Notes

Extracellular vesicle-associated miRNAs are usually the versatile reply to gestational diabetes.
The modeling of source distributions of finite spatial extent in ultrasound and medical imaging applications is a problem of longstanding interest. In time domain methods, such as the finite difference time domain or pseudospectral approaches, one requirement is the representation of such distributions over a grid, normally Cartesian. Various artefacts, including staircasing errors, can arise. In this short contribution, the problem of the representation of a distribution over a grid is framed as an optimisation problem in the Fourier domain over a preselected set of grid points, thus maintaining control over computational cost, and allowing the fine tuning of the optimisation to the wavenumber range of interest for a particular numerical method. Numerical results are presented in the important special case of the spherical cap or bowl source.A solidly mounted resonator on flexible Polyimide (PI) substrate with high effective coupling coefficient (Kt2) of 14.06% is reported in this paper. This high Kt2 is resulting from the LiNbO3 (LN) single crystalline film and [SiO2/Mo]3 Bragg reflector. The quality of LN film fabricated by Crystal-ion-slicing (CIS) technique using Benzocyclobutene (BCB) bonding layer was close to the bulk crystalline LN. The interfaces of the Al/LN/Al/[SiO2/Mo]3 Bragg reflector/BCB/PI multilayer are sharp and the thickness of each layer is consistent with its design value. The resonant frequency and the Kt2 keep stable when it is bended at different radii. These results demonstrate a feasible approach to realizing RF filters on flexible polymer substrates, which is an indispensable device for building integrated and multi-functional wireless flexible electronic systems.Superharmonic imaging with dual-frequency imaging systems uses conventional low-frequency ultrasound transducers on transmit, and high-frequency transducers on receive to detect higher order harmonic signals from microbubble contrast agents, enabling high-contrast imaging while suppressing clutter from background tissues. Current dual-frequency imaging systems for superharmonic imaging have been used for visualizing tumor microvasculature, with single-element transducers for each of the low- and high-frequency components. However, the useful field of view is limited by the fixed focus of single-element transducers, while image frame rates are limited by the mechanical translation of the transducers. In this paper, we introduce an array-based dual-frequency transducer, with low-frequency and high-frequency arrays integrated within the probe head, to overcome the limitations of single-channel dual-frequency probes. read more The purpose of this study is to evaluate the line-by-line high-frequency imaging and superharmonic imaging capabilities of the array-based dual-frequency probe for acoustic angiography applications in vitro and in vivo. We report center frequencies of 1.86 MHz and 20.3 MHz with -6 dB bandwidths of 1.2 MHz (1.2 to 2.4 MHz) and 14.5 MHz (13.3 to 27.8 MHz) for the low- and high-frequency arrays, respectively. With the proposed beamforming schemes, excitation pressure was found to range from 336 kPa to 458 kPa at its azimuthal foci. This was sufficient to induce nonlinear scattering from microbubble contrast agents. Specifically, in vitro contrast channel phantom imaging and in vivo xenograft mouse tumor imaging by this probe with superharmonic imaging showed contrast-to-tissue ratio improvements of 17.7 dB and 16.2 dB, respectively, compared to line-by-line micro-ultrasound B-mode imaging.Digital breast tomosynthesis (DBT) is a quasi-three-dimensional imaging modality that can reduce false negatives and false positives in mass lesion detection caused by overlapping breast tissue in conventional two-dimensional (2D) mammography. The patient dose of a DBT scan is similar to that of a single 2D mammogram, while acquisition of each projection view adds detector readout noise. The noise is propagated to the reconstructed DBT volume, possibly obscuring subtle signs of breast cancer such as microcalcifications (MCs). This study developed a deep convolutional neural network (DCNN) framework for denoising DBT images with a focus on improving the conspicuity of MCs as well as preserving the ill-defined margins of spiculated masses and normal tissue textures. We trained the DCNN using a weighted combination of mean squared error (MSE) loss and adversarial loss. We configured a dedicated x-ray imaging simulator in combination with digital breast phantoms to generate realistic in silico DBT data for training. We compared the DCNN training between using digital phantoms and using real physical phantoms. The proposed denoising method improved the contrast-to-noise ratio (CNR) and detectability index (d') of the simulated MCs in the validation phantom DBTs. These performance measures improved with increasing training target dose and training sample size. Promising denoising results were observed on the transferability of the digital-phantom-trained denoiser to DBT reconstructed with different techniques and on a small independent test set of human subject DBT images.The concept of biological age (BA) - although important in clinical practice - is hard to grasp mainly due to the lack of a clearly defined reference standard. For specific applications, especially in pediatrics, medical image data are used for BA estimation in a routine clinical context. Beyond this young age group, BA estimation is mostly restricted to whole-body assessment using non-imaging indicators such as blood biomarkers, genetic and cellular data. However, various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors. Thus, a whole-body assessment of the BA does not reflect the deviations of aging behavior between organs. To this end, we propose a new imaging-based framework for organ-specific BA estimation. In this initial study we focus mainly on brain MRI. As a first step, we introduce a chronological age (CA) estimation framework using deep convolutional neural networks (Age-Net). We quantitatively assess the performance of this framework in comparison to existing state-of-the-art CA estimation approaches.
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