Robustness Analysis of Texture Features with Different Beamforming Techniques

Texture features are often used on ultrasound images in various applications to give forth important clinical information. Recently, many beamforming techniques have been developed to provide better resolution and contrast in the final image. It is currently unknown, however, how these different techniques may also alter pixel intensity spatial distribution, known as texture. We provide here a robustness analysis of first and second order texture features using six beamforming techniques, on both phantom and in vivo musculoskeletal images. We show that second order texture features are more robust compared to first order features, especially when considering in vivo musculoskeletal images

Detecting anatomical characteristics of single motor units by combining high density electromyography and ultrafast ultrasound: a simulation study

Muscle force production is the result of a sequence of electromechanical events that translate the neural drive issued to the motor units (MUs) into tensile forces on the tendon. Current technology allows this phenomenon to be investigated non-invasively. Single MU excitation and its mechanical response can be studied through high-density surface electromyography (HDsEMG) and ultrafast ultrasound (US) imaging respectively. In this study, we propose a method to integrate these two techniques to identify anatomical characteristics of single MUs. Specifically, we tested two algorithms, combining the tissue velocity sequence (TVS, obtained from ultrafast US images), and the MU firings (extracted from HDsEMG decomposition). The first is the Spike Triggered Averaging (STA) of the TVS based on the occurrences of individual MU firings, while the second relies on the correlation between the MU firing patterns and the TVS spatio-temporal independent components (STICA). A simulation model of the muscle contraction was adapted to test the algorithms at different degrees of neural excitation (number of active MUs) and MU synchronization. The performances of the two algorithms were quantified through the comparison between the simulated and the estimated characteristics of MU territories (size, location). Results show that both approaches are negatively affected by the number of active MU and synchronization levels. However, STICA provides a more robust MU territory estimation, outperforming STA in all the tested conditions. Our results suggest that spatio-temporal independent component decomposition of TVS is a suitable approach for anatomical and mechanical characterization of single MUs using a combined HDsEMG and ultrafast US approach

Impact of increasing levels of condensed tannins from sainfoin in the grower-finisher diets of entire male pigs on growth performance, carcass characteristics, and meat quality.

Sainfoin is a protein-rich legume with an ideal amino acid profile and therefore could partly replace soybeans in the diets of growing pigs. However, sainfoin also contains a non-negligible amount of condensed tannins (CTs), which can act as antinutritional factors. Bioactive plant compounds, like hydrolysable tannins, have been suggested to be suitable in entire male (EM) production, as they impair the development of accessory sex glands and, by that, reduce boar taint compound levels without negatively impacting growth. It is unknown whether, similar to hydrolysable tannins, CTs from sainfoin reduce the incidence of boar taint without impacting growth performance, carcass traits, and meat quality. For the experiment, 48 Swiss Large White EM were assigned within litter to one of four grower (25-60 kg BW) and finisher (60-105 kg BW) diets supplemented with 0 (T0), 5 (T5), 10 (T10), and 15% (T15) sainfoin meal, respectively. The four diets were designed to be isocaloric and isoproteic. Increasing the dietary sainfoin level had no negative effect on growth performance or the carcass characteristics. Despite leading to a similar feed intake between the treatment groups, increasing the dietary sainfoin levels tended (P ≤ 0.08) to reduce the number of feeder visits but increased the time spent at the feeder as well as the feed intake per visit during the finisher period. By increasing sainfoin intake, the levels of C18:3n-3 and long-chain homologs linearly increased (P < 0.01) in the backfat and intramuscular fat (IMF), whereas in the backfat, but not the IMF, the 18:2n-6 levels decreased (P < 0.01). The latter triggered a greater (P < 0.01) desaturation rate (C18:1n-9/C18:0) of the saturated fatty acids, resulting in a greater (P < 0.01) proportion of monounsaturated fatty acid. Apart from a linear decrease (P = 0.02) in the androstenone levels in the longissimus thoracis (LT), increasing the sainfoin intake had no effect on the level of boar taint in the LT and backfat. As determined by the elevated correlation coefficient, skatole and indole levels, but not androstenone levels, in the adipose tissue seem to be reliable proxies for their respective levels in LT and, therefore, in pork. In conclusion, sainfoin is a suitable homegrown protein source for grower finisher pigs and can be included at up to 15% in the diet to replace 7% of soybean in a diet without producing any noteworthy effects on growth, whereas the impact of CTs on boar taint was limited

Multimodal T2w and DWI Prostate Gland Automated Registration

Multiparametric magnetic resonance imaging (mpMRI) is emerging as a promising tool in the clinical pathway of prostate cancer (PCa). The registration between a structural and a functional imaging modality, such as T2-weighted (T2w) and diffusion-weighted imaging (DWI) is fundamental in the development of a mpMRI-based computer aided diagnosis (CAD) system for PCa. Here, we propose an automated method to register the prostate gland in T2w and DWI image sequences by a landmark-based affine registration and a non-parametric diffeomorphic registration. An expert operator manually segmented the prostate gland in both modalities on a dataset of 20 patients. Target registration error and Jaccard index, which measures the overlap between masks, were evaluated pre-and post-registration resulting in an improvement of 44% and 21%, respectively. In the future, the proposed method could be useful in the framework of a CAD system for PCa detection and characterization in mpMRI

Automatic Extraction of Dermatological Parameters from Nevi Using an Inexpensive Smartphone Microscope: A Proof of Concept

The evolution of smartphone technology has made their use more common in dermatological applications. Here we studied the feasibility of using an inexpensive smartphone microscope for the extraction of dermatological parameters and compared the results obtained with a portable dermoscope, commonly used in clinical practice. Forty-two skin lesions were imaged with both devices and visually analyzed by an expert dermatologist. The presence of a reticular pattern was observed in 22 dermoscopic images, but only in 10 smartphone images. The proposed paradigm segments the image and extracts texture features which are used to train and validate a neural network to classify the presence of a reticular pattern. Using 5-fold cross-validation, an accuracy of 100% and 95% was obtained with the dermoscopic and smartphone images, respectively. This approach can be useful for general practitioners and as a triage tool for skin lesion analysis

Ultrasound Image Beamforming Optimization Using a Generative Adversarial Network

Recently, research has been focusing on the development of artificial intelligence ultrasound beamforming methods to improve the contrast and resolution of B-mode images. In this work, we propose an innovative beamforming domain transfer method using a generative adversarial network (GAN). The GAN takes as input a plane-wave (PW) delay and sum (DAS) image and generates an image as if it had been acquired using the focused modality and reconstructed with the filtered Delay Multiply and Sum (F-DMAS) beamforming technique. A Verasonics Vantage 256 system (L11-5v linear array) was used to acquire 560 (480 and 80 for train and test set, respectively) in-vivo musculoskeletal US images. Images were acquired on five muscles (gastrocnemius lateralis, gastrocnemius medialis, vastus lateralis, vastus medialis, and biceps) on both sides of 14 healthy volunteers (50% female). RF data were acquired both in plane-wave (PW) and focused mode and beamformed using the UltraSound ToolBox (USTB). The DAS beamforming method was employed for PW data, whereas the focused data were reconstructed using F-DMAS. Various dynamic ranges (dR) were employed to create the final 8-bit PW DAS images (dR = 55, 65, 75, 85 dB) while an automatic dR was employed to optimize focused F-DMAS images. A Pix2Pix GAN architecture was designed to formulate the task of beamforming as the translation from one domain (PW DAS image) to another (focused F-DMAS image). Our GAN employed a UNet as the generator and a 3-layer fully convolutional PatchGAN as the discriminator. The proposed GAN architecture shows promising results, generating a GAN image comparable to the F-DMAS image, i.e., in terms of SSIM (0.5183 +/- 0.0437 and 0.5152 +/- 0.0519 for GAN images vs DAS images and F-DMAS images vs DAS images). Overall, our GAN enhances image quality and simulates focused F-DMAS beamforming starting from a PW DAS image without needing to access the raw RF data, which is typically unavailable with clinical ultrasound devices

On the Flow Past an Array of Two-Dimensional Street Canyons Between Slender Buildings

The flow above an idealized, two-dimensional series of parallelepipedal buildings is examined with the aim of investigating how the building width (W) to height (H) aspect ratio affects the turbulence in the roughness sublayer and the ventilation of the underlying street canyons. We compare the case of buildings with a squared section (AR(B) = W/H = 1.0) with a configuration with slender buildings (AR(B)= 0.1) both in the case of unit canyon width (D) to height (H) aspect ratio (AR(C) = D/H = 1) and in the case of AR(C) = 2. The former corresponds to skimming flow and the latter to wake-interference regime. Measurements are performed in a water channel, measuring velocity on a vertical mid-plane using a particle-image velocimetry technique. The mean flow, its second-order turbulence statistics, the exchange fluxes, and the integral time scales are investigated, with results showing that slender buildings enhance turbulence production and yield larger integral time scales in the region just above the building roof. Namely, in the skimming-flow and wake-interference regimes, the maximum vertical velocity variance is more than doubled and increased by 50%, respectively. The combined analysis of the turbulence production fields and the snapshots of the flow during sweep and ejection events demonstrate that the shear layer between the canyon and the external flow is significantly more unstable with slender buildings, mainly because the damping effect of the vertical velocity fluctuations from the flat roof of the upwind building is substantially missing. Consequently, a larger (downstream) portion of the interface is prone to the direct interaction of the external flow structures. The higher turbulence intensity promotes the ventilation at the canyon interface, which is increased by a factor of two in the skimming-flow regime and a factor of 1.26 in the wake-interference regime. In summary, the present experiments show that the effect of the reduced building aspect ratio is particularly significant when the urban canopy consists of narrow canyons. The result is of interest since narrow street canyons are typically bounded by slender buildings in the urban texture of the old European city centres

Generalization of a deep learning network for beamforming and segmentation of ultrasound images

Recently, deep neural networks (DNNs) for beamforming and segmenting plane-wave ultrasound images have been proposed. The promising results obtained so far focus on segmenting anechoic, almost circular structures using one architecture trained on a large dataset. We present a study of DNNs generalizability for beamforming and segmenting structures of various shapes and echogenicity. Three different encoder architectures (i.e. VGG13/16/19) and target images with standard dynamic range (dR = 60 dB, E60) or an automatically determined dR (Eauto) were compared. Field II was used to simulate 6560 images (with hyperechoic, hypoechoic, anechoic and mixed targets) using random bunches of ellipses to generate different shapes for DNN training. The test set included 816 simulated images, 21 images of a phantom (CIRS040GSE) and 24 images of the carotid artery. The DNN architecture has 1 encoder and 2 decoders, for segmentation and beamforming, based on the UNet. Using the VGG19 trained with Eauto images, a considerable improvement was achieved when compared to other architectures, especially when performing tests on experimental data. Overall, the promising results obtained encourage us to further investigate the use of DNNs for beamforming and segmentation, with the aim to improve the performance and generalize their use for specific ultrasound imaging applications