Measurement Of Tissue Motion Research Articles

Rapid change in environment for research on diagnostic ultrasound.

For progression of research, of course, experimental systems play an important role. Fifteen years ago, at the start of my research career, it was still difficult for researchers, maybe particularly for researchers in universities, to obtain an experimental system that was capable of acquiring beamformed ultrasonic radio-frequency (RF) echoes from an ultrasonic array transducer, due to the limitations of the available hardware, e.g., low sampling frequency and the limited memory of the analog-to-digital (A/D) converter. In our case, we were forced to use base-band signals (composed of in-phase and quadrature signals) to acquire ultrasonic signals for longer acquisition periods with a limited amount of memory because acquisition of beamformed RF signals required a higher sampling frequency and more memory. Even for acquisition of base-band signals, we needed to develop an expensive in-house acquisition system to acquire the base-band signals of beamformed ultrasonic RF echoes for several seconds. The sampling frequency and amount of memory for the in-house system were 10 MHz and 16 MB, respectively. There was a severe tradeoff between sampling frequency and the period available for acquisition (amount of memory). For example, for research on ultrasound imaging, it is desirable to acquire RF signals, which requires a high sampling frequency, and, thus, the number of frames acquired is reduced. On the other hand, in the case of research involving measurement of tissue motion, for example, it is necessary to acquire ultrasonic signals for a certain required period and the sampling frequency should be limited. Therefore, signals such as base-band signals were preferred because they did not require a high sampling frequency. In the past decade, the capability of acquisition systems has improved dramatically. Now we are able to obtain an acquisition system with a much higher sampling frequency and more memory. At around the year 2000, such systems implemented acquisition of beamformed RF signals for a certain required number of frames, e.g., for several seconds. However, it was still difficult to acquire ultrasonic RF echoes received by each element of an ultrasonic array transducer (channel RF signal). Therefore, there were very few research groups who could develop and implement methods for diagnostic ultrasound imaging using RF signals obtained from array elements, though there should have been many good ideas on diagnostic ultrasound imaging based on array signal processing. However, systems capable of acquiring channel RF signals and also controlling the transmitted ultrasonic field have become available at last due to further innovation in the capability of acquisition systems. Such systems are still not cheap, but now we are able to obtain such systems much more easily than before. As described above, there was a severe tradeoff in the past, i.e., higher sampling frequency and shorter acquisition period to acquire RF echoes or lower sampling frequency and longer acquisition period based on base-band signals. However, now we have been freed from such limitations. Current ultrasound acquisition systems, which are able to acquire channel RF signals and control the transmitted ultrasonic field, will significantly broaden the opportunities for research on diagnostic ultrasound imaging, and the prevalence of such systems may lead to explosive progression in related research fields. H. Hasegawa (&) Graduate School of Biomedical Engineering, Tohoku University, Sendai, Miyagi, Japan e-mail: hasegawa@ecei.tohoku.ac.jp

Bandpass sampling of high-frequency tissue motion

The characterization of tissue viscoelastic properties requires the measurement of tissue motion over a region of interest at frequencies that significantly exceed the frame rates of conventional ultrasound systems. In this paper, we propose that the bandpass sampling technique be applied to tissue motion sampling. With this approach, high-frequency signals limited to a frequency band can be sampled and reconstructed without aliasing at a sampling frequency that is lower than the Nyquist rate. We first review this approach and discuss the selection of the tissue excitation frequency band and of the feasible sampling frequencies that allow signal reconstruction without aliasing. We then demonstrate the approach using simulations based on the finite element method and ultrasound simulations. Finally, we perform experiments on tissue-mimicking materials and demonstrate accurate motion estimation using a lower sampling rate than that required by the conventional sampling theorem. The estimated displacements were used to measure the elasticity and viscosity in a phantom in which an inclusion has been correctly delineated. Thus, with bandpass sampling, it is feasible to use conventional beamforming on diagnostic ultrasound systems to perform high-frequency dynamic elastography. The method is simple to implement because it does not require beam interleaving, additional hardware, or synchronization.

Proper Analytic Point Spread Function for Lateral Modulation

For ultrasonic lateral modulation for the imaging and measurement of tissue motion, better envelope shapes of the point spread function (PSF) than of a parabolic function are searched for within analytic functions or windows on the basis of the knowledge of the ideal shape of PSF previously obtained, i.e., having a large full width at half maximum and short feet. Through simulation of displacement vector measurement, better shapes are determined. As a better shape, a new window is obtained from a Turkey window by changing Hanning windows by power functions with an order larger than the second order. The order of measurement accuracies obtained is as follows, the new window > rectangular window > power function with a higher order > parabolic function > Akaike window.

Needle-tissue interaction modeling using ultrasound-based motion estimation: Phantom study

Needle insertion simulators find use in a number of medical interventions, such as prostate brachytherapy. A needle insertion simulator has three main components: the needle model, the tissue model, and the model of interaction between the needle and the tissue.In this paper, a new methodology is introduced for the joint modeling of tissue and needle-tissue interactions. The approach consists of the measurement of tissue motion using ultrasound, and of the needle position and base force. Tissue motion is determined using a correlation-based algorithm that processes the ultrasound radiofrequency data. The tissue elastic parameters and the parameters of the tissue-needle interaction model are determined by using numerical optimization to match the response of the needle insertion model to the measured data.Phantom experiments were carried out in which a brachytherapy needle was inserted into a two-layer non-homogeneous phantom mimicking a prostate and its surrounding tissue. Experimental results show good agreement with the model obtained. In particular, the parameters of a three-parameter force model were identified for each layer of the phantom to fit the measured force to the simulated one. Also, the Young's modulus of each layer was identified to match the measured and simulated nodal axial displacements. This is the first report of the use of ultrasound radiofrequency data to characterize tissue motion during needle insertion. As the method is non-invasive and does not involve ionizing radiation, its application in patient studies is feasible.

A spline-based algorithm for continuous time-delay estimation using sampled data

Time delay estimation (TDE) lies at the heart of signal processing algorithms in a broad range of application areas, including communications, coherent imaging, speech processing, and acoustics. In medical ultrasound for example, TDE is used in blood flow estimation, tissue motion measurement, tissue elasticity estimation, phase aberration correction, and a number of other algorithms. Because of its central significance, TDE accuracy, precision, and computational cost are of critical importance. Furthermore, because TDE is typically performed on sampled signals-and delay estimates are usually desired over a continuous domain-time delay estimator performance should be considered in conjunction with associated interpolation. In this paper we present a new time-delay estimator that directly determines continuous time-delay estimates from sampled data. The technique forms a spline-based, piecewise continuous representation of the reference signal then solves for the minimum of the sum squared error between the reference and the delayed signals to determine their relative time delay. Computer simulation results clearly show that the proposed algorithm significantly outperforms other algorithms in terms of jitter and bias over a broad range of conditions. We also describe a modified version of the algorithm that includes companding with only a minor increase in computational cost.

Measurement of tissue motion.

A number of ultrasonic methods are available for the detection of tissue motion as it occurs physiologically in the body. The detection of echoes from within the body in less than 1 ms after the initial transmission of ultrasound and the Doppler effect have enabled a range of instrumentation to be developed. The subject owes a great deal to advances in transducer design, electronics and computer technology. Over many years fast B-mode imaging and M-mode traces of boundary position versus time have been the main clinical tools. Currently new sophisticated detection and imaging techniques are being produced based on the Doppler effect and on tracking motion in tissue images. The measurement of several velocity components is permitting velocity vectors to be determined more completely, adding to accuracy. Not surprisingly, cardiology is the main field of application but there are other areas of interest, e.g. vascular, musculo-skeletal and foetal function studies.

Multilevel and Motion Model-Based Ultrasonic Speckle Tracking Algorithms

A multilevel motion model-based approach to ultrasonic speckle tracking has been developed that addresses the inherent trade-offs associated with traditional single-level block matching (SLBM) methods. The multilevel block matching (MLBM) algorithm uses variable matching block and search window sizes in a coarse-to-fine scheme, preserving the relative immunity to noise associated with the use of a large matching block while preserving the motion field detail associated with the use of a small matching block. To decrease further the sensitivity of the multilevel approach to noise, speckle decorrelation and false matches, a smooth motion model-based block matching (SMBM) algorithm has been implemented that takes into account the spatial inertia of soft tissue elements. The new algorithms were compared to SLBM through a series of experiments involving manual translation of soft tissue phantoms, motion field computer simulations of rotation, compression and shear deformation, and an experiment involving contraction of human forearm muscles. Measures of tracking accuracy included mean squared tracking error, peak signal-to-noise ratio (PSNR) and blinded observations of optical flow. Measures of tracking efficiency included the number of sum squared difference calculations and the computation time. In the phantom translation experiments, the SMBM algorithm successfully matched the accuracy of SLBM using both large and small matching blocks while significantly reducing the number of computations and computation time when a large matching block was used. For the computer simulations, SMBM yielded better tracking accuracies and spatial resolution when compared with SLBM using a large matching block. For the muscle experiment, SMBM outperformed SLBM both in terms of PSNR and observations of optical flow. We believe that the smooth motion model-based MLBM approach represents a meaningful development in ultrasonic soft tissue motion measurement.

Tissue motion assessment from scattered ultrasound echoes

Ultrasound has become a standard clinical tool in the diagnosis and treatment of illness and injury. Perhaps the most well‐known use of ultrasound is the noninvasive imaging of different parts of the body. Ultrasound does, however, have other applications in medical diagnoses, such as in tissue characterization and in the measurement of tissue motion in vivo. The measurement of tissue motion is a somewhat broad category. It can include the motion of physically active organs such as the heart and discrete structures such as cardiac valves and arterial walls to diagnose heart disease. It can be used to measure motion in soft tissues (such as the liver) due to the pulsing of an artery in the tissue. The soft tissue motion is a function of elasticity of the tissue, which is directly related to the state of the tissue (diseased or healthy). Finally, the measurement of blood flow velocity is an important parameter in diagnosing coronary diseases such as venous thrombosis. Regardless of the particular application of ultrasound, all applications require the transmission and capture of rf ultrasonic signals. After capture, the signals must be processed in some way to extract the desired information. In the case of estimating tissue motion, many methods exist, ranging from the well‐known and widely implemented Doppler technique to newly developed time domain methods; from estimating velocity from a single rf A‐scan to using multiple M‐mode video images. The topic of this presentation is to review and compare some of the methods currently under development to measure tissue motion from scattered ultrasound echoes. [Work supported by NIH/HL 39704 and NIH/CA 36029.]