Ole Marius Hoel Rindal

Understanding contrast improvements from capon beamforming

This paper investigates the contrast improvements seen when using adaptive (Capon) beamforming to create ultrasound images. Ultrasound images of cysts have been simulated using linear array imaging in Field II. The contrast of the cyst compared to the speckle surrounding it has been investigated, especially the improved edges produced by the Capon beamformer. We show that it is the improved edges that cause the contrast improvements. The resulting beampattern from the Capon beamformer has been compared to the beampattern from the conventional delay and sum (DAS) beamformer with different apodizations to show how the improved edges are generated. Finally, a bright inclusion was added to the simulation to demonstrate how the visual contrast changes when the image contains multiple intensity plateaus.

The dynamic range of adaptive beamformers

All adaptive beamformers aim at increased resolution and better contrast. However, the same effect can be produced by dynamic range stretching. Adaptive beamformers are often tested on wire targets or uniform cysts, which do not allow to study dynamic range alterations. This work shows that dynamic range stretching may account for some of the benefits of some adaptive beamformers in the literature. We propose a phantom allowing us to do a dynamic range calibration. We demonstrate this calibration for the Mallart-Fink Coherence Factor (CF), the Camacho-Fritsch Phase Coherence Factor (PCF) and CaponsMinimum Variance (MV) beamformer.We show that the increased resolution and contrast reported for the CF and PCF are to a great extent due to the alterations of the dynamic range.

The dark region artifact in adaptive ultrasound beamforming

An undesired effect, the dark region artifact (DRA), has been under-communicated in our research community. The DRA appear next to acoustically strong targets for some of the many adaptive beamformers introduced in the literature. This study investigates the DRA for a collection of adaptive beamformers and shows that this effect originates because some of the methods fail to estimate which signals arise in the mainlobe and which originates from sidelobes. The DRA results in darker regions in the ultrasound images, indicating the wrong acoustical amplitude. Therefore, the measured contrast can falsely appear higher for adaptive beamformers affected by the DRA.

Automatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Data

The automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs) that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier’s arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier’s arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers.

Influence of the Delay-Multiply-And-Sum beamformer on the ultrasound image amplitude

The Delay-Multiply-And-Sum (DMAS) beamformer has recently been presented in the context of medical ultrasound image formation. Images obtained with the DMAS beamformer present improved contrast resolution and noise rejection when compared to images obtained with the standard Delay-And-Sum (DAS) beamformer. We study here the signal statistics for a homogeneous medium using both the DAS and DMAS beamformers. We show that they are dependent on the coherence between the received signals. The final image amplitude is more dependent on the signal coherence when using the DMAS beamformer than when using the DAS beamformer. Ultrasound images of a phantom illustrate the dependency of the image amplitude with the signal coherence when using the DMAS beamformer. We conclude that the improved image quality obtained using the DMAS beamformer comes from a larger signal amplitude suppression as the signal coherence decreases.

The ultrasound toolbox

In the last decade the number of beamforming methods has exploded. Many innovative ideas have been proposed, but we lack the tools to compare the different techniques efficiently. The PICMUS challenge (IUS 2016, Tours) was a pioneering step that made clear that two things are required to establish a fair comparison: a common data format, and a body of methods to process that data.

Double Adaptive Plane-wave Imaging

This paper describes a contribution to the Plane-wave Imaging Challenge in Medical UltraSound (PICMUS). The suggested beamformer uses Capons Minimum Variance beamforming technique to create the low quality plane-wave images and the same Minimum Variance criteria when coherently compounding multiple low quality plane-wave images into one image of higher quality. This results in a Double Adaptive Plane-wave Imaging (DAPI) beamforming algorithm producing increased lateral resolution and improved cyst contrast compared to the conventional Delay-And-Sum (DAS) beamformer.

Point scatterer enhancement in ultrasound by wavelet coefficient shrinkage

Background speckle can often obscure objects of interest in an ultrasound image. The probability of detection and classification of point scatterers is highly affected by background speckle. The proposed algorithm uses a coherence-based wavelet coefficient shrinkage method. Point scatterers in the ultrasound image are enhanced by separating coherent point targets from incoherent background speckle. Results using Field II ultrasound simulations show how the algorithm retains the point scatterers and increases their conspicuity. The algorithm has potential to detect microcalcifications in breast tissue.

The influence of speckle statistics on contrast metrics in ultrasound imaging

Adaptive beamformers aim for improved resolution and contrast in the ultrasound images, and their performance is typically benchmarked using metrics such as contrast ratio (CR) and contrast-to-noise ratio (CNR). Using synthetic aperture Field II simulations, we show that certain beamformers alter speckle statistics and that this opens up for cherry picking of contrast metrics.

An adaptive mirage

We are in the middle of a Cambrian explosion. Software beamforming has redefined what can be done with the signal. As a consequence, our field has become flooded with adaptive beamforming (AB) algorithms, methods that by clever manipulation of channel data have exceeded our wildest expectations for the maximum achievable contrast and resolution. Or have they? If we define image quality in terms of the contrast ratio (CR) and the full-width half-maximum (FWHM), there is another way of getting unprecedented image quality. Dynamic range stretching, the kind of stretching one gets from squaring the beamformed signal amplitude, will also produce higher CR and smaller FWHM. If AB alters the output dynamic range, then the reported CR and FWHM are invalid. No tools are available yet for researchers and reviewers to check this. Here we address this problem. We propose a phantom to measure the dynamic range of AB. The phantom includes a speckle gradient band similar to those used in the calibration of monitors. The...