Mohamed Almekkawy

Multiple-frequency phased array pattern synthesis for HIFU surgery

We present a simulation/experimental study to evaluate and optimize the focusing capabilities of a phased array prototype when excited by multiple-frequency components. A multiple-focus multiple-frequency pattern synthesis algorithm for phased arrays has been developed and tested using linear simulations in Matlab. The algorithm maintains the precise phase relationship between the frequency components at each focal spot to achieve a desirable therapeutic outcome. Preliminary simulations indicate that the focal region can be shaped based on the alignment and phase of multiple-frequency components. The pattern synthesis algorithm is experimentally validated with a 3.5 MHz, 64-element prototype designed for small-animal and superficial therapeutic HIFU applications (Imasonic, Inc) which has a 52% fractional bandwidth, allowing for therapeutic output in the frequency range of 2.7-4.6 MHz. Validation with hydrophone measurements at the focal locations showed that there is in increase in harmonic generation at the focal point with the frequency mixed patterns when compared to a conventional single frequency excitation pattern. This increased non-linearity, will allow for increased thermal absorption at the focal point, thus allowing for larger treatment volumes with the same total power or reduced treatment time per shot when compared to the single frequency case. Ex vivo experiments with fresh porcine liver were conducted to study the effect of multiple-frequency patterns when compared with conventional single frequency patterns during lesion formation. The lesion size was increased for the multiple-frequency patterns when compared the single frequency pattern at normalized power with respect to each other. In conclusion, wideband piezocomposite array transducers, together with multi-channel arbitrary waveform generators are enabling technologies which allow for complex, multiple-focus, multiple-frequency HIFU patterns. These patterns can enhanced the focal gain with proper phase alignment. Furthermore, multiple frequency patterns have been shown to be able to increase the harmonic generation at the focal spot, thus improving local absorption. Our early results with ex vivo porcine liver indicate that multiple frequency excitation can enhance the therapeutic gain at the focal points.

Two-dimensional speckle tracking using zero phase crossing with Riesz transform

Ultrasound speckle tracking (ST) provides robust estimates of fine tissue displacements along the beam direction due to the analytic nature of echo data. A multi-dimensional ST method (MDST) with subsample accuracy in all dimensions is introduced. The algorithm is based on the gradient of the magnitude and the zero-phase crossing of 2D complex correlation of the generalized analytic signal. The generalization method utilizes the Riesz transform (the vector extension of the Hilbert transform). Robustness of the tracking algorithm is investigated using realistic synthetic data sequences created with (Field II) for which the bench mark displacement was known. In addition, the new MDST method is used in the estimation of the flow and surrounding tissue motion on human carotid artery in vivo. The data were collected using a linear array probe of a Sonix RP ultrasound scanner at 325 frames per second. The vessel diameter was calculated from the upper and lower vessel wall displacements, and clearly showed a blood pressure wave-like pattern. The results obtained show that using the Riesz transform produces a more robust estimation of the true displacement of the simulated model compared to previously published results. This could have significant impact on strain calculations near vessel walls.

Solving the ultrasound inverse scattering problem of inhomogeneous media using different approaches of total least squares algorithms

The distorted Born iterative method (DBI) is used to solve the inverse scattering problem in the ultrasound tomography with the objective of determining a scattering function that is related to the acoustical properties of the region of interest (ROI) from the disturbed waves measured by transducers outside the ROI. Since the method is iterative, we use Born approximation for the first estimate of the scattering function. The main problem with the DBI is that the linear system of the inverse scattering equations is ill-posed. To deal with that, we use two different algorithms and compare the relative errors and execution times. The first one is Truncated Total Least Squares (TTLS). The second one is Regularized Total Least Squares method (RTLS-Newton) where the parameters for regularization were found by solving a nonlinear system with Newton method. We simulated the data for the DBI method in a way that leads to the overdetermined system. The advantage of RTLS-Newton is that the computation of singular value decomposition for a matrix is avoided, so it is faster than TTLS, but it still solves the similar minimization problem. For the exact scattering function we used Modified Shepp-Logan phantom. For finding the Born approximation, RTLS-Newton is 10 times faster than TTLS. In addition, the relative error in L2-norm is smaller using RTLS-Newton than TTLS after 10 iterations of the DBI method and it takes less time.

Reconstruction of ultrasound tomography for cancer detection using total least squares and conjugate gradient method

The distorted Born iterative (DBI) method is a powerful approach for solving the inverse scattering problem for ultrasound tomographic imaging. This method iteratively solves the inverse problem for the scattering function and the forward problem for the inhomogeneous Green’s function and the total field. Because of the ill-posed system from the inverse problem, regularization methods are needed to obtain a smooth solution. The three methods compared are truncated total least squares (TTLS), conjugate gradient for least squares (CGLS), and Tikhonov regularization. This paper uses numerical simulations to compare these three approaches to regularization in terms of both quality of image reconstruction and speed. Noise from both transmitters and receivers is very common in real applications, and is considered in stimulation as well. The solutions are evaluated by residual error of scattering function of region of interest(ROI), convergence of total field solutions in all iteration steps, and accuracy of estimated Green’s functions. By comparing the result of reconstruction quality as well as the computational cost of the three methods under different ultrasound frequency, we prove that TTLS method has the lowest error in solving strongly ill-posed problems. CGLS consumes the shortest computational time but its error is higher than TTLS, but lower than Tikhonov regularization.

Two-dimensional speckle tracking using parabolic polynomial expansion with Riesz transform

Ultrasound speckle tracking provides a robust motion estimation of fine tissue displacements along the beam direction. Extensions to 2-D have been proposed in recent years. Due to relatively coarse lateral sampling, several solutions relied on lateral interpolation in order to achieve subsample accuracy. We introduce a new multi-dimensional speckle tracking method (MDST) with subsample accuracy in all dimensions. The proposed algorithm is based on solving a least squares problem to estimate the coefficients of a second order polynomial expansion to fit the magnitude of the two dimensional complex normalized correlation of the generalized analytic signal in the vicinity of the true peak. The generalization method utilizes the Riesz transform which is the multidimensional Hilbert transform. The displacement is estimated from acquired successive radio-frequency data frames of the region of interest. Field II simulation of flow data in a channel with a bench mark known displacement is generated to validate the accuracy of the method. In addition, the new MDST method is applied to imaging data from a flow phantom (ATS Model 524) to estimate the flow motion and pulsating channel wall. Simulations and experimental results demonstrate the effectiveness of the proposed technique.

Transcranial enhanced Ultrasound Imaging of induced substantia nigra in brain using adaptive Third Order Volterra Filter: In-vivo results

Hyperechogenicity of the substantia nigra (SN) in the “butterfly shaped” midbrain is a widely recognized diagnostic marker to differentiate between the early stages of Parkinsons Disease (PD) and other diseases which cause parkinsonian symptoms. While clinical differentiation of these diseases can be difficult, hyperechogenicity of the SN is only common in PD patients. Transcranial B-mode Ultrasound Imaging (TCUI) has become a heavily relied upon method to detect echogenicity in the brain. While standard B-mode imaging can show the presence of SN hyperechogenicity, it may not be able to do so with high enough specificity for reliably accurate diagnoses. The cutoff of what is considered a normal echogenic size is 0.2cm2. Clearly, boundary definition is of the utmost importance to avoid overestimating the size of the echogenic area. Many studies have shown that the harmonic component of ultrasound images have better dynamic range than standard B-mode images. That is, the images show greater contrast between light and dark regions, so low energy noise signals are less likely to show up in the image. Whereas a simple bandpass filter across the harmonic frequency would contain interference from the noisy fundamental component due to overlap of the frequency bands. We propose the use of an adaptive Third Order Volterra Filter (TOVF), which is a nonlinear filter that separates a B-mode image into its linear, quadratic, and cubic components regardless of spectral overlap. This paper investigates several variants of the commonly used adaptive Least Mean Squared (LMS) algorithm for determining filter coefficients, and their potential to improve dynamic range and resolution in B-mode images compared to a standard LMS algorithm. We found that several variant algorithms indeed show improvement in terms of Power Spectral Density (PSD) at the harmonics.

In-vivo transcranial ultrasound imaging of induced Substantia Nigra hyperechogenicity using adaptive sparse Third Order Volterra Filter

The difference between the early stages of Parkinsons Disease (PD) and other diseases with similar symptoms is quite difficult to discern. Thus, hyperechogenicity of the Substantia Nigra (SN) revealed in ultrasound imaging has become a standard diagnostic marker for accurately diagnosing PD, as it is only common in PD patients. This has resulted in Transcranial B-mode Ultrasound Imaging (TCUI) becoming a widely used tactic for diagnosis of PD, as ultrasound is naturally well-suited to detect echogenicity. The accepted cutoff for hyperechogenicity is an echogenic area of 0.2cm2. Currently, clinician outline the echogenic area manually with a cursor, which naturally leaves room for ambiguity and human error. Unfortunately standard B-mode images of the SN are noisy enough that determining the boundaries of the echogenic area are typically quite ambiguous. This is why we suggest the use of the Third Order Volterra Filter (ToVF), which can separate an image into its linear, quadratic, and cubic components with no spectral overlap. One common method of implementing the Volterra filter is with an adaptive Least Mean Squares (LMS) algorithm. This paper examines Zero-Attracting variants of LMS algorithms, which take advantage of the sparse nature of ultrasound data for improved performance. We found that the Zero-Attracting algorithms converged to lower steady state errors, and also performed better in terms of dynamic range and boundary definition.

Body temperature control circuit

There is currently no animal model available that allows chronic control of body temperature, e.g. to study the effects of body temperature on disease. Prior studies have shown that by heating the preoptic area (POA) of the hypothalamus of rodents with a heated water-perfused probe the body temperature can be lowered, but this method is not feasible for chronic use. Here we present two control circuit designs that employ high performance operational amplifiers and a feedback network that has the ability to chronically modulate the temperature of the POA of the hypothalamus, thus adjusting the body temperature set point. This will be done by permanently implanting a heated micro-probe (thermistor) and using a feedback system to accurately control the temperature of the thermistor to help the brain center control the temperature. The control circuit and feedback network were designed to provide accurate and sustainable control over the subjects body temperature while focusing on the miniaturization of components without sacrificing performance. For this design the power consumed by the circuit was achieved to be 3.8 mW and the size of the board is currently 9 cm2. Tadiran batteries were used to employ high-performance button cells. The thermistor used is around 360 μm and it is capable of controlling the temperature between 98.6 °F/37 °C and 122 °F/50 °C. Thus, the circuit is sufficiently small to be carried by a rodent while chronically controlling the POA temperature.

An optimized ultrasound digital beamformer with dynamic focusing implemented on FPGA

We present a resource-optimized dynamic digital beamformer for an ultrasound system based on a field-programmable gate array (FPGA). A comprehensive 64-channel receive beamformer with full dynamic focusing is embedded in the Altera Arria V FPGA chip. To improve spatial and contrast resolution, full dynamic beamforming is implemented by a novel method with resource optimization. This was conceived using the implementation of the delay summation through a bulk (coarse) delay and fractional (fine) delay. The sampling frequency is 40 MHz and the beamformer includes a 240 MHz polyphase filter that enhances the temporal resolution of the system while relaxing the Analog-to-Digital converter (ADC) bandwidth requirement. The results indicate that our 64-channel dynamic beamformer architecture is amenable for a low power FPGA-based implementation in a portable ultrasound system.

Nonlinear modeling of pulsed and CW HIFU beams for dual-mode ultrasound arrays

Nonlinear propagation can have beneficial or detrimental effects on focused ultrasound beams for imaging and therapy. The advent of piezocomposite transducer technology have allowed for the fabrication of new generation of therapeutic arrays with relatively wide bandwidth and low cross coupling between elements (resulting in new dual-mode ultrasound array (DMUA) systems for imaging and therapy). The feedback capabilities of DMUAs offer new opportunities to characterize the HIFU beam in situ, including its spectral components due to harmonic generation. This form of feedback allows for the optimization of the DMUA driving patterns to achieve maximum therapeutic gain, by maximizing the harmonic generation within the focal spot. We present results of full 3D modeling of nonlinear wave propagation from the surface of currently available DMUA prototypes (1 and 3.5 MHz) into the treatment volume, modeled as heterogeneous absorbing medium. For example, a 1 MHz DMUA with f-number of 0.8 when geometrically focused...