Receive Beamforming in Medical Ultrasound—A Review of Aperture Data Processing

This review focuses on ultrasound beamforming methods, a crucial step in the process of ultrasound image formation. With the shift towards software-based receive beamforming in research and commercial settings, a number of limiting factors around the application of advanced beamforming algorithms have been removed.  Now, the opportunities for image quality improvements are only limited by available compute resources. This has lead to a plethora of works presenting and investigating novel beamforming methods. We review the recent advances in receive beamforming, specifically focused on methods that perform aperture data processing—the processing of the received echo signals after application of appropriate delays—and can therefore replace summation in delay-and-sum.  After presenting and contrasting the methods, we compare and discuss their respective advantages and limitations. Our goal is to give the reader an intuition on their working principles, show the relations between the different methods, and serve as a starting point for further study of the literature or implementations. The code we used to perform the comparisons is available at https://github.com/goeblr/rxbf_review </p

An Observer-Based Fusion Method using Multicore Optical Shape Sensors and Ultrasound Images for Magnetically-Actuated Catheters

Minimally invasive surgery involves using flexible medical instruments such as endoscopes and catheters. Magnetically actuated catheters can provide improved steering precision over conventional catheters. However, besides the actuation method, an accurate tip position is required for precise control of the medical instruments. In this study, the tip position obtained from transverse 2D ultrasound images and multicore optical shape sensors are combined using a robust sensor fusion algorithm. The tip position is tracked in the ultrasound images using a template-based tracker and a convolutional neural network based tracker, respectively. Experimental results for a rhombus path are presented, where data obtained from both tracking sources are fused using Luenberger and Kalman state estimators. The mean and standard deviation of the Euclidean error for the Luenberger observer is 0.2+-0.11 [mm] whereas for the Kalman filter it is 0.18+-0.13 [mm], respectively