Valenciawilliams7413

Ultrasound Computed Tomography (USCT) has great potential for 3D quantitative imaging of acoustic breast tissue properties. Typical devices include high-frequency transducers, which makes tomography techniques based on numerical wave propagation simulations computationally challenging, especially in 3D. Therefore, despite the finite-frequency nature of ultrasonic waves, ray-theoretical approaches to transmission tomography are still widely used. This work introduces finite-frequency traveltime tomography to medical ultrasound. In addition to being computationally tractable for 3D imaging at high frequencies, the method has two main advantages (1) It correctly accounts for the frequency dependence and volumetric sensitivity of traveltime measurements, which are related to off-ray-path scattering and diffraction. (2) It naturally enables out-of-plane imaging and the construction of 3D images from 2D slice-by-slice acquisition systems. Our method rests on the availability of calibration data in water, used to linearize the forward problem and to provide analytical expressions of cross-correlation traveltime sensitivity. As a consequence of the finite frequency content, sensitivity is distributed in multiple Fresnel volumes, thereby providing out-of-plane sensitivity. To improve computational efficiency, we develop a memory-efficient implementation by encoding the Jacobian operator with a 1D parameterization, which allows us to extend the method to large-scale domains. We validate our tomographic approach using lab measurements collected with a 2D setup of transducers and using a cylindrically symmetric phantom. We then demonstrate its applicability for 3D reconstructions by simulating a slice-by-slice acquisition system using the same dataset.This study proposed a rotary traveling wave ultrasonic motor utilizing the B (0, 5) axial bending mode of a ring-shape stator. The proposed motor had a compact structure as only four groups of piezoelectric ceramics were nested into the stator to produce a bending traveling wave, a new design method was proposed utilized less PZT ceramics to reduce the volume and to improve the mechanical output characteristics. The operating principle of the proposed motor was illustrated. The finite element analysis was performed to obtain the vibration modes and the motion trajectories of the stator. A prototype was manufactured to validate the operating principle. The two standing waves and the motion trajectories of the driving tips were measured. The results denoted that this motor obtained an output speed of 53.86 rpm under a preload of 0.69 N when the frequency and voltage were 24.86 kHz and 250 Vp-p, the maximum stall torque was tested as about 0.11 N·m under the preload of 3.14 N Finally, this study was compared with a previous design and it was found that the volume was reduced markedly; furthermore, the no-load speed, the efficiency, the torque density and the power density were improved significantly.This paper reports on the modeling, design, fabrication, and testing of high-performance X-cut Lithium Niobate (LN) Laterally Vibrating Resonators (LVR) operating around 50 MHz. The objective of this work is to exploit the high Figure of Merit (FoM) -product of quality factor at series resonance (Qs) and electromechanical coupling (kt2)- to provide for large passive voltage amplification in the front-end of emerging radio frequency (RF) applications -i.e., Wake-Up Radio Receivers (WuRx). Finite Element Analysis (FEA) is performed to optimize devices' geometry and ensure simultaneous high Qs and kt2. Resonators exhibiting Qs > 5,300 and kt2 > 27% are demonstrated, with FoM > 1,650 -the highest recorded for resonators in the MHz range to the authors' knowledge. Finally, passive voltage gains between 35 V/V and 57 V/V are showcased for capacitive loads ranging from 400 fF to 1 pF.Unsupervised domain adaptation has increasingly gained interest in medical image computing, aiming to tackle the performance degradation of deep neural networks when being deployed to unseen data with heterogeneous characteristics. In this work, we present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA), to effectively adapt a segmentation network to an unlabeled target domain. Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. In particular, we simultaneously transform the appearance of images across domains and enhance domain-invariance of the extracted features by leveraging adversarial learning in multiple aspects and with a deeply supervised mechanism. The feature encoder is shared between both adaptive perspectives to leverage their mutual benefits via end-to-end learning. We have extensively evaluated our method with cardiac substructure segmentation and abdominal multi-organ segmentation for bidirectional cross-modality adaptation between MRI and CT images. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images, and outperforms the state-of-the-art domain adaptation approaches by a large margin.Deformable image registration is a very important field of research in medical imaging. Recently multiple deep learning approaches were published in this area showing promising results. However, drawbacks of deep learning methods are the need for a large amount of training datasets and their inability to register unseen images different from the training datasets. One shot learning comes without the need of large training datasets and has already been proven to be applicable to 3D data. In this work we present a one shot registration approach for periodic motion tracking in 3D and 4D datasets. see more When applied to a 3D dataset the algorithm calculates the inverse of the registration vector field simultaneously. For registration we employed a U-Net combined with a coarse to fine approach and a differential spatial transformer module. The algorithm was thoroughly tested with multiple 4D and 3D datasets publicly available. The results show that the presented approach is able to track periodic motion and to yield a competitive registration accuracy.