Switchable and Tunable Deep Beamformer Using Adaptive Instance Normalization for Medical Ultrasound

SIMUS3: An open-source simulator for 3-D ultrasound imaging

BACKGROUND AND OBJECTIVE

Computational Ultrasound Imaging (CUI) has become increasingly popular in the medical ultrasound community, facilitated by free simulation software. These tools enable the design and exploration of transmit sequences, transducer arrays, and signal processing. We recently introduced SIMUS, a frequency-based ultrasound simulator within the open-source MUST toolbox, which offers numerical advantages and allows easy consideration of frequency-dependent factors. In response to the growing interest in simulating ultrasound imaging with 2-D matrix arrays, we present 3-D versions, PFIELD3 and SIMUS3.

METHOD

The linear acoustic equations driving these functions are described, with theoretical assumptions reviewed for user guidance.

RESULTS

Comparative analyses with Field II, using a 32×32 element 3-MHz matrix array, highlight the performance of PFIELD3 and SIMUS3 under various transmission conditions.

CONCLUSIONS

This work extends the capabilities of existing CUI tools and provides researchers with valuable resources for advanced ultrasound simulations.

Real-time ultrasound phase imaging

The Correlation-Based (CB) imaging method is characterized by its high spatial resolution capabilities, but it is known to require heavy computational resources due to its high complexity. This paper shows that the CB imaging method can be used to estimate the phase of the complex reflection coefficients contained in the observation window. The resulting Correlation-Based Phase Imaging (CBPI) method can be used to segment and identify different features or tissue elasticity variations in a given medium. A Numerical validation is first proposed by considering a set of fifteen point-like scatterers on a Verasonics Simulator. Then, three experimental datasets are used to show the potential of CBPI on scatterers and specular reflectors. In vitro imaging results are first presented to show that CBPI allows retrieving phase information on hyperechoic reflectors, but also on weak reflectors such as elasticity targets. It is demonstrated that CBPI helps distinguishing regions of different elasticity, but of same low-contrast echogenicity, which is otherwise impossible with standard B-mode or Synthetic Aperture Focusing Techniques (SAFT). Then, CBPI of a needle in an ex vivo chicken breast is performed to show that the method works on specular reflectors. It is shown that the phase of the different interfaces associated to the first wall of the needle are well reconstructed using CBPI. The heterogeneous architecture used to enable real-time CBPI is presented. A Nvidia GeForce RTX 2080 Ti Graphics Processing Unit (GPU) is used to process the real-time acquired signals from a Verasonics Vantage 128 research echograph. Frame rates of 18 frames per second are achieved for the whole acquisition and signal processing chain on standard a 500 × 200 pixels grid.

Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network

Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems. Herein, we propose a method for image enhancement where, the overall quality of the US images is improved by simultaneous enhancement of US image resolution and noise suppression. To avoid over-smoothing and preserving structural/texture information, we devise texture compensation in our proposed method to retain the useful anatomical features. Moreover, we also utilize US image formation physics knowledge to generate augmentation datasets which can improve the training of our proposed method. Our experimental results showcase the performance of the proposed network as well as the effectiveness of the utilization of US physics knowledge to generate augmentation datasets.