Browsing by Subject "Nonlinear Acoustics"

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    Modern Diagnostic and Therapeutic Ultrasound Systems: A Nonlinear Approach
    (2024-06) Sahoo, Abhishek
    The evolution of science, technology, and engineering has shaped every aspect of the world we live in. Medical science is no exception. Tremendous computing power, miniaturization of hardware electronics, advances in material science, etc. have improved the quality of life by bringing precise and affordable diagnostic imaging facilities that ensure the possibility of earlier treatment plans. Among the various traditional imaging modalities, ultrasound has been a familiar and popular choice due to its portability, safety, low cost, and non-invasiveness, to name a few. Traditionally, ultrasound is used for imaging favorable acoustic media such as soft tissues, the abdomen, blood vessels, etc. whereas Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have been the standard methods for imaging bone, muscle, brain, etc. owing to their superior spatial resolution and the ability to display tiny anatomical details to diagnose medical conditions. Therefore, therapeutic applications of ultrasound such as neuromodulation and high-intensity focused ultrasound (HIFU) typically rely on CT and MR image guidance before locking a target and initiating surgical procedures. However, the advent of dual-mode ultrasound arrays (DMUA) brings ultrasound one step closer to being self-reliant by unlocking both diagnostic and therapeutic capabilities for a wide range of applications. The inherent registration and shared time base between the imaging and therapeutic sequences make DMUAs ideal for critical applications such as transcranial imaging and focused ultrasound delivery where precision is the key. For image-guided therapeutic ultrasound to succeed and become the standard medical practice in the future, diagnostic quality and image specificity enhancement of the DMUAs is an absolute necessity, which is going to be the main focus of this dissertation. A nonlinear approach to ultrasound image reconstruction is studied to demonstrate its advantages over the standard B-mode imaging method. Ultrasound imaging artifacts such as reverberation, multiple scattering, side-lobe and grating lobe artifacts, beamforming artifacts, ultrasound clutter, etc. are fundamental to the physics of acoustic propagation and the assumptions made by the image reconstruction algorithms. Their presence compromises the overall diagnostic value, and image specificity by reducing the tissue contrast. However, it is well known that the nonlinearly generated ultrasound echoes are purer and contain significantly fewer undesired artifacts than their fundamental counterpart. Hence, an ultrasound image reconstructed from nonlinear echo components is superior in imaging quality. Following the polynomial signal processing concepts, a quadratic Volterra filter is designed to extract second-order nonlinearities. The challenge of optimal signal separation between the fundamental and nonlinear energies faced in the previously reported work is overcome by a band-selective quadratic kernel synthesis approach. First, a data-driven least-squares quadratic Volterra kernel is estimated from the imaging data samples to separate the nonlinear echo components. The quadratic kernel is further decomposed using the singular value decomposition (SVD). Next, the individual singular modes are analyzed and grouped according to their bifrequency responses. The grouped singular modes are then filtered with the corresponding 2D Gaussian bandpass filters to remove the out-of-band noisy interactions followed by a coherent summation to broaden the effective bandwidth of the kernel. Multiple sets of quadratic images are reconstructed using each group of kernels which capture energies at different nonlinear frequency interactions. The efficiency of the band-selective quadratic Volterra kernel synthesis algorithm is demonstrated on several imaging datasets acquired using multiple ultrasound arrays and then measuring their spatial resolution, contrast-to-tissue ratio (CTR), artifacts suppression, etc. A quality assurance phantom image shows the recovery of the true size of the embedded structures, improved axial and lateral resolution, and sidelobe suppression with the nonlinearly reconstructed images. In another study, in an in-vivo imaging of a porcine kidney, the quadratically reconstructed images reveal the structure and boundary of the kidney that is otherwise invisible in the standard B-mode image. Thus, the Volterra based band-selective kernel synthesis approach is successfully applied to multiple datasets to reconstruct high-quality, artifacts-free images revealing the true structure and dimension of the anatomical structures. Volterra based nonlinear signal separation model is an excellent analytical tool that helps us connect the theory of nonlinear propagation of an ultrasound beam to a filter design problem for separating the echo components. Moving forward, an algorithm-driven filter design procedure is pursued to capture nonlinear frequency interactions across the entire bifrequency spectrum irrespective of their participating energy. We depart from the idea of a least-squares kernel estimation strategy where quadratic energy is usually biased by fundamental wave energy. Instead, multiple kernels are designed after identifying all possible bifrequencies of interest having a higher likelihood of quadratic interactions in the medium. This is done after a thorough spectral analysis, identification of fundamental and a few higher-order harmonics, and second-order pairing of all the frequency components to form bifrequency pairs. Then, two-dimensional bandpass filters around these bifrequencies of interest are synthesized. First, two-dimensional lowpass filters are designed as per filter specifications by extending one-dimensional FIR lowpass filter concepts and then the required amount of bifrequency shifts are incorporated by multiplying 2D weighting operators following the modulation property of Fourier transform. Finally, the 2D quadratic filters are applied to transskull and transcranial imaging datasets acquired from a series of experiments conducted on a couple of cadaveric human heads and the quadratic images are reconstructed. The performance of all the filters is compared by computing the bifrequency responses of the 2D filters, and the quality of the quadratic images. It is observed that among all the most likely candidates, only a few of the quadratic filters correspond to true quadratic energy interactions whereas the rest are noisy which is evident from the quality of the reconstructed images. Additionally, all the true filters capturing energy at different spectral interactions preserve different imaging features, giving us a comprehensive visualization of different anatomical structures for a better diagnostic assessment. In our case, this has led to precise image guidance during FUS delivery in the cortical region of the brain. In addition to diagnostic quality enhancement, nonlinear imaging methods have been successfully implemented in quantitative ultrasound applications such as displacement estimation with higher accuracy. The final phase of the research is driven in the direction of implementing the above quadratic filter in real-time on a field programmable gate array (FPGA). An eigenvalue decomposition based parallel cascade architecture is designed to approximate the quadratic filter with just a couple of FIR filters and squaring elements to reconstruct quadratic images. The proposed architecture runs with significantly fewer resources and at a higher speed compared to the traditional direct implementation of the filter. Additionally, this leads to a scalable solution where the resource and timing analyses are linear w.r.t the filter memory length. This successful demonstration opens the door for low-cost, low-power, real-time hardware implementation in wearable medical ultrasound applications.