Browsing by Subject "Adaptive imaging"

This dissertation investigates the clinical utility of adaptive ultrasonic frequency selection using principles of spatial coherence. Presently, the status quo for the selection of settings on an ultrasound scanner leaves much room for improvement. Time constraints and the prevalence of injury to sonographers limit the degree to which scanner settings may optimized for a given patient or acoustic window. One such setting is the frequency, which balances the levels of acoustic noise and resolution. Manufacturers usually include a low- and high-frequency option, but these settings are coarse relative to the overall transducer bandwidth, and leave little room for personalized scanning of each patient. The goal of adaptive frequency selection is to maximize image quality by selecting an optimal frequency at a per-image basis. Automating the process of selecting scanner settings requires a user-independent image quality metric, and conventional metrics, such as contrast and contrast-to-noise ratio (CNR), often require user input to draw a multiple regions-of-interest (ROIs) on the image. This is time consuming as well as prone to further user bias. However, spatial coherence-based metrics, a category of image quality metrics developed by our group and others for use in medical ultrasound, avoids these issues while remaining sensitive to acoustic noise.

This work is presented in four chapters. Chapter 1 provides a review of spatial coherence in medical ultrasound, including image quality characterization techniques, beamforming methods, and a discussion of potential future areas of exploration.

Chapter 2 details a simulation study in which spatial coherence is used to predict the loss in imaging contrast as well as separate the effects of different acoustic noise sources. Results showed agreement between theory and simulations for a multitude of image quality metrics when considering two types of noise: incoherent noise and partially coherent noise. Minimal error was seen between coherence-predicted contrast loss and measured contrast loss. This presented framework shows promise to improve the evaluation of noise reduction strategies.

Chapter 3 details the development of an efficient method to collect frequency-dependent spatial coherence information by leveraging a type of coded transmission known as a chirp. Chirp-collected measurements of coherence were compared to those acquired by individually transmitted conventional pulses over a range of frequencies. Results from ex vivo and in vivo acquisitions showed that chirps replicated the mean coherence in a region-of-interest. This work indicates that the use of chirps is a viable strategy to expedite the collection of frequency-dependent spatial coherence, presenting an avenue for real-time adaptive frequency selection.

Lastly, Chapter 4 details the clinical validation of adaptive frequency selection through a reader study. Image quality improvements shown with coherence-based metrics were corroborated by reader outcomes scores for overall quality, border detection, and target conspicuity. Statistical testing revealed a significant difference between the rated image quality of adaptive images and transducer default images. These results suggest that an optimal frequency can be automatically selected for target detection.

Adaptive ultrasound systems, designed to automatically and dynamically tune imaging parameters based on image quality feedback, represent a promising solution for reducing the user-dependence of ultrasound. The efficacy of such systems, however, depends on the ability to accurately and reliably measure in vivo image quality with minimal user interaction -- a task for which existing image quality metrics are ill-suited. This dissertation explores the application of backscatter spatial coherence as an alternative image quality metric for adaptive imaging. Adaptive ultrasound methods applying spatial coherence feedback are evaluated in the context of three different applications: 1) the automated selection of acoustic output, 2) model-based clutter suppression in B-mode imaging, and 3) adaptive wall filtering in color flow imaging.

A novel image quality metric, known as the lag-one coherence (LOC), was introduced along with the theory that relates LOC to channel noise and the conventional image quality metrics of contrast and contrast-to-noise ratio (CNR). Simulation studies were performed to validate this theory and compare the variability of LOC to that of conventional metrics. In addition, matched measurements of LOC, contrast, CNR, and temporal correlation were obtained from harmonic phantom and liver images formed with varying mechanical index (MI) to assess the feasibility of adaptive acoustic output selection using LOC feedback. Measurements of LOC in simulation and phantom demonstrated lower variability in LOC relative to contrast and CNR over a wide range of clinically-relevant noise levels. This improved stability was supported by in vivo measurements of LOC that showed increased monotonicity with changes in MI compared to matched measurements of contrast and CNR (88.6% and 85.7% of acquisitions, respectively). The sensitivity of LOC to temporally-stable acoustic noise was evidenced by positive correlations between LOC and contrast (r=0.74) and LOC and CNR (r=0.66) at high acoustic output levels in the absence of thermal noise. Together, these properties translated to repeatable characterization of patient-specific trends in image quality that were able to demonstrate feasibility for the automated selection of acoustic output using LOC and its application for in vivo image quality feedback.

In a second study, a novel model-based adaptive imaging method called Lag-one Spatial Coherence Adaptive Normalization, or LoSCAN, was explored as a means to locally estimate and compensate for the contribution of spatially incoherent clutter from conventional delay-and-sum (DAS) images using measurements of LOC. Suppression of incoherent clutter by LoSCAN resulted in improved image quality without introducing many of the artifacts common to other coherence-based beamforming methods. In simulations with known targets and added channel noise, LoSCAN was shown to restore native contrast and increase DAS dynamic range by as much as 10-15 dB. These improvements were accompanied by DAS-like speckle texture along with reduced focal dependence and artifact compared to other coherence-based methods. Under in vivo liver and fetal imaging conditions, LoSCAN resulted in increased generalized contrast-to-noise ratio (gCNR) in nearly all matched image pairs (N = 366) with average increases of 0.01, 0.03, and 0.05 in good, fair, and poor quality DAS images, respectively, and overall changes in gCNR from -0.01 to 0.20, contrast-to-noise ratio (CNR) from -0.05 to 0.34, contrast from -9.5 to -0.1 dB, and texture mu/sigma from -0.37 to -0.001 relative to DAS.

The application of spatial coherence image quality feedback was further investigated in the context of color flow imaging to perform adaptive wall filter selection. The relationship between velocity estimation accuracy and spatial coherence was demonstrated in simulations with varying flow and clutter conditions. This relationship was leveraged to implement a novel method for coherence-based adaptive wall filtering, which selects a unique wall filter at each imaging location based on local clutter and flow properties captured by measurements of LOC and short-lag spatial coherence (SLSC). In simulations and phantom studies with known flow velocities and clutter, coherence-adaptive wall filtering was shown to reduce velocity estimation bias by suppressing low frequency energy from clutter and minimizing the attenuation of flow signal, while maintaining comparable velocity estimation variance relative to conventional wall filtering. These properties translated to in vivo color flow images of liver and fetal vessels that were able to provide direct visualization of low and high velocity flow under various cluttered imaging conditions without the manual tuning of wall filter cutoffs and/or priority thresholds.

Together, these studies present several promising applications of spatial coherence that are fundamentally unique from existing methods in ultrasound. Results in this work support the broad application of spatial coherence feedback to perform patient, window, and target-specific adjustment of imaging parameters to improve the usability and efficacy of diagnostic ultrasound.

Traditional radar imaging systems are implemented using the focal plane

technique, steering beam antennas, or synthetic aperture imaging. These conventional

methods require either a large number of sensors to form a focal plane array similar to the

idea of an optical camera, or a single transceiver mechanically scanning the field of view.

The former results in expensive systems whereas the latter results in long acquisition time.

Computational imaging methods are widely used for the ability to acquire information

beyond the recorded pixels, thus are ideal options for reducing the number of radar

sensors in radar imaging systems. Novel antenna designs such as the frequency diverse

antennas are capable of optimizing antennas for computational imaging algorithms. This

thesis tries to find a solution for improving the efficiency of radar imaging using a method

that combines computational imaging and novel antenna designs. This thesis first

proposes two solutions to improve the two aspects of the tradeoff respectively, i.e. the

number of sensors and mechanical scanning. A method using time-of-flight imaging

algorithm with a sparse array of antennas is proposed as a solution to reduce the number

of sensors required to estimate a reflective surface. An adaptive algorithm based on the

Bayesian compressive sensing framework is proposed as a solution to minimize

mechanical scanning for synthetic aperture imaging systems. The thesis then explores the

feasibility to further improve radar imaging systems by combining computational

imaging and antenna design methods as a solution. A rapid prototyping method for

manufacturing custom-designed antennas is developed for implementing antenna

designs quickly in a laboratory environment. This method has facilitated the design of a

frequency diverse antenna based on a leaky waveguide design, which can be used under

computational imaging framework to perform 3D imaging. The proposed system is

capable of performing imaging and target localization using only one antenna and

without mechanical scanning, thus is a promising solution to ultimately improve the

efficiency for radar imaging.