Ultrasound basics

Low-cost depth/IMU intelligent sensor fusion for indoor robot navigation

This paper presents a mobile robot platform, which performs both indoor and outdoor localization based on an intelligent low-cost depth–inertial fusion approach. The proposed sensor fusion approach uses depth-based localization data to enhance the accuracy obtained by the inertial measurement unit (IMU) pose data through a depth–inertial fusion. The fusion approach is based on feedforward cascade correlation networks (CCNs). The aim of this fusion approach is to correct the drift accompanied by the use of the IMU sensor, using a depth camera. This approach also has the advantage of maintaining the high frequency of the IMU sensor and the accuracy of the depth camera. The estimated mobile robot dynamic states through the proposed approach are deployed and examined through real-time autonomous navigation. It is shown that using both the planned path and the continuous localization approach, the robot successfully controls its movement toward the destination. Several tests were conducted with different numbers of layers and percentages of the training set. It is shown that the best performance is obtained with 12 layers and 80% of the pose data used as a training set for CCN. The proposed framework is then compared to the solution based on fusing the information given by the XSens IMU–GPS sensor and the Kobuki robot built-in odometry solution. As demonstrated in the results, an enhanced performance was achieved with an average Euclidean error of 0.091 m by the CCN, which is lower than the error achieved by the artificial neural network by 56%.

Mitral Valve Segmentation Using Robust Nonnegative Matrix Factorization

Analyzing and understanding the movement of the mitral valve is of vital importance in cardiology, as the treatment and prevention of several serious heart diseases depend on it. Unfortunately, large amounts of noise as well as a highly varying image quality make the automatic tracking and segmentation of the mitral valve in two-dimensional echocardiographic videos challenging. In this paper, we present a fully automatic and unsupervised method for segmentation of the mitral valve in two-dimensional echocardiographic videos, independently of the echocardiographic view. We propose a bias-free variant of the robust non-negative matrix factorization (RNMF) along with a window-based localization approach, that is able to identify the mitral valve in several challenging situations. We improve the average f1-score on our dataset of 10 echocardiographic videos by 0.18 to a f1-score of 0.56.

Externally triggered release of growth factors - A tissue regeneration approach

Tissue regeneration aims to achieve functional restoration following injury by creating an environment to enable the body to self-repair. Strategies for regeneration rely on the introduction of biomaterial scaffolding, cells and bioactive molecules into the body, at or near the injury site. Of these bioactive molecules, growth factors (GFs) play a pivotal role in directing regenerative pathways for many cell populations. However, the therapeutic use of GFs has been limited by the complexity of biological injury and repair, and the properties of the GFs themselves, including their short half-life, poor tissue penetration, and off-target side effects. Externally triggered delivery systems have the potential to facilitate the delivery of GFs into the target tissues with considerations of the timing, sequence, amount, and location of GF presentation. This review briefly discusses the challenges facing the therapeutic use of GFs, then, we discuss approaches to externally trigger GF release from delivery systems categorised by stimulation type; ultrasound, temperature, light, magnetic fields and electric fields. Overall, while the use of GFs for tissue regeneration is still in its infancy, externally controlled GF delivery technologies have the potential to achieve robust and effective solutions to present GFs to injured tissues. Future technological developments must occur in conjunction with a comprehensive understanding of the biology at the injury site to ensure translation of promising technologies into real world benefit.

Molecular imaging with bioconjugates in mouse models of cancer

The definition of molecular imaging provided by the Society of Nuclear Medicine is "the visualization, characterization and measurement of biological processes at the molecular and cellular levels in humans and other living systems". This review gives an overview of the technologies available for and the potential benefits from molecular imaging at the preclinical stage. It focuses on the use of imaging probes based on bioconjugates and for reasons of brevity confines itself to discussion of applications in the field of oncology, although molecular imaging can be equally useful in many fields including cardiovascular medicine, neurosciences, infection, and others.