Estimation and Correction of Aberration in Medical Ultrasound Imaging

The work presented in this thesis is devoted to studying aberration in ultrasound medical imaging, and to provide methods for correcting aberration of ultrasound signals in order to obtain optimum image quality. The thesis is composed of five chapters. All chapters may be read individually. The presented results are generated from simulations. Chapter 1 presents a description of the aberration phenomenon, and a brief discussion of its medical and practical implications. A mathematical description of aberration is introduced by modelling the Green's function for propagation in a heterogeneous medium. In Ch. 2, aberration from a point scatterer in the focus of an array is studied. Aberration is generated by two body wall models, generating weak and strong aberration, emulating the human abdominal wall. The results show that if correctly estimated, aberration can be close to ideally characterized by arrival time and amplitude fluctuations measured across the receive array. Using the arrival time and amplitude fluctuations in a time-delay and amplitude transmit aberration correction filter, produce close to ideal correction of the retransmitted beam. A point source represents a situation which is rarely found in medical ultrasound imaging. A method for estimating aberration from random scatterers is developed in Ch. 3. The method is based on a cross-correlation analysis, and may in general estimate aberration at each frequency component of the received ultrasound signal. Due to the results from Ch. 2, the method is only investigated for a time-delay and amplitude estimate at the center frequency of the signal. The same aberrators as in Ch. 2 are used. The results show that the method does not produce satisfactory estimates of the arrival time and amplitude fluctuations for both aberrators. The backscatter in ultrasound imaging is determined by the width of the focused transmit beam used to obtain the image. Aberration widens the transmit beam, and the back-scattering region may become quite large. Since the human body wall has a certain thickness, the body wall itself generates interference of the signals propagating from different scatterers to the array. This smoothens aberration parameters such as arrival time and amplitude fluctuations, making proper estimation of these unfeasible. Aberration correction is performed as a filter process prior to transmit of the ultrasound beam. This means that aberration estimation/correction methods model aberration as a filter, that is, all effects of aberration are assumed to be fully described in an infinitely thin layer at the array surface. For a point source, this assumption is fulfilled since the signal received on different array elements originates from the same spatial point. For a large scattering region this is generally not true, and the aberration described on a specific array element is dependent of the sum of aberrations generated along different propagation paths from each contributing scatterer. It is then impossible to obtain ideal aberration correction for a specific point in space (usually the focus of the array). A solution to this problem may be sought by iteration of transmit-beam aberration correction (transmit-beam iteration). Transmit-beam iteration is described as a process where an uncorrected transmit-beam is used for an initial estimate of aberration parameters. A new beam with correction is then transmitted, generating a new estimate of the parameters. This process is repeated until some convergence criterion is met. The goal of this process is to reduce the width of the transmit beam, in order for the aberration on a specific receive element to be independent of the scatterers spatial position. Transmit-beam iteration is studied in Ch. 4. Now, eight different aberrators are used, all emulating the human abdominal wall. Here, the estimator developed in Ch. 3 is compared with a similar type of estimator. New insight into the equalities and differences between the estimation methods are provided through transmit-beam iteration considerations. The results show that using a time-delay and amplitude aberration correction filter, both algorithms provide close to ideal aberration correction after two to three transmit-beam iterations for all aberrators. In addition, an earlier developed focus criterion proves to give accurate description of the point of convergence, and the accuracy of the correction. The aberration estimation method described in Chapter. 3, was developed in the frequency domain. In Ch. 5, a time domain implementation is introduced. Necessary assumptions made in the time domain implementation makes the algorithm different from the frequency domain implementation. Since the receive signal in ultrasound imaging is a stochastic variable, estimation of arrival time-delays and amplitudes at the array, is connected with uncertainty. A variance analysis of both the time and frequency domain implementations is performed. There exists only minor differences between the two implementations with respect to variance. The variance in the estimates proved to be highly dependent upon the aberrator. Results also indicate that a transmit-beam iteration process converges, even if the variance in the initial estimate for the iteration process is very high. In appendix A, a brief discussion of aberration as a function of frequency is provided

Automatic interpretation of cement evaluation logs from cased boreholes using supervised deep neural networks

The integrity of cement in cased boreholes is typically evaluated using well logging. However, well logging results are complex and can be ambiguous, and decisions associated with significant risks may be taken based on their interpretation. Cement evaluation logs must therefore be interpreted by trained professionals. To aid these interpreters, we propose a system for automatically interpreting cement evaluation logs, which they can use as a basis for their own interpretation. This system is based on deep convolutional neural networks, which we train in a supervised manner using a dataset of around 60 km of interpreted well log data. Thus, the networks learn the connections between data and interpretations during training. More specifically, the task of the networks is to classify the bond quality (among 6 ordinal classes) and the hydraulic isolation (2 classes) in each 1m depth segment of each well based on the surrounding 13 m of well log data. We quantify the networks' performance by comparing over all segments how well the networks' interpretations of unseen data match the reference interpretations. For bond quality, the networks’ interpretation exactly matches the reference 51.6% of the time and is off by no more than one class 88.5% of the time. For hydraulic isolation, the interpretations match the reference 86.7% of the time. For comparison, a random-guess baseline gives matches of 16.7%, 44.4%, and 50%, respectively. We also compare with how well human reinterpretations of the log data match the reference interpretations, finding that the networks match the reference somewhat better. This may be linked to the networks learning and sharing the biases of the team behind the reference interpretations. An analysis of the results indicates that the subjectivity inherent in the interpretation process (and thereby in the reference interpretations we used for training and testing) is the main reason why we were not able to achieve an even better match between the networks and the reference

Ultrasonic focusing through a steel layer for acoustic imaging

Ultrasonic tools are being used for imaging in a large variety of fields, spanning from medical applications in a hospital to applications deep down in oil and gas wells. When applying ultrasonic imaging techniques to image through elastic materials, such as steel, one of the main challenges these tools have to overcome is the high impedance differences between the steel and the surrounding media, making it a barrier for the acoustic wave. Putting effort into focusing through steel, maximizing the energy propagating to a point on the outside of the elastic layer, we aim to use back scattered pulses from this point to conduct imaging of the volume outside a steel layer as well as for flow monitoring using Doppler techniques. To investigate the focusing of an ultrasonic beam through an Ultrasonic tools are being used for imaging in a large variety of fields, spanning from medical applications in a hospital to applications deep down in oil and gas wells. When applying ultrasonic imaging techniques to image through elastic materials, such as steel, one of the main challenges these tools have to overcome is the high impedance differences between the steel and the surrounding media, making it a barrier for the acoustic wave. Putting effort into focusing through steel, maximizing the energy propagating to a point on the outside of the elastic layer, we aim to use back scattered pulses from this point to conduct imaging of the volume outside a steel layer as well as for flow monitoring using Doppler techniques. To investigate the focusing of an ultrasonic pulse through an elastic layer, a numerical study was conducted using the 2D finite-difference time-domain (FDTD) simulation tool SimSonic. Applying the time delays corresponding to focusing in a water layer to a linear phased array, shows poor focusing through an elastic layer. By implementing beamforming to the ultrasonic array, calculated using techniques related to time reversal (TR) and by a beamforming tool based on ray-tracing, focusing was achieved. At the desired focus depth it is shown that for small angles, utilizing pressure waves in the elastic layer, we can get a focused pulse propagating through the desired focus position with 3dB beamwidths of 4.2mm and 3.7mm, depending on whether the TR technique or the beamforming code was being used respectively. Increasing the angle of incidence to focus via conversion to shear waves in the elastic steel layer, the focused pulse’s maxima misses the desired focus position with 0.9mm, while the 3dB beamwidth is 5.1mm. It is shown that implementing techniques related to TR for focusing at small angles, and the beamforming code for both smaller and larger angles, makes it possible to focus the energy of the transmitted pulse through the steel layer. At the desired focus depth, 20mm below the steel plate, it is shown that for small angles, utilizing pressure waves in the elastic layer, we can get a focused beam propagating through the desired focus position with -3dB beamwidths of 4.2mm and 3.7mm, depending on whether the TR technique or the beamforming code was being used respectively. Increasing the angle of incidence to focus via conversion to share waves in the elastic steel layer, using the beamforming code to calculate the time delays, gives a focused beam propagating through the plate. The maxima misses the desired focus position with 0.9mm, while the -3dB beamwidth is 5.1mm. It is shown that implementing techniques related to TR for focusing at small angles, and the beamforming code for both smaller and larger angles, makes it possible to focusUltrasonic focusing through a steel layer for acoustic imagingacceptedVersio

Improved Lesion Detection Using Nonlocal Means Post-Processing

Software beamforming allows more flexible and complex algorithms, often referred to as adaptive beamforming techniques, that are blurring the boundaries between beamforming and image processing. Many adaptive beamforming algorithms claim to improve lesion detectability. Based on recent advances, we hypothesize that image processing techniques that reduce speckle variability yield better lesion detectability than state-of-the-art adaptive beamformers.This hypothesis is investigated on six algorithms: two image processing techniques, and four adaptive beamformers. As a target we use Field II simulations of a hypoechoic cyst with noise added to simulate different SNR conditions. Lesion detectability is estimated using the Generalized Contrast-to-Noise Ratio (GCNR). The results support our hypothesis

Improved Lesion Detection Using Nonlocal Means Post-Processing

Software beamforming allows more flexible and complex algorithms, often referred to as adaptive beamforming techniques, that are blurring the boundaries between beamforming and image processing. Many adaptive beamforming algorithms claim to improve lesion detectability. Based on recent advances, we hypothesize that image processing techniques that reduce speckle variability yield better lesion detectability than state-of-the-art adaptive beamformers.This hypothesis is investigated on six algorithms: two image processing techniques, and four adaptive beamformers. As a target we use Field II simulations of a hypoechoic cyst with noise added to simulate different SNR conditions. Lesion detectability is estimated using the Generalized Contrast-to-Noise Ratio (GCNR). The results support our hypothesis

The Generalized Contrast-to-Noise Ratio: A Formal Definition for Lesion Detectability

In the last 30 years, the contrast-to-noise ratio (CNR) has been used to estimate the contrast and lesion detectability in ultrasound images. Recent studies have shown that the CNR cannot be used with modern beamformers, as dynamic range alterations can produce arbitrarily high CNR values with no real effect on the probability of lesion detection. We generalize the definition of CNR based on the overlap area between two probability density functions. This generalized CNR (gCNR) is robust against dynamic range alterations; it can be applied to all kind of images, units, or scales; it provides a quantitative measure for contrast; and it has a simple statistical interpretation, i.e., the success rate that can be expected from an ideal observer at the task of separating pixels. We test gCNR on several state-of-the-art imaging algorithms and, in addition, on a trivial compression of the dynamic range. We observe that CNR varies greatly between the state-of-the-art methods, with improvements larger than 100%. We observe that trivial compression leads to a CNR improvement of over 200%. The proposed index, however, yields the same value for compressed and uncompressed images. The tested methods showed mismatched performance in terms of lesion detectability, with variations in gCNR ranging from -0.08 to +0.29. This new metric fixes a methodological flaw in the way we study contrast and allows us to assess the relevance of new imaging algorithms

The Generalized Contrast-to-Noise Ratio: A Formal Definition for Lesion Detectability

In the last 30 years, the contrast-to-noise ratio (CNR) has been used to estimate the contrast and lesion detectability in ultrasound images. Recent studies have shown that the CNR cannot be used with modern beamformers, as dynamic range alterations can produce arbitrarily high CNR values with no real effect on the probability of lesion detection. We generalize the definition of CNR based on the overlap area between two probability density functions. This generalized CNR (gCNR) is robust against dynamic range alterations; it can be applied to all kind of images, units, or scales; it provides a quantitative measure for contrast; and it has a simple statistical interpretation, i.e., the success rate that can be expected from an ideal observer at the task of separating pixels. We test gCNR on several state-of-the-art imaging algorithms and, in addition, on a trivial compression of the dynamic range. We observe that CNR varies greatly between the state-of-the-art methods, with improvements larger than 100%. We observe that trivial compression leads to a CNR improvement of over 200%. The proposed index, however, yields the same value for compressed and uncompressed images. The tested methods showed mismatched performance in terms of lesion detectability, with variations in gCNR ranging from -0.08 to +0.29. This new metric fixes a methodological flaw in the way we study contrast and allows us to assess the relevance of new imaging algorithms

Quantification of ocular surface microcirculation by computer assisted video microscopy and diffuse reflectance spectroscopy

In piglets we tested the applicability of digital video microscopy and diffuse reflectance spectroscopy for non-invasive assessments of limbal and bulbar conjunctival microcirculation. A priori we postulated that the metabolic rate is higher in limbal as compared to bulbar conjunctiva, and that this difference is reflected in microvascular structure or function between the two locations. Two study sites, Oslo University Hospital (OUH), Norway and Cleveland Clinic (CC), USA, used the same video microscopy and spectroscopy techniques to record limbal and bulbar microcirculation in sleeping piglets. Recordings were analyzed with custom-made software to quantify functional capillary density, capillary flow velocity and microvascular oxygen saturation in measuring volumes of approximately 0.1 mm3. The functional capillary density was higher in limbus than in bulbar conjunctiva at both study sites (OUH: 18.1 ± 2.9 versus 12.2 ± 2.9 crossings per mm line, p < 0.01; CC: 11.3 ± 3.0 versus 7.1 ± 2.8 crossings per mm line, p < 0.01). Median categorial capillary blood flow velocity was higher in bulbar as compared with limbal recordings (CC: 3 (1–3) versus 1 (0–3), p < 0.01). Conjunctival microvascular oxygen saturation was 88 ± 5.9% in OUH versus 94 ± 7.5% in CC piglets. Non-invasive digital video microscopy and diffuse reflectance spectroscopy can be used to obtain data from conjunctival microcirculation in piglets. Limbal conjunctival microcirculation has a larger capacity for oxygen delivery as compared with bulbar conjunctiva

Ocular surface microcirculation is better preserved with pulsatile versus continuous flow during cardiopulmonary bypass—An experimental pilot

Background: Non- pulsatile cardiopulmonary bypass (CPB) may induce micro-vascular dysregulation. In piglets, we compared ocular surface microcirculation during pulsatile versus continuous flow (CF) bypass.Methods: Ocular surface microcirculation in small tissue volumes (~0.1 mm3) at limbus (high metabolic rate) and bulbar conjunctiva (low metabolic rate) was ex-amined in a porcine model using computer assisted video microscopy and diffuse reflectance spectroscopy, before and after 3 and 6 h of pulsatile (n = 5 piglets) or CF (n = 3 piglets) CPB. Functional capillary density, capillary flow velocity and microvascular oxygen saturation were quantified.Results: At limbus, velocities improved with pulsatility (p< 0.01) and deterio-rated with CF (p< 0.01). In bulbar conjunctiva, velocities were severely reduced with CF (p< 0.01), accompanied by an increase in capillary density (p< 0.01). Microvascular oxygen saturation decreased in both groups.Conclusion: Ocular surface capillary densities and flow patterns are better pre-served with pulsatile versus CF during 6 h of CPB in sleeping piglets