Towards Fast Region Adaptive Ultrasound Beamformer for Plane Wave Imaging Using Convolutional Neural Networks.

Automatic learning algorithms for improving the image quality of diagnostic B-mode ultrasound (US) images have been gaining popularity in the recent past. In this work, a novel convolutional neural network (CNN) is trained using time of flight corrected in-vivo receiver data of plane wave transmit to produce corresponding high-quality minimum variance distortion less response (MVDR) beamformed image. A comprehensive performance comparison in terms of qualitative and quantitative measures for fully connected neural network (FCNN), the proposed CNN architecture, MVDR and Delay and Sum (DAS) using the dataset from Plane wave Imaging Challenge in Ultrasound (PICMUS) is also reported in this work. The CNN architecture can leverage the spatial information and will be more region adaptive during the beamforming process. This is evident from the improvement seen over the baseline FCNN approach and conventional MVDR beamformer, both in resolution and contrast with an improvement of 6 dB in CNR using only zero-angle transmission over the baseline. The observed reduction in the requirement of number of angles to produce similar image metrics can provide a possibility for higher frame rates.

An Angle Independent Depth Aware Fusion Beamforming Approach for Ultrafast Ultrasound Flow Imaging.

In the case of vector flow imaging systems, the most employed flow estimation techniques are the directional beamforming based cross correlation and the triangulation-based autocorrelation. However, the directional beamforming-based techniques require an additional angle estimator and are not reliable if the flow angle is not constant throughout the region of interest. On the other hand, estimates with triangulation-based techniques are prone to large bias and variance at low imaging depths due to limited angle for left and right apertures. In view of this, a novel angle independent depth aware fusion beamforming approach is proposed and evaluated in this paper. The hypothesis behind the proposed approach is that the peripheral flows are transverse in nature, where directional beamforming can be employed without the need of an angle estimator and the deeper flows being non-transverse and directional, triangulation-based vector flow imaging can be employed. In the simulation study, an overall 67.62% and 74.71% reduction in magnitude bias along with a slight reduction in the standard deviation are observed with the proposed fusion beamforming approach when compared to triangulation-based beamforming and directional beamforming, respectively, when implemented individually. The efficacy of the proposed approach is demonstrated with in-vivo experiments.

Introducing Attention Mechanism for EEG Signals: Emotion Recognition with Vision Transformers.

The accurate emotional assessment of humans can prove beneficial in health care, security investigations and human interaction. In contrast to emotion recognition from facial expressions which can prove to be inaccurate, analysis of electroencephalogram (EEG) activity is a more accurate representation of one's state of mind. With advancements in deep learning, various methods are being employed for this task. In this research, importance of attention mechanism in EEG signals is introduced through two vision transformer based methods for the classification of EEG signals on the basis of emotions. The first method utilizes 2-D images generated through continuous wavelet transform (CWT) of the raw EEG signals and the second method directly operates on the raw signal. The publicly available and widely accepted DEAP dataset has been utilized in this research for validating the proposed approaches. The proposed approaches report very high accuracies of 97% and 95.75% using CWT and 99.4% and 99.1% using raw signal for valence and arousal classifications respectively, which clearly highlights the significance of attention mechanism for EEG signals. The proposed methodology also ensures faster training and testing time which suits the clinical purposes.Clinical Relevance- This work establishes a highly accurate algorithm for emotion recognition using EEG signals which has potential applications in music-based therapy.