Browsing by Subject "Spatial coherence"

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.

Diagnostic ultrasound imaging is sometimes unable to yield clinically useful data. This is caused by the presence of body walls. This thesis examines how body walls impacts the propagation of ultrasound in the human body and searches for ways to improve image quality.Previous works have demonstrated remarkable improvements in tissue stiffness quantification with the use of elevated Mechanical Index (MI) during ultrasonic harmonic shear wave elasticity imaging (SWEI). Since ultrasound SWEI sequences consist of a long push pulse and a short tracking pulse, it remains unclear which one of the two pulses is impacted more by the body wall and benefits more from elevated MI. In Chapter 3, an opposing window experiment is devised and built to isolate the impacts of the body wall on push and track beams. Track beams are found to be more affected by the presence of body walls and to benefit from higher MI transmits. In Chapter 4, 3D nonlinear ultrasound simulations and experimental measurements were used to estimate the range of in situ pressures that can occur during transcutaneous abdominal imaging, and to identify the sources of error when estimating in situ peak rarefaction pressures (PRP) using linear derating as specified by the MI guideline. Using simulations, it was found that for a large transmit aperture (F/1.5) MI consistently overestimated in situ PRP by 20-48%, due primarily to phase aberration. For a medium transmit aperture (F/3), the MI accurately estimated the in situ PRP to within 8%. For a small transmit aperture (F/5), MI consistently underestimated the in situ PRP by 32-50%, with peak locations occurring 1-2 cm before the focal depth, often within the body wall itself. The large variability across body wall samples and focal configurations demonstrates the limitations of the simplified linear derating scheme. The results suggest that patient specific in situ PRP estimation would allow for increases in transmit pressures, particularly for tightly focused beams, to improve diagnostic image quality while ensuring patient safety. Tissue harmonic signal quality has been shown to improve with elevated acoustic pressure. The peak rarefaction pressure (PRP) for a given transmit, however, is limited by the FDA guideline for the mechanical index (MI). In Chapter 4, We demonstrated that the MI overestimates in situ PRP for tightly focused beams in vivo due primarily to phase aberration. In Chapter 5, we evaluate two spatial coherence-based image quality metrics, short-lag spatial coherence (SLSC) and harmonic short-lag spatial coherence (HSC), as proxy estimates for phase aberration and assess their correlation with in situ PRP in simulations and experimentally when imaging through abdominal body walls. We demonstrate strong correlation between both spatial coherence-based metrics with in situ PRP (r2 = 0.77 for HSC, r2 = 0.67 for SLSC), an observation that could be leveraged in the future for patient-specific selection of acoustic output.

The US Food and Drug Administration (FDA) provides guidelines for maximum acoustic output for diagnostic ultrasound imaging through metrics such as intensity, Mechanical Index (MI), and Thermal Index (TI). However, even within these guideline values, if the acoustic exposure levels used do not benefit image quality, they represent an unnecessary risk to patient safety. Ultrasound users have control over many settings, including ones that directly and indirectly change the acoustic output, and the user is largely responsible for deciding how to manage the safety risks based on on-screen displays of MI and TI. The FDA and professional societies advise users to observe the ALARA (as low as reasonably achievable) principle with regard to acoustic exposure, but several studies have shown that the majority of ultrasound users do not monitor safety indices. To address this discrepancy, an adaptive ultrasound method has been developed that could be used to automatically adjust acoustic exposure in real-time in response to image quality feedback.

In this work, MI was used as the measure of acoustic output, and lag-one coherence (LOC) was the image quality feedback parameter. LOC is the average spatial correlation between backscattered echoes received on neighboring ultrasound transducer array elements. Previous work has shown that LOC is predictive of local signal-to-noise ratio (SNR), and that it is sensitive to incoherent acoustic clutter and temporally-incoherent noise. During B-mode ultrasound imaging, LOC was monitored as MI was adjusted, and the data consistently formed a sigmoid shape. At lower MI values, LOC increased quickly with increasing output, but at higher MI values, increases in acoustic output often did not translate to increased image quality. This relationship was consistent for other image quality metric-versus-MI data, including contrast, contrast-to-noise ratio (CNR), and generalized contrast-to-noise ratio (gCNR).

The MI value at which the LOC began to approach an asymptote was denoted the "ALARA MI.” In this work, ALARA MI values were calculated for a range of obstetric imaging targets that are scanned during anatomy exams, including placenta, fetal abdomen, heart, kidney, bladder, stomach, ventricles, and extremities. The placenta data had the lowest median ALARA MI (0.59) and the fetal heart data had the highest (0.83). There was considerable variation in the ALARA MI values, even for the same participant, so frequent updates to the acoustic output settings would be recommended during live scanning. Additionally, the correlation between the ALARA MI and the LOC achieved at that setting was found to be very weak.

Initially, a fixed region of interest (ROI) was used for acoustic output optimization. This would require the structure to be aligned with the ROI and the optimization process to be manually initiated. Considering the demands on the sonographer during clinical ultrasound scanning, it would not be feasible to add these steps every time a new imaging window is used. An automated ROI-selection algorithm was developed that would allow the entire adaptive acoustic output selection process to happen without user input. This algorithm used envelope-detected B-mode image data that are readily available on clinical scanners to identify where to perform the optimization. Testing on clinical placenta and fetal abdomen data showed that it reliably recommended good regions for acoustic output optimization.

The results of this work suggest that near-maximum image quality can be achieved with a lower acoustic output level than is currently used clinically, and automated acoustic output adjustments could enable more consistent observation of the ALARA principle. In the future, this could be extended to other ultrasound modes, such as Doppler imaging, and additional acoustic output metrics could be incorporated.

Preliminary assessment of temporal SNR was performed, and a wide range of temporal SNR levels is associated with the ALARA MI settings found in this study. Future work may also investigate using a temporal SNR threshold to determine the ALARA output level. Spatial coherence measurements, such as LOC, reflect the degradation in image quality from acoustic clutter and electronic noise, and temporal coherence is affected by motion and electronic noise. Although motion is an important factor in clinical imaging, temporal coherence does not require access to channel data, so these calculations would be easier to implement on existing scanners. These trade-offs are important to consider when attempting to capture the underlying electronic noise level to inform an automated ALARA ultrasound system.