Enhanced axial and lateral resolution using stabilized pulses

Mutually orthogonal Golay complementary sequences in the simultaneous synthetic aperture method for medical ultrasound diagnostics. An experimental study

Complementary Golay coded sequences (CGCS) have several advantages over conventional short pulse transmitted signals. Specifically, CGCS allow the signal-to-noise ratio (SNR) to be increased. Moreover, due to matched filtering and compression, echoes resembling the short pulse waveform with substantially higher amplitude can be obtained. However, CGCS require two subsequent transmissions to obtain a single compressed signal. This decreases the data acquisition rate and the frame rate of ultrasound imaging by two-fold. To alleviate this problem, mutually orthogonal Golay complementary sequences (MOGCS) can be used. MOGCS allow the simultaneous transmission of two CGCS pairs to be implemented, yielding the acoustic data for two image frames in one data acquisition cycle. The main objective of this work was an experimental study of the most crucial parameters of the received acoustic signals, e.g. the signal-to-noise ratio (SNR), the side-lobes level (SLL) of the signal and the axial resolution, obtained from simultaneous transmission of two pairs of CGCS comprising a MOGCS set to demonstrate their feasibility of being used in ultrasonography. For this purpose, a simultaneous synthetic transmit aperture method (SSTA) was proposed. The SSTA is based on MOGCS transmission and simultaneous reconstruction of two image frames from a single data acquisition cycle. This doubles the image reconstruction rate in comparison with conventional CGCS signals. In this paper, the ultrasound data from a perfect reflector, commercial phantoms and in vivo measurements were analysed. Two 16-bit long CGCS pairs comprising the MOGCS set were programmed and transmitted using the Verasonics Vantage™ research ultrasound system equipped with a Philips ATL L7-4 linear array ultrasound probe. It was shown that the signal parameters and overall quality of reconstructed B-mode images did not deteriorate when using the MOGCS in comparison to the conventional CGCS and short pulse signals explored so far.

Sub-wavelength lateral detection of tissue-approximating masses using an ultrasonic metamaterial lens

Practically applied techniques for ultrasonic biomedical imaging employ delay-and-sum (DAS) beamforming which can resolve two objects down to 2.1λ within the acoustic Fresnel zone. Here, we demonstrate a phononic metamaterial lens (ML) for detection of laterally subwavelength object features in tissue-like phantoms beyond the phononic crystal evanescent zone and Fresnel zone of the emitter. The ML produces metamaterial collimation that spreads 8x less than the emitting transducer. Utilizing collimation, 3.6x greater lateral resolution beyond the Fresnel zone limit was achieved. Both hard objects and tissue approximating masses were examined in gelatin tissue phantoms near the Fresnel zone limit. Lateral dimensions and separation were resolved down to 0.50λ for hard objects, with tissue approximating masses slightly higher at 0.73λ. The work represents the application of a metamaterial for spatial characterization, and subwavelength resolution in a biosystem beyond the Fresnel zone limit.

Traditional methods for ultrasound detection in biomedical application suffer from limited lateral resolution. Here, the authors show that a phononic metamaterial lens can be used for spatial characterisation of subwavelength objects, even beyond the Fresnel zone of the emitting transducer.

Enhancement of Acoustic Microscopy Lateral Resolution: A Comparison Between Deep Learning and Two Deconvolution Methods

Scanning acoustic microscopy (SAM) provides high-resolution images of biological tissues. Since higher transducer frequencies limit penetration depth, image resolution enhancement techniques could help in maintaining sufficient lateral resolution without sacrificing penetration depth. Compared with existing SAM research, this work introduces two novelties. First, deep learning (DL) is used to improve lateral resolution of 180-MHz SAM images, comparing it with two deconvolution-based approaches. Second, 316-MHz images are used as ground truth in order to quantitatively evaluate image resolution enhancement. The samples used were mouse and rat brain sections. The results demonstrate that DL can closely approximate ground truth (NRMSE = 0.056 and PSNR = 28.4 dB) even with a relatively limited training set (four images, each smaller than 1 mm ×1 mm). This study suggests the high potential of using DL as a single image superresolution method in SAM.

Single-image Bayesian Restoration and Multi-image Super-resolution Restoration for B-mode Ultrasound Using an Accurate System Model Involving Correlated Nature of the Speckle Noise

B-mode ultrasound is an essential part of radiological examinations due to its low cost, safety, and portability, but has the drawbacks of the speckle noise and output of most systems is two-dimensional (2D) cross sections. Image restoration techniques, using mathematical models for image degradation and noise, can be used to boost resolution (deconvolution) as well as to reduce the speckle. In this study, new single-image Bayesian restoration (BR) and multi-image super-resolution restoration (BSRR) methods are proposed for in-plane B-mode ultrasound images. The spatially correlated nature of the speckle was modeled, allowing for examination of two different models for BR and BSRR for uncorrelated Gaussian (BR-UG, BSRR-UG) and correlated Gaussian (BR-CG, BSRR-CG). The performances of these models were compared with common image restoration methods (Wiener filter, bilateral filtering, and anisotropic diffusion). Well-recognized metrics (peak signal-to-noise ratio, contrast-to-noise ratio, and normalized information density) were used for algorithm free-parameter estimation and objective evaluations. The methods were tested using superficial tissue (2D scan data collected from volunteers, tissue-mimicking resolutions, and breast phantoms). Improvement in image quality was assessed by experts using visual grading analysis. In general, BSRR-CG performed better than all other methods. A potential downside of BSRR-CG is increased computation time, which can be addressed by the use of high-performance graphics processing units (GPUs).

Contrast-enhanced ultrasound imaging using pulse inversion spectral deconvolution

A contrast-enhanced ultrasound (CEUS) imaging approach, termed pulse inversion spectral deconvolution (PISD), is introduced. The approach uses two Gaussian-weighted Hermite polynomials to form two inverted pulse sequences. The two inversed pulses are then used to filter ultrasound (US) backscattered data and discrimination of the linear and nonlinear signal components. A research US scanner equipped with a linear array transducer was used for data acquisition. The receive data from all channels are shaped using plane wave imaging beamforming with angular compounding (from one to nine angles). In vitro data was collected with a tissue mimicking flow phantom perfused with an US contrast agent using PISD and traditional nonlinear (NLI) US imaging as comparison. The role of imaging frequency (between 4.5 and 6.25 MHz) and mechanical index (from 0.1 to 0.3) were evaluated. Preliminary in vivo data was collected in the hindlimb of three healthy mice. Preliminary experimental findings indicate that the PISD contrast-to-tissue ratio was improved nearly ten times compared to the NLI US imaging approach. Also, the spatial resolution was improved due to the effect of deconvolution and spatial angular compounding. Overall, PISD is a promising postprocessing technique for real-time CEUS imaging.