Ultrasound computed tomography (USCT) holds great promise for improving the detection and management of breast cancer. Because they are based on the acoustic wave equation, waveform inversion-based reconstruction methods can produce images that possess improved spatial resolution properties over those produced by ray-based methods. However, waveform inversion methods are computationally demanding and have not been applied widely in USCT breast imaging. In this work, source encoding concepts are employed to develop an accelerated USCT reconstruction method that circumvents the large computational burden of conventional waveform inversion methods. This method, referred to as the waveform inversion with source encoding (WISE) method, encodes the measurement data using a random encoding vector and determines an estimate of the sound speed distribution by solving a stochastic optimization problem by use of a stochastic gradient descent algorithm. Both computer simulation and experimental phantom studies are conducted to demonstrate the use of the WISE method. The results suggest that the WISE method maintains the high spatial resolution of waveform inversion methods while significantly reducing the computational burden.

/pdf/waveform-inversion-with-source-encoding-for-breast-sound-c2tignlcgw.pdf

Waveform inversion with source encoding for breast sound speed reconstruction in ultrasound computed tomography

Ultrasound computed tomography (USCT) holds great promise for breast cancer screening. Waveform inversion-based image reconstruction methods account for higher order diffraction effects and can produce high-resolution USCT images, but are computationally demanding. Recently, a source encoding technique has been combined with stochastic gradient descent (SGD) to greatly reduce image reconstruction times. However, this method bundles the stochastic data fidelity term with the deterministic regularization term. This limitation can be overcome by replacing SGD with a structured optimization method, such as the regularized dual averaging method, that exploits knowledge of the composition of the cost function. In this paper, the dual averaging method is combined with source encoding techniques to improve the effectiveness of regularization while maintaining the reduced reconstruction times afforded by source encoding. It is demonstrated that each iteration can be decomposed into a gradient descent step based on the data fidelity term and a proximal update step corresponding to the regularization term. Furthermore, the regularization term is never explicitly differentiated, allowing nonsmooth regularization penalties to be naturally incorporated. The wave equation is solved by the use of a time-domain method. The effectiveness of this approach is demonstrated through computer simulation and experimental studies. The results suggest that the dual averaging method can produce images with less noise and comparable resolution to those obtained by the use of SGD.

Regularized Dual Averaging Image Reconstruction for Full-Wave Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) imaging with a ring array holds significant potential for the inspection of small animals, breast tissue, and industrial components. Despite its promise, current techniques face challenges in terms of image quality. This paper presents a multi-mode aperture matching method for USCT image reconstruction with a ring array, aiming to enhance imaging performance. The proposed method utilizes a full ring transducer to transmit signals with a designated sub-aperture, while the receiving sub-aperture is adjusted based on the reference center element. By altering the transmitting and receiving sub-apertures under different conditions, specific imaging effects can be achieved. Experimental validation was conducted using a hair strand of 0.03 mm diameter placed at both center and eccentric positions within the ring array, in conjunction a breast phantom. Image resolution and contrast were assessed using full width at half maximum and contrast-to-noise ratio metrics. The results identified optimal transmitting and receiving sub-aperture matching modes for imaging the hair strand at various positions, and demonstrated the impact of this method on imaging targets at different positions within the breast phantom. These findings and experimental validations provide significant implications for enhancing imaging performance in USCT with a ring array.

Ultrasound computed tomography image reconstruction with multi-mode aperture matching of ring array

Ultrasound is widely used in biomedical imaging. High-frequency ultrasound at MHz is usually used for good resolution. However, ultrasound at this band cannot permeate human thorax, for it is strongly scattered and reflected by air inclusions in the thorax. Low-frequency ultrasound can bypass this limitation and capture useful information deep within the thorax. Hence, this paper presents a new signal processing method to enhance the signal quality of low-frequency ultrasound imaging by improving the signal-to-noise ratio (SNR) and reducing the mean square error (MSE). Specifically, we propose a solution that integrates the empirical mode decomposition (EMD)-based wavelet thresholding algorithm with the multiple signal classification (MUSIC) algorithm. The MUSIC algorithm pre-processes the low-frequency ultrasound signal prior to implementing the EMD-based wavelet threshold denoising algorithm for further noise reduction. We assess the effectiveness of our approach through simulation and experimental research. Results demonstrate substantial performance improvements in the quality of low-frequency ultrasound signals with SNR enhancement ranging from 2.2915 to 3.3358 dB in the simulation, and MSE decrease of 0.3018. Moreover, the experiment yields an SNR improvement range of 1.0274 to 2.9372 dB and a corresponding decrease of 0.2202 in MSE. In agreement with the simulation outcomes, these results confirm the efficacy of our proposed method as an efficient solution for improving the quality of low-frequency ultrasound signals. 

Low-Frequency Ultrasound Thoracic Signal Processing Based on Music Algorithm and EMD Wavelet Thresholding

Ultrasound Tomography Systems for Medical Imaging

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Ultrasound Tomography Systems for Medical Imaging