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The widespread development of new ultrasound image formation techniques has created a need for a standardized methodology for comparing the resulting images. Traditional methods of evaluation use quantitative metrics to assess the imaging performance in specific tasks such as point resolution or lesion detection. Quantitative evaluation is complicated by unconventional new methods and non-linear transformations of the dynamic range of data and images. Transformationindependent image metrics have been proposed for quantifying task performance. However, clinical ultrasound still relies heavily on visualization and qualitative assessment by expert observers. We propose the use of histogram matching to better assess differences across image formation methods. We briefly demonstrate the technique using a set of sample beamforming methods and discuss the implications of such image processing. We present variations of histogram matching and provide code to encourage application of this method within the imaging community.Focused ultrasound (FUS) therapies induce therapeutic effects in localized tissues using either temperature elevations or mechanical stresses caused by an ultrasound wave. During an FUS therapy, it is crucial to continuously monitor the position of the FUS beam in order to correct for tissue motion and keep the focus within the target region. Toward the goal of achieving real-time monitoring for FUS therapies, we have developed a method for the real-time visualization of an FUS beam using ultrasonic backscatter. The intensity field of an FUS beam was reconstructed using backscatter from an FUS pulse received by an imaging array and then overlaid onto a B-mode image captured using the same imaging array. The FUS beam visualization allows one to monitor the position and extent of the FUS beam in the context of the surrounding medium. Variations in the scattering properties of the medium were corrected in the FUS beam reconstruction by normalizing based on the echogenicity of the coaligned B-mode image. On average, normalizing by echogenicity reduced the mean square error between FUS beam reconstructions in nonhomogeneous regions of a phantom and baseline homogeneous regions by 21.61. FUS beam visualizations were achieved, using a single diagnostic imaging array as both an FUS source and an imaging probe, in a tissue-mimicking phantom and a rat tumor in vivo with a frame rate of 25-30 frames/s.Pancreatic cancer is a malignant form of cancer with one of the worst prognoses. The poor prognosis and resistance to therapeutic modalities have been linked to TP53 mutation. Pathological examinations, such as biopsies, cannot be frequently performed in clinical practice; therefore, noninvasive and reproducible methods are desired. However, automatic prediction methods based on imaging have drawbacks such as poor 3D information utilization, small sample size, and ineffectiveness multi-modal fusion. In this study, we proposed a model-driven multi-modal deep learning scheme to overcome these challenges. A spiral transformation algorithm was developed to obtain 2D images from 3D data, with the transformed image inheriting and retaining the spatial correlation of the original texture and edge information. The spiral transformation could be used to effectively apply the 3D information with less computational resources and conveniently augment the data size with high quality. Moreover, model-driven items were designed to introduce prior knowledge in the deep learning framework for multi-modal fusion. The model-driven strategy and spiral transformation-based data augmentation can improve the performance of the small sample size. A bilinear pooling module was introduced to improve the performance of fine-grained prediction. The experimental results show that the proposed model gives the desired performance in predicting TP53 mutation in pancreatic cancer, providing a new approach for noninvasive gene prediction. The proposed methodologies of spiral transformation and model-driven deep learning can also be used for the artificial intelligence community dealing with oncological applications. Our source codes with a demon will be released at https//github.com/SJTUBME-QianLab/SpiralTransform.We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++) that contains "4,372" images with "1.51 million" annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations, making it a very challenging dataset. Additionally, the dataset consists of a rich set of annotations at both image-level and head-level. Several recent methods are evaluated and compared on this dataset. The dataset can be downloaded from http//www.crowd-counting.com. Furthermore, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only high-confidence residuals in the refinement path. The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors.One-shot neural architecture search (NAS) has recently become mainstream in the NAS community because it significantly improves computational efficiency through weight sharing. However, the supernet training paradigm in one-shot NAS introduces catastrophic forgetting. To overcome this problem of catastrophic forgetting, we formulate supernet training for one-shot NAS as a constrained continual learning optimization problem such that learning the current architecture does not degrade the validation accuracy of previous architectures. The key to solving this constrained optimization problem is a novelty search based architecture selection (NSAS) loss function that regularizes the supernet training by using a greedy novelty search method to find the most representative subset. We applied the NSAS loss function to two one-shot NAS baselines and extensively tested them on both a common search space and a NAS benchmark dataset. We further derive three variants based on the NSAS loss function, the NSAS with depth constrain (NSAS-C) to improve the transferability, and NSAS-G and NSAS-LG to handle the situation with a limited number of constraints. The experiments on the common NAS search space demonstrate that NSAS and it variants improve the predictive ability of supernet training in one-shot NAS baselines.

The purpose of this study was to assess whether accelerometry effectively reflects muscle vibrations measured with ultrafast ultrasonography.

Vibration characteristics initiated on the vastus lateralis muscle by an impactor were compared when assessed with accelerometry and ultrasonography. Continuous wavelet transforms and statistical parametric mapping (SPM) were performed to identify discrepancies in vibration power over time and frequency between the two devices.

The SPM analysis revealed that the accelerometer underestimated the muscle vibration power above 50 Hz during the first 0.06 seconds post impact. Furthermore, the accelerometer overestimated the muscle vibration power under 20 Hz, from 0.1 seconds after the impact. Linear regression revealed that the thicker the subcutaneous fat localized under the accelerometer, themore the muscle vibration frequency and damping were underestimated by the accelerometer.

The skin and the fat tissues acted like a low-pass filter above 50 Hz and oscillated in a less damped manner than the muscle tissue under 20 Hz.

To eliminate some artifacts caused by the superficial tissues and assess the muscle vibration characteristics with accelerometry, it is suggested to 1) high-pass filter the acceleration signal at a frequency of 20 Hz, under certain conditions, and 2) include participants with less fat thickness. Therefore, the subcutaneous thickness must be systematically quantified under each accelerometer location to clarify the differences between subjects and muscles.

To eliminate some artifacts caused by the superficial tissues and assess the muscle vibration characteristics with accelerometry, it is suggested to 1) high-pass filter the acceleration signal at a frequency of 20 Hz, under certain conditions, and 2) include participants with less fat thickness. Therefore, the subcutaneous thickness must be systematically quantified under each accelerometer location to clarify the differences between subjects and muscles.Human speech perception can be described as Bayesian perceptual inference but how are these Bayesian computations instantiated neurally? We used magnetoencephalographic recordings of brain responses to degraded spoken words and experimentally manipulated signal quality and prior knowledge. We first demonstrate that spectrotemporal modulations in speech are more strongly represented in neural responses than alternative speech representations (e.g. spectrogram or articulatory features). Critically, we found an interaction between speech signal quality and expectations from prior written text on the quality of neural representations; increased signal quality enhanced neural representations of speech that mismatched with prior expectations, but led to greater suppression of speech that matched prior expectations. This interaction is a unique neural signature of prediction error computations and is apparent in neural responses within 100 ms of speech input. https://www.selleckchem.com/products/biricodar.html Our findings contribute to the detailed specification of a computational model of speech perception based on predictive coding frameworks.Continuous pulse oximetry monitoring in stable patients with bronchiolitis is discouraged by national guidelines in order to reduce overuse, yet wide practice variation exists among hospitals. Understanding the association between monitoring overuse and hospital unit-level factors may identify areas for improvement. Conducted at 25 sites from the Pediatric Research in Inpatient Settings (PRIS) Network's Eliminating Monitoring Overuse (EMO) study, this substudy used data from 2,366 in-person observations of pulse oximetry use in patients with bronchiolitis to determine whether hospital unit-level factors were associated with variation in pulse oximetry use for patients in whom continuous monitoring is not indicated. Hospital units were classified by bronchiolitis admission burden. Monitoring rates were analyzed in a mixed-effects model that accounted for variation in baseline monitoring rates among hospitals and adjusted for covariates significantly associated with continuous pulse oximetry monitoring use in the primary study's analysis. Low burden units ( less then 10% of total admissions) had a 2.16-fold increased odds of pulse oximetry overuse compared to high burden units (≥40% of total admissions) (95% CI, 1.27-3.69; P = .01). These results suggest that units caring for a lower percentage of patients with bronchiolitis are more likely to overuse pulse oximetry despite national guidelines.