or “the high-PAS” was designed to promote corticomotoneuronal synapses. Such PAS, applied as a long-term intervention, has demonstrated therapeutic efficacy in spinal cord injury (SCI) patients. Adding a second TMS pulse, however, rendered this protocol inhibitory. The current study sought for more effective PAS parameters. Here, we added a third TMS pulse, i.e., a 20-Hz rTMS (three pulses at 96% SO) combined with high-frequency PNS (six pulses at 100 Hz). We examined the ability of the proposed stimulation paradigm to induce the potentiation of motor-evoked potentials (MEPs) in five human subjects and described the safety and tolerability of the new protocol in these subjects. In this study, rTMS alone was used as a control. In addition, we compared the efficacy of the new protocol in five subjects with two PAS protocols consisting of PNS trains of six pulses at 100 Hz combined with (a) single 100% SO TMS pulses (high-PAS) and (b) a 20-Hz rTMS at a lower intensity (three pulses at 120% RMT). The MEPs were measured immediately after, and 30 and 60 min after the stimulation. Although at 0 and 30 min there was no significant difference in the induced MEP potentiation between the new PAS protocol and the rTMS control, the MEP potentiation remained significantly higher at 60 min after the new PAS than after rTMS alone. At 60 min, the new protocol was also more effective than the two other PAS protocols. The new protocol caused strong involuntary twitches in three subjects and, therefore, its further characterization is needed before introducing it for clinical research. Additionally, its mechanism plausibly differs from PAS with high-frequency PNS that has been used in SCI patients.

Optimizing three-dimensional (3D) k-space sampling trajectories is important for efficient MRI yet presents a challenging computational problem. This work proposes a generalized framework for optimizing 3D non-Cartesian sampling patterns via data-driven optimization. We built a differentiable simulation model to enable gradient-based methods for sampling trajectory optimization. The algorithm can simultaneously optimize multiple properties of sampling patterns, including image quality, hardware constraints (maximum slew rate and gradient strength), reduced peripheral nerve stimulation (PNS), and parameter-weighted contrast. The proposed method can either optimize the gradient waveform (spline-based freeform optimization) or optimize properties of given sampling trajectories (such as the rotation angle of radial trajectories). Notably, the method can optimize sampling trajectories synergistically with either model-based or learning-based reconstruction methods. We proposed several strategies to alleviate the severe nonconvexity and huge computation demand posed by the large scale. The corresponding code is available as an open-source toolbox. We applied the optimized trajectory to multiple applications including structural and functional imaging. In the simulation studies, the image quality of a 3D kooshball trajectory was improved from 0.29 to 0.22 (NRMSE) with Stochastic optimization framework for 3D NOn-Cartesian samPling trajectorY (SNOPY) optimization. In the prospective studies, by optimizing the rotation angles of a stack-of-stars (SOS) trajectory, SNOPY reduced the NRMSE of reconstructed images from 1.19 to 0.97 compared to the best empirical method (RSOS-GR). Optimizing the gradient waveform of a rotational EPI trajectory improved participants' rating of the PNS from "strong" to "mild." SNOPY provides an efficient data-driven and optimization-based method to tailor non-Cartesian sampling trajectories.

Soft sensors have been playing a crucial role in detecting different types of physical stimuli to part or the entire body of a robot, analogous to mechanoreceptors or proprioceptors in biology. Most of the currently available soft sensors with compact form factors can detect only a single deformation mode at a time due to the limitation in combining multiple sensing mechanisms in a limited space. However, realizing multiple modalities in a soft sensor without increasing its original form factor is beneficial, because even a single input stimulus to a robot may induce a combination of multiple modes of deformation. Here, we report a multifunctional soft sensor capable of decoupling combined deformation modes of stretching, bending, and compression, as well as detecting individual deformation modes, in a compact form factor. The key enabling design feature of the proposed sensor is a combination of heterogeneous sensing mechanisms: optical, microfluidic, and piezoresistive sensing. We characterize the performance on both detection and decoupling of deformation modes, by implementing both a simple algorithm of threshold evaluation and a machine learning technique based on an artificial neural network. The proposed soft sensor is able to estimate eight different deformation modes with accuracies higher than 95%. We lastly demonstrate the potential of the proposed sensor as a method of human-robot interfaces with several application examples highlighting its multifunctionality.

The time-varying magnetic field gradients used in MRI can cause peripheral nerve stimulation (PNS) in human subjects, as a result of the electric fields induced in tissue. The local electric field, E, is given by E = - partial differential A/ partial differential t - nabla phi where A, is the vector potential and phi is the scalar electric potential generated by charges accumulated at boundaries between regions of different conductivity. Difficulties in calculating phi have led some investigators to use - partial differential A/ partial differential t alone as a predictor of the induced field. Here the spatial variation of - partial differential A/ partial differential t and E is investigated for the case of a simple spherical conductor exposed to time-varying gradients produced by two different gradient coils that generate identical internal magnetic fields, but very different vector potentials. The results indicate that the temporal derivative of A bears little relation to the induced electric field, and that consequently neglecting the effect of the scalar potential introduces significant errors in estimating the likelihood of PNS.

Recently, head mounted displays based on holographic optical elements have been reported. These head mounted displays can be implemented in a compact form factor but the image delivered to user's each eye is still two-dimensional, which causes accommodation-vergence mismatch. In this paper, we propose a holographic three-dimensional head mounted display which displays holographic three-dimensional images to each eye. The proposed configuration can achieve a compact form factor by using holographic optical elements, and solve the accommodation-vergence mismatch by presenting holographic three-dimensional images to each eye.

Dual-stripline is gaining popularity in computer designs to save printed circuit board (PCB) cost and achieve more compact form factor. A key concern in dual-stripline design is the inter-layer crosstalk (ILC). In this paper, differential dual-stripline crosstalk is investigated, and a complete design strategy is provided. In addition to the conventional crosstalk mitigation techniques, an innovative wiring technique is proposed to reduce ILC for parallel dual-striplines. The proposed routing strategy can effectively mitigate the impact of ILC, and, as a result, enable high-density PCB layout, achieve compact form factor, and save the bill of material (BOM) cost.

Frequency Selective Surfaces (FSSs) are engineered periodic structures that function as spatial filters, enabling the selective transmission, reflection, or rejection of electromagnetic (EM) waves. These structures are widely utilized in advanced RF and microwave systems for applications such as filtering, electromagnetic shielding, and stealth technology. In this manuscript, we propose an angularly stable and polarization-insensitive band-stop Frequency Selective Surface (FSS) optimized for applications in the lower Ka-band. The proposed FSS is constructed from a periodic two-dimensional array of compact unit cells, each with dimensions of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5.5 ~\text{mm} \times 5.5 ~\text{mm}$</tex>. This compact configuration supports seamless integration into high-density millimeter-wave (mmWave) systems. Each unit cell incorporates a concentric dual-ring geometry embedded with orthogonally intersecting cross-slots. This unique configuration enhances the EM wave rejection capability of the surface by inducing multiple resonances. As a result, the FSS achieves a wideband stopband response ranging from 20 GHz to 28.5 GHz, corresponding to a notable fractional bandwidth (FBW) of approximately 34%. The dual-ring structure promotes resonance coupling, while the cross-slot inclusions facilitate effective suppression of incident waves, regardless of polarization orientation. The concentric design further contributes to polarization insensitivity, ensuring consistent stopband performance for both vertically and horizontally polarized waves. In addition, the symmetrical layout enhances angular stability, a critical requirement for real-world deployment. To verify the robustness of the design, full-wave electromagnetic simulations were conducted under varying angles of incidence for both Transverse Electric (TE) and Transverse Magnetic (TM) polarizations. The results reveal negligible shifts in the resonance frequency and stable stopband attenuation across angles up to 60°, confirming the angular resilience of the structure. With its compact form factor, wideband rejection characteristics, and strong angular and polarization stability, the proposed FSS is highly suitable for advanced mmWave applications, including EM interference suppression, co-site interference mitigation, and radar cross-section (RCS) reduction in defense, aerospace, and next-generation communication systems.

Far-UVC light in the wavelength range of 200–230 nm has attracted renewed interest because of its safety for human exposure and effectiveness in inactivating pathogens. Here we present a compact solid-state far-UVC laser source based on second-harmonic generation (SHG) using a low-cost commercially-available blue laser diode pump. Leveraging the high intensity of light in a nanophotonic waveguide and heterogeneous integration, our approach achieves Cherenkov phase-matching across a bonded interface consisting of a silicon nitride (SiN) waveguide and a beta barium borate (BBO) nonlinear crystal. Through systematic investigations of waveguide dimensions and pump power, we analyze the dependencies of Cherenkov emission angle, conversion efficiency, and output power. Experimental results confirm the feasibility of generating far-UVC, paving the way for mass production in a compact form factor. This solid-state far-UVC laser source shows significant potential for applications in human-safe disinfection, non-line-of-sight free-space communication, and deep-UV Raman spectroscopy.

Metasurface has drawn extensive attention due to its capability of modulating light with a high degree of freedom through ultrathin and sub-wavelength optical elements, and metalens, as one of its important applications, promises to replace the bulky refractive optics, facilitating the imaging system light-weight and compact characteristics. Besides, computer-generated holography (CGH) is of substantial interest for three-dimensional (3D) imaging technology by virtue of its ability of restoring the whole optical wave field and re-constructing the true 3D scene. Consequently, the combination of metalens and CGH holds transformative potential in enabling the miniaturization of 3D imaging systems. However, its imaging performance is subject to the aberrations and speckle noises originating from the metalens and CGH. Inspired by recent progress that computational imaging can be applied to close the gap, a novel full-color imaging system, adopting end-to-end joint optimization of metalens and CGH for high imaging quality, is proposed in this paper. The U-net based network as the pre-processing adjusts weights to make the holographic reconstruction offset imaging defects, incorporating the imaging processing into the step of generating hologram. Optimized by deep learning, the proposed imaging system is capable of full-color imaging with high fidelity in a compact form factor, envisioned to take an essential step towards the high-performance miniaturized imaging system.

Solid State Drives (SSDs) have become industry standard for data storage due to their compact form factor, high data transfer speeds, and low power consumption. However, each SSD manufacturer uses their own proprietary firmware, meaning the user does not know how data is stored on the SSD. The primary contribution of this work is the demonstration of the capability to classify activity (read and write) on an SSD versus non-activity (idle) using the temperature side-channel. In addition, we present promising initial results using temperature signatures obtained from an infrared camera to classify read, write, and idle operations. Read and write operations were evaluated at 100 MB, 500 MB, and 1000 MB on four industry standard M.2 SSDs. This study demonstrated the ability to determine when an SSD was inactive with 100% recall, while being able to identify an active SSD with at least 94 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> sensitivity for each of the four SSDs. We were able to classify operations with an accuracy of 80% on three of the four SSDs.

The ePix100 x-ray camera is a new system designed and built at SLAC for experiments at the Linac Coherent Light Source (LCLS). The camera is the first member of a family of detectors built around a single hardware and software platform, supporting a variety of front-end chips. With a readout speed of 120 Hz, matching the LCLS repetition rate, a noise lower than 80 e-rms and pixels of 50 m x 50 m, this camera offers a viable alternative to fast readout, direct conversion, scientific CCDs in imaging mode. The detector, designed for applications such as X-ray Photon Correlation Spectroscopy (XPCS) and wavelength dispersive X-ray Emission Spectroscopy (XES) in the energy range from 2 to 10 keV and above, comprises up to 0.5 Mpixels in a very compact form factor. In this paper, we report the performance of the camera during its first use at LCLS.

We propose a novel, to the best of our knowledge, waveguide-type optical see-through Maxwellian near-eye display for augmented reality. A pin-mirror holographic optical element (HOE) array enables the Maxwellian view and eye-box replication. Virtual images with deep depth of field are presented by each pin-mirror HOE, alleviating the discrepancy between vergence and accommodation distance. The compact form factor is achieved by the thin waveguide and HOE couplers.