Sparse-view ultrasound diffraction tomography using compressed sensing with nonuniform FFT

Deep Learning Ultrasound Computed Tomography Under Sparse Sampling

Ultrasound computed tomography (USCT) is a fast-emerging imaging modality that is expected to help detect and characterize breast tumors by quantifying the distribution of the speed of sound (SOS) and acoustic attenuation in breast tissue. High-quality quantitative SOS reconstruction in USCT requires a large number of transducers, which incurs high system costs and slow computation. In contrast, sparsely distributed arrays are low-cost and fast but significantly degrade image quality. Thus, we propose a framework to achieve high-quality SOS reconstruction under sparse sampling based on a convolutional neural network (SRSS-Net) with faster computation. We first apply the bent-ray algorithm to sparsely sampled data and then apply the SRSS-Net to efficiently improve the image quality. Experimental results on synthetic and real datasets demonstrate that the proposed SRSS-Net provides reconstructions that are superior to those of state-of-the-art methods in terms of artifact suppression, structural preservation, quantitative restoration, and computational speed. As demonstrated in our experiments, the fine-tuning training strategy is suggested when applying SRSS-Net to real-world circumstances. The imaging and computational performance of SRSS-Net on the inhomogeneous breast phantom further demonstrates that SRSS-Net has great potential in real-time breast cancer detection.

Dynamic Speed of Sound Adaptive Transmission-Reflection Ultrasound Computed Tomography

Ultrasound computed tomography (USCT) can visualize a target with multiple imaging contrasts, which were demonstrated individually previously. Here, to improve the imaging quality, the dynamic speed of sound (SoS) map derived from the transmission USCT will be adapted for the correction of the acoustic speed variation in the reflection USCT. The variable SoS map was firstly restored via the optimized simultaneous algebraic reconstruction technique with the time of flights selected from the transmitted ultrasonic signals. Then, the multi-stencils fast marching method was used to calculate the delay time from each element to the grids in the imaging field of view. Finally, the delay time in conventional constant-speed-assumed delay and sum (DAS) beamforming would be replaced by the practical computed delay time to achieve higher delay accuracy in the reflection USCT. The results from the numerical, phantom, and in vivo experiments show that our approach enables multi-modality imaging, accurate target localization, and precise boundary detection with the full-view fast imaging performance. The proposed method and its implementation are of great value for accurate, fast, and multi-modality USCT imaging, particularly suitable for highly acoustic heterogeneous medium.

The application of compressive sampling in rapid ultrasonic computerized tomography (UCT) technique of steel tube slab (STS)

This paper explores a new method for rapid structural damage inspection of steel tube slab (STS) structures along randomly measured paths based on a combination of compressive sampling (CS) and ultrasonic computerized tomography (UCT). In the measurement stage, using fewer randomly selected paths rather than the whole measurement net is proposed to detect the underlying damage of a concrete-filled steel tube. In the imaging stage, the ℓ1-minimization algorithm is employed to recover the information of the microstructures based on the measurement data related to the internal situation of the STS structure. A numerical concrete tube model, with the various level of damage, was studied to demonstrate the performance of the rapid UCT technique. Real-world concrete-filled steel tubes in the Shenyang Metro stations were detected using the proposed UCT technique in a CS framework. Both the numerical and experimental results show the rapid UCT technique has the capability of damage detection in an STS structure with a high level of accuracy and with fewer required measurements, which is more convenient and efficient than the traditional UCT technique.

Compressed sensing for ultrasound computed tomography

Ultrasound computed tomography (UCT) allows the reconstruction of quantitative tissue characteristics, such as speed of sound, mass density, and attenuation. Lowering its acquisition time would be beneficial; however, this is fundamentally limited by the physical time of flight and the number of transmission events. In this letter, we propose a compressed sensing solution for UCT. The adopted measurement scheme is based on compressed acquisitions, with concurrent randomised transmissions in a circular array configuration. Reconstruction of the image is then obtained by combining the born iterative method and total variation minimization, thereby exploiting variation sparsity in the image domain. Evaluation using simulated UCT scattering measurements shows that the proposed transmission scheme performs better than uniform undersampling, and is able to reduce acquisition time by almost one order of magnitude, while maintaining high spatial resolution.

Thermometry and ablation monitoring with ultrasound

In this review we present the current status of ultrasound thermometry and ablation monitoring, with emphasis on the diverse approaches published in the literature and with an eye on which methods are closest to clinical reality. It is hoped that this review will serve as a guide to the expansion of sonographic methods for treatment monitoring and thermometry since the last brief review in 2007.