Dissertations / Theses on the topic 'Ultrasound imaging'

Medisinsk ultralydavbildning er et relativt rimelig verktøy som er i utstrakte bruk på dagens sykehus og tildels også legekontor. En underliggende antakelse ved dagens avbildningsteknikker er at vevet som skal avbildes i grove trekk er homogent. Det vil i praksis si at de akustiske egenskapene varierer lite. I tilfeller der denne forutsetningen ikke holder vil resultatet bli betraktlig reduksjon av bildekvaliteten. Prosjektet har fokusert på hvordan man best mulig kan korrigere for denne kvalitetsforringelsen. Arbeidet har resultert i et styrket teoretisk rammeverk for modellering, programvare for numerisk simulering. Rammeverket gir en felles forankring for tidligere publiserte metoder som "time-reversal mirror", "beamsum-correlation" og "speckle brightness", og gir derfor en utvidet forståelse av disse metodene. Videre har en ny metode blitt utviklet basert på egenfunksjonsanalyse av et stokastisk tilbakespredt lydfelt. Denne metoden vil potensielt kunne håndtere sterk spredning fra områder utenfor hovedaksen til ultralydstrålen på en bedre måte enn tidligere metoder. Arbeidet er utført ved Institutt for matematiske fag, NTNU, med professor Harald Krogstad, Institutt for matematiske fag, som hovedveileder og professor Bjørn Angelsen, Institutt for sirkulasjon og bildediagnostikk, som medveileder.

Objective evaluation of the image quality on ultrasound images is a comprehensive task due to the relatively low image quality compared to other imaging techniques. It is desirable to objectively determine the quality of ultrasound images since quantification of the quality removes the subjective evaluation which can lead to varying results. The scanner will also be more user friendly if the user is given feedback on the quality of the current image. This thesis has investigated in the objective evaluation of image quality in phantom images. It has been emphasized on the parameter spatial variance which is incorporated in the image analysis system developed during the project assignment. The spatial variance was tested for a variety of settings as for instance different beam densities and number of MLAs. In addition, different power spectra have been evaluated related to the ProbeContact algorithm developed by the Department of Circulation and Medical Imaging (ISB). The algorithm has also been incorporated in the image analysis system. The results show that the developed algorithm gives a good indication of the spatial variance. An image gets more and more spatially variant as the beam density decreases. If the beam density goes below the Nyquist sampling limit, the point target will appear to move more slowly when passing a beam since the region between the two beams are undersampled. This effect will be seen in the correlation coefficient plots which is used as a measure of spatial variance. The results from the calculations related to the ProbeContact algorithm show that rearranging the order of the averaging and the Fourier transformation will have an impact on the calculated probe contact, but the differences are tolerable. All the evaluated methods can be used, but performing Fourier transform before averaging can be viewed as the best solution since it gives a lateral power spectrum with low variance and a smooth mean frequency and bandwidth when they are compared for several frames. This is suggested with the reservations of that basic settings are used. Performing 1D (in the lateral direction) or 2D Fourier transform before averaging will not have any impact of the resulting power spectrum as long as normalized Fourier tranform is used. The conclusion is that the image analysis system, including the spatial variance parameter, is a good tool for evaluating various parameters related to image quality. The system is improved by the ProbeContact algorithm which gives a good indication of the image quality based on the acoustic contact of the probe. Even though the image analysis system is limited to phantom images, the thesis is a starting point in the process of obtaining objective evaluation of the image quality in clinical images since others may use it as a basis for their work.

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.

The ability to image myocardial perfusion is very important in order to detect coronary diseases. GE Vingmed Ultrasound uses contrast agent in combination with a pulse inversion (PI) technique to do the imaging. But this technique does not function sufficiently for all patients. Therefore have other techniques been tested out, including transmission of pulses with different amplitude (AM), to enhance the nonlinear signal from contrast bubbles. But a problem achieving sufficient cancellation of linear tissue signal is a feebleness of the method. In this diploma work has an effort been put into enhancing the tissue suppression in amplitude modulation. First the source of the lack of suppression was searched for by measuring electrical and acoustical pulses. The further examination revealed a dissymmetry in between pulses of different amplitude. To reduce this error were several attempts to make a compensation filter performed, which finally resulted in a filter created of echo data acquired from a tissue mimicking phantom. The filter was furthermore tested out on a flow phantom to see how it affected the signal from tissue and contrast bubbles, compared to the former use of a constant instead of the filter. The comparison showed 1.5-3.2 dB increase in tissue suppression (TS). But unfortunately did the filtering process slightly reduce the contrast signal as well, which resulted in a smaller increase of Contrast-to-Tissue-Ratio (CTR) than TS; 1.0-2.8 dB. During the work was the source of another problem concerning tissue suppression discovered. In earlier work by the author cite{prosjekt} the experimental results suffered from low TS around the transmitted frequency, which was found inexplicable at that time. This problem was revealed to be caused by reverberations from one pulse, interfering with the echoes from the next pulse. The solution suggested in this thesis is to transmit pulses in such a way that every pulse used to create an image has a relatively equal pulse in front. For instance, if a technique employs two pulses to create an image, and the first has half the amplitude and opposite polarity of the second. Then, to eliminate the reverberations must the first imaging pulse have a pulse in front which has half the amplitude and opposite polarity of the pulse in front of the second imaging pulse.