Three dimensional (3D) ultrasound has become an increasingly popular medical imaging tool over the last decade. It offers significant advantages over Two Dimensional (2D) ultrasound, such as improved accuracy, the ability to display image planes that are physically impossible with 2D ultrasound, and reduced dependence on the skill of the sonographer. Among 3D medical imaging techniques, ultrasound is the only one portable enough to be used by first responders, on the battlefield, and in rural areas. There are three basic methods of acquiring 3D ultrasound images. In the first method, a 2D array transducer is used to capture a 3D volume directly, using electronic beam steering. This method is mainly used for echocardiography. In the second method, a linear array transducer is mechanically actuated, giving a slower and less expensive alternative to the 2D array. The third method uses a linear array transducer that is moved by hand. This method is known as freehand 3D ultrasound. Whether using a 2D array or a mechanically actuated linear array transducer, the position and orientation of each image is known ahead of time. This is not the case for freehand scanning. To reconstruct a 3D volume from a series of 2D ultrasound images, assumptions must be made about the position and orientation of each image, or a mechanism for detecting the position and orientation of each image must be employed. The most widely used method for freehand 3D imaging relies on the assumption that the probe moves along a straight path with constant orientation and speed. This method requires considerable skill on the part of the sonographer. Another technique uses features within the images themselves to form an estimate of each image's relative location. However, these techniques are not well accepted for diagnostic use because they are not always reliable. The final method for acquiring position and orientation information is to use a six Degree-of-Freedom (6 DoF) tracking system. Commercially available 6 DoF tracking systems use magnetic fields, ultrasonic ranging, or optical tracking to measure the position and orientation of a target. Although accurate, all of these systems have fundamental limitations in that they are relatively expensive and they all require sensors or transmitters to be placed in fixed locations to provide a fixed frame of reference. The goal of the work presented here is to create a probe tracking system for freehand 3D ultrasound that does not rely on any fixed frame of reference. This system tracks the ultrasound probe using only sensors integrated into the probe itself. The advantages of such a system are that it requires no setup before it can be used, it is more portable because no extra equipment is required, it is immune from environmental interference, and it is less expensive than external tracking systems. An ideal tracking system for freehand 3D ultrasound would track in all 6 DoF. However, current sensor technology limits this system to five. Linear transducer motion along the skin surface is tracked optically and transducer orientation is tracked using MEMS gyroscopes. An optical tracking system was developed around an optical mouse sensor to provide linear position information by tracking the skin surface. Two versions were evaluated. One included an optical fiber bundle and the other did not. The purpose of the optical fiber is to allow the system to integrate more easily into existing probes by allowing the sensor and electronics to be mounted away from the scanning end of the probe. Each version was optimized to track features on the skin surface while providing adequate Depth Of Field (DOF) to accept variation in the height of the skin surface. Orientation information is acquired using a 3 axis MEMS gyroscope. The sensor was thoroughly characterized to quantify performance in terms of accuracy and drift. This data provided a basis for estimating the achievable 3D reconstruction accuracy of the complete system. Electrical and mechanical components were designed to attach the sensor to the ultrasound probe in such a way as to simulate its being embedded in the probe itself. An embedded system was developed to perform the processing necessary to translate the sensor data into probe position and orientation estimates in real time. The system utilizes a Microblaze soft core microprocessor and a set of peripheral devices implemented in a Xilinx Spartan 3E field programmable gate array. The Xilinx Microkernel real time operating system performs essential system management tasks and provides a stable software platform for implementation of the inertial tracking algorithm. Stradwin 3D ultrasound software was used to provide a user interface and perform the actual 3D volume reconstruction. Stradwin retrieves 2D ultrasound images from the Terason t3000 portable ultrasound system and communicates with the tracking system to gather position and orientation data. The 3D reconstruction is generated and displayed on the screen of the PC in real time. Stradwin also provides essential system features such as storage and retrieval of data, 3D data interaction, reslicing, manual 3D segmentation, and volume calculation for segmented regions. The 3D reconstruction performance of the system was evaluated by freehand scanning a cylindrical inclusion in a CIRS model 044 ultrasound phantom. Five different motion profiles were used and each profile was repeated 10 times. This entire test regimen was performed twice, once with the optical tracking system using the optical fiber bundle, and once with the optical tracking system without the optical fiber bundle. 3D reconstructions were performed with and without the position and orientation data to provide a basis for comparison. Volume error and surface error were used as the performance metrics. Volume error ranged from 1.3% to 5.3% with tracking information versus 15.6% to 21.9% without for the version of the system without the optical fiber bundle. Volume error ranged from 3.7% to 7.6% with tracking information versus 8.7% to 13.7% without for the version of the system with the optical fiber bundle. Surface error ranged from 0.319 mm RMS to 0.462 mm RMS with tracking information versus 0.678 mm RMS to 1.261 mm RMS without for the version of the system without the optical fiber bundle. Surface error ranged from 0.326 mm RMS to 0.774 mm RMS with tracking information versus 0.538 mm RMS to 1.657 mm RMS without for the version of the system with the optical fiber bundle. The prototype tracking system successfully demonstrated that accurate 3D ultrasound volumes can be generated from 2D freehand data using only sensors integrated into the ultrasound probe. One serious shortcoming of this system is that it only tracks 5 of the 6 degrees of freedom required to perform complete 3D reconstructions. The optical system provides information about linear movement but because it tracks a surface, it cannot measure vertical displacement. Overcoming this limitation is the most obvious candidate for future research using this system. The overall tracking platform, meaning the embedded tracking computer and the PC software, developed and integrated in this work, is ready to take advantage of vertical displacement data, should a method be developed for sensing it.

This dissertation develops and analyzes several techniques for improving channel estimation and tracking performance in distributed multi-input multi-output (D-MIMO) wireless communication systems. D-MIMO communication systems have been studied for the last decade and are known to offer the benefits of antenna arrays, e.g., improved range and data rates, to systems of single-antenna devices. D-MIMO communication systems are considered a promising technology for future wireless standards including advanced cellular communication systems. This dissertation considers problems related to channel estimation and tracking in D-MIMO communication systems and is focused on three related topics: (i) characterizing oscillator stability for nodes in D-MIMO systems, (ii) the development of an optimal unified tracking framework and a performance comparison to previously considered sub-optimal tracking approaches, and (iii) incorporating independent kinematics into dynamic channel models and using accelerometers to improve channel tracking performance. A key challenge of D-MIMO systems is estimating and tracking the time-varying channels present between each pair of nodes in the system. Even if the propagation channel between a pair of nodes is time-invariant, the independent local oscillators in each node cause the carrier phases and frequencies and the effective channels between the nodes to have random time-varying phase offsets. The first part of this dissertation considers the problem of characterizing the stability parameters of the oscillators used as references for the transmitted waveforms. Having good estimates of these parameters is critical to facilitate optimal tracking of the phase and frequency offsets. We develop a new method for estimating these oscillator stability parameters based on Allan deviation measurements and compare this method to several previously developed parameter estimation techniques based on innovation covariance whitening. The Allan deviation method is validated with both simulations and experimental data from low-precision and high-precision oscillators. The second part of this dissertation considers a D-MIMO scenario with $N_t$ transmitters and $N_r$ receivers. While there are $N_t imes N_r$ node-to-node pairwise channels in such a system, there are only $N_t + N_r$ independent oscillators. We develop a new unified tracking model where one Kalman filter jointly tracks all of the pairwise channels and compare the performance of unified tracking to previously developed suboptimal local tracking approaches where the channels are not jointly tracked. Numerical results show that unified tracking tends to provide similar beamforming performance to local tracking but can provide significantly better nullforming performance in some scenarios. The third part of this dissertation considers a scenario where the transmit nodes in a D-MIMO system have independent kinematics. In general, this makes the channel tracking problem more difficult since the independent kinematics make the D-MIMO channels less predictable. We develop dynamics models which incorporate the effects of acceleration on oscillator frequency and displacement on propagation time. The tracking performance of a system with conventional feedback is compared to a system with conventional feedback and local accelerometer measurements. Numerical results show that the tracking performance is significantly improved with local accelerometer measurements.