IEEE transactions on ultrasonics, ferroelectrics, and frequency control最新文献

Ultrasound has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultra-portable and cost-effective, akin to the stethoscope. Nevertheless, and despite many advances, ultrasound image quality and diagnostic efficacy are still highly operator- and patient-dependent. In difficult-to-image patients, image quality is often insufficient for reliable diagnosis. In this paper, we put forth the idea that ultrasound imaging systems can be recast as information-seeking agents that engage in reciprocal interactions with their anatomical environment. Such agents autonomously adapt their transmit-receive sequences to fully personalize imaging and actively maximize information gain in-situ. To that end, we will show that the sequence of pulse-echo experiments that an ultrasound system performs can be interpreted as a perception-action loop: the action is the data acquisition, probing tissue with acoustic waves and recording reflections at the detection array, and perception is the inference of the anatomical and or functional state, potentially including associated diagnostic quantities. We then equip systems with a mechanism to actively reduce uncertainty and maximize diagnostic value across a sequence of experiments, treating action and perception jointly using Bayesian inference given generative models of the environment and action-conditional pulse-echo observations. Since the representation capacity of the generative models dictates both the quality of inferred anatomical states and the effectiveness of inferred sequences of future imaging actions, we will be greatly leveraging the enormous advances in deep generative modelling (generative AI), that are currently disrupting many fields and society at large. Finally, we show some examples of cognitive, closed-loop, ultrasound systems that perform active beamsteering and adaptive scanline selection, based on deep generative models that track anatomical belief states.

This paper reports a 30×12 row-column (RC) addressed flexible piezoelectric micromachined ultrasound transducer (PMUT) array with a top-down fabrication process. The fabrication uses a temporary carrier wafer from which the array device is released by deep reactive ion etching (DRIE). About 0.8 μm thick sol-gel processed Lead Zirconate Titanate (PZT) thin film acts as the active piezoelectric. The flexible PMUT membrane includes the PZT film and a 14 μm polyimide as a passive layer. A sidewall made of polyimide measuring 21 μm in thickness with a cavity of 100 μm in diameter, is realized by reactive ion etching (RIE). Laser Doppler Vibrometer (LDV) characterization of the PMUT indicates 2.7 megahertz (MHz) and 2.1 MHz as the resonance frequency in-air and underwater, respectively. Excitation of a single PMUT element coupled with 5 V direct current (DC) bias results in 1.2 nm/V sensitivity in-air whereas when the same is excited along with 10 V DC bias, a pressure response of 40 Pa/V at 1 cm is measured underwater using a hydrophone. The presented results under bending to an 8 mm bending radius show the potential for wearable applications in shallow-depth regions subject to further optimization.

Three-dimensional ultrasound (3D US) imaging with freehand scanning is utilized in cardiac, obstetric, abdominal, and vascular examinations. While 3D US using either a 'wobbler' or 'matrix' transducer suffers from a small field of view and low acquisition rates, freehand scanning offers significant advantages due to its ease of use. However, current 3D US volumetric reconstruction methods with freehand sweeps are limited by imaging plane shifts along the scanning path, i.e., out-of-plane (OOP) motion. Prior studies have incorporated motion sensors attached to the transducer, which is cumbersome and inconvenient in a clinical setting. Recent work has introduced deep neural networks (DNNs) with 3D convolutions to estimate the position of imaging planes from a series of input frames. These approaches, however, fall short for estimating OOP motion. The goal of this paper is to bridge the gap by designing a novel, physics inspired DNN for freehand 3D US reconstruction without motion sensors, aiming to improve the reconstruction quality, and at the same time, to reduce computational resources needed for training and inference. To this end, we present our physics guided learning-based prediction of pose information (PLPPI) model for 3D freehand US reconstruction without 3D convolution. PLPPI yields significantly more accurate reconstructions and offers a major reduction in computation time. It attains a performance increase in the double digits in terms of mean percentage error, with up to 106% speedup and 131% reduction in Graphic Processing Unit (GPU) memory usage, when compared to latest deep learning methods.

Traditionally, implants are powered by batteries, which have to be recharged by an inductive power link. In the recent years, ultrasonic power links are being investigated, promising more available power for deeply implanted miniaturized devices. These implants often need to transfer back information. For ultrasonically powered implants, this is usually achieved with On-Off Keying based on backscatter modulation, or active driving of a secondary transducer. In this paper, we propose to superimpose subcarriers, effectively leveraging Frequency-Shift Keying, which increases the robustness of the link against interference and fading. It also allows for simultaneous powering and communication, and inherently provides the possibility of frequency domain multiplexing for implant networks. The modulation scheme can be implemented in miniaturized application specific integrated circuits, field programmable gate arrays, and microcontrollers. We have validated this modulation scheme in a water tank during continuous ultrasound and movement. We achieved symbol rates of up to 104 kBd, and were able to transfer data through 20 cm of water and through a 5 cm tissue phantom with additional misalignment and during movements. This approach could provide a robust uplink for miniaturized implants that are located deep inside the body and need continuous ultrasonic powering.

Large vessel occlusion (LVO) stroke, in which major cerebral arteries such as the internal carotid and middle cerebral arteries supplying the brain are occluded, is the most debilitating form of acute ischemic stroke (AIS). The current gold standard treatment for LVO stroke is mechanical thrombectomy, however, initial attempts to recanalize these large, proximal arteries supplying the brain fail in up to 75% of cases, leading to repeated passes that decrease the likelihood of success and affect patient outcomes. We report the design, fabrication, and testing of a 3 mm × 3 mm forward-treating US transducer with an acoustic metamaterial lens to dissolve blood clots recalcitrant to first pass mechanical thrombectomy in LVO stroke. Due to the lens with microscale features, the device was able to produce a 2.3× increase in peak negative pressure (4.3 MPa vs 1.8 MPa) and 2.4× increase in blood clot dissolution rate (5.43 ± 0.89 mg/min vs 2.23 ± 0.41 mg/min) with 90% mass reduction after 30 minutes of treatment. In this small endovascular form factor, the acoustic metamaterial lens increased the acoustic output from the transducer while minimizing the US energy delivered to the surrounding areas outside of the treatment volume.

Accurate assessment of spinal cord vasculature is important for the urgent diagnosis of injury and subsequent treatment. Ultrasound localization microscopy (ULM) offers super-resolution imaging of microvasculature by localizing and tracking individual microbubbles across multiple frames. However, a long data acquisition often involves significant motion artifacts caused by breathing and heartbeat, which further impairs the resolution of ULM. This effect is particularly pronounced in spinal cord imaging due to respiratory movement. We propose a VoxelMorph-based deep learning motion correction method to enhance ULM performance in spinal cord imaging. Simulations were conducted to demonstrate the motion estimation accuracy of the proposed method, achieving a mean absolute error of 8 μm. Results from in vivo experiments show that the proposed method efficiently compensates for rigid and nonrigid motion, providing improved resolution with smaller vascular diameters and enhanced microvessel reconstruction after motion correction. Nonrigid deformation fields with varying displacement magnitudes were applied to in vivo data for assessing the robustness of the algorithm.

Ultrasound Localization Microscopy (ULM) is a novel super-resolution imaging technique that can image the vasculature in vivo at depth with resolution far beyond the conventional limit of diffraction. By relying on the localization and tracking of clinically approved microbubbles injected in the blood stream, ULM can provide not only anatomical visualization but also hemodynamic quantification of the microvasculature of different tissues. Various deep-learning approaches have been proposed to address challenges in ULM including denoising, improving microbubble localization, estimating blood flow velocity or performing aberration correction. Proposed deep learning methods often outperform their conventional counterparts by improving image quality and reducing processing time. In addition, their robustness to high concentrations of microbubbles can lead to reduced acquisition times in ULM, addressing a major hindrance to ULM clinical application. Herein, we propose a comprehensive review of the diversity of deep learning applications in ULM focusing on approaches assuming a sparse microbubbles distribution. We first provide an overview of how existing studies vary in the constitution of their datasets or in the tasks targeted by deep learning model. We also take a deeper look into the numerous approaches that have been proposed to improve the localization of microbubbles since they differ highly in their formulation of the optimization problem, their evaluation, or their network architectures. We finally discuss the current limitations and challenges of these methods, as well as the promises and potential of deep learning for ULM in the future.

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods can offer computationally efficient alternatives. This study investigates the impact of the chosen input modalities on IILR methods for high-resolution SOS reconstruction in USCT. The selected modalities are traveltime tomography (TT) and reflection tomography (RT), which produce a low-resolution SOS map and a reflectivity map, respectively. These modalities have been chosen for their lower computational cost relative to FWI and their capacity to provide complementary information: TT offers a direct SOS measure, while RT reveals tissue boundary information. Systematic analyses were facilitated by employing a virtual USCT imaging system with anatomically realistic numerical breast phantoms. Within this testbed, a supervised convolutional neural network (CNN) was trained to map dual-channel (TT and RT images) to a high-resolution SOS map. Single-input CNNs were trained separately using inputs from each modality alone (TT or RT) for comparison. The accuracy of the methods was systematically assessed using normalized root mean squared error (NRMSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). For tumor detection performance, receiver operating characteristic analysis was employed. The dual-channel IILR method was also tested on clinical human breast data. Ensemble average of the NRMSE, SSIM, and PSNR evaluated on this clinical dataset were 0.2355, 0.8845, and 28.33 dB, respectively.