Image formation algorithms for a low-cost, freehand ultrasound scanner

Abstract

Every day and every minute, a woman dies due to complications during pregnancy or childbirth. Approximately 303,000 deaths, related to childbirth, have been recorded in 2015, of which 99% occurred in developing countries. Reducing preventable maternal mortality crucially depends on ensuring that women have access to basic health care services; before, during and after childbirth. Maternal mortality risk factors could be detected by using ultrasound imaging, but these devices are very expensive hence not affordable for healthcare providers in developing countries. Conventional ultrasound devices use multi-element piezoelectric transducer array to produce a 2D or a 3D image, while only a single piezoelectric element transducer has been used in the proposed scanner. This probe design greatly reduces the hardware complexity, power consumption and beamforming computational load, hence bringing the manufacturing cost down to less than $100. This will make the device affordable to the developing countries. However, by reducing the number of elements in the transducer to only one, has made the reconstruction of a geometrically correct image a challenging task.The algorithms and techniques need to be developed that will calculate the position of the ultrasound probe without the use of any position sensor. The research presented in this thesis describes the design and implementation of the image formation techniques for the proposed low-cost ultrasound scanner. There are two techniques of ultrasound scanning namely polar scan and linear scan. For a polar scan, the ultrasound probe is rotated over the skin across one of the axes of the probe. To create a geometrically correct image of a polar scan, the angular data is measured with the help of micro-electromechanical systems (MEMS) gyroscope which is built in the probe assembly. The result has been shown in the thesis for the phantom and in-vivo data for a polar scan. The second type of scan is a free-hand linear scan in which the probe is manually scanned over the desired region of interest (ROI), by translating it from one direction to the other. The collected raw echo data and it’s relative orientation data measured with the MEMS gyroscope was transmitted to the computer via Wi-Fi for further processing. A novel position estimation algorithm is proposed which measures the decorrelation between successive scanlines to estimate probe’s velocity. With the aid of an Unscented Kalman Filter (UKF), this is used to estimate the probe’s position. Clustering is used to make the position estimation algorithm to be robust in more complex scenes and variable tissue properties in human scans. For the first time, gaussian mixture model (GMM), spatial fuzzy c-means (SFCM) and k-means clustering techniques have been exploited on a 1-D raw echo data. The mean percentage error for estimating probe’s position without using any clustering technique is 28.7%. This gets reduced to 12.3% by using k-means, 15% by using SFCM and 4.2% by using GMM. The position estimation data is combined with the orientation data using the fusion algorithm which is used to reconstruct a geometrically correct 2D image. The in-vivo experiments have shown promising results for clinical diagnostic and with further work this technique has potential to deliver a very low cost ultrasound probe design for use in the developing world