Deep Coherence Learning: An Unsupervised Deep Beamformer for High Quality Single Plane Wave Imaging in Medical Ultrasound

Plane wave imaging (PWI) in medical ultrasound is becoming an important reconstruction method with high frame rates and new clinical applications. Recently, single PWI based on deep learning (DL) has been studied to overcome lowered frame rates of traditional PWI with multiple PW transmissions. However, due to the lack of appropriate ground truth images, DL-based PWI still remains challenging for performance improvements. To address this issue, in this paper, we propose a new unsupervised learning approach, i.e., deep coherence learning (DCL)-based DL beamformer (DL-DCL), for high-quality single PWI. In DL-DCL, the DL network is trained to predict highly correlated signals with a unique loss function from a set of PW data, and the trained DL model encourages high-quality PWI from low-quality single PW data. In addition, the DL-DCL framework based on complex baseband signals enables a universal beamformer. To assess the performance of DL-DCL, simulation, phantom and in vivo studies were conducted with public datasets, and it was compared with traditional beamformers (i.e., DAS with 75-PWs and DMAS with 1-PW) and other DL-based methods (i.e., supervised learning approach with 1-PW and generative adversarial network (GAN) with 1-PW). From the experiments, the proposed DL-DCL showed comparable results with DMAS with 1-PW and DAS with 75-PWs in spatial resolution, and it outperformed all comparison methods in contrast resolution. These results demonstrated that the proposed unsupervised learning approach can address the inherent limitations of traditional PWIs based on DL, and it also showed great potential in clinical settings with minimal artifacts

A New Nonlinear Compounding Technique for Ultrasound B-mode Medical Imaging

Compounding techniques have been used in ultra-fast ultrasound imaging to improve image quality by reducing clutter noise, smoothing speckle variance and enhancing its spatial resolution at the cost of reducing frame rate. However, the reduction of clutter noise and side lobes inside the anechoic regions is minimal when combining conventional spatial compounding and delay-and-sum (DAS) beamforming. Despite the availability of advanced beamforming algorithms such as filtered-delay-multiply-and-sum (FDMAS), its prevalence is hindered by relatively high computational cost. In this study, a new nonlinear compounding technique known as filtered multiply and sum (FMAS) was proposed to improve the B-mode image quality without increasing the overall computational complexity. With three compunding angles, the lateral resolution for DAS-FMAS was improved by 36% and 19% compared to DAS and FDMAS. The proposed DAS-FMAS technique also provided improvements of 14.1 dB and 7.29 dB in contrast ratio than DAS and FDMAS

Optimizing the lateral beamforming step for filtered-delay multiply and sum beamforming to improve active contour segmentation using ultrafast ultrasound imaging

As an alternative to delay-and-sum beamforming, a novel beamforming technique called filtered-delay multiply and sum (FDMAS) was introduced recently to improve ultrasound B-mode image quality. Although a considerable amount of work has been performed to evaluate FDMAS performance, no study has yet focused on the beamforming step size, , in the lateral direction. Accordingly, the performance of FDMAS was evaluated in this study by fine-tuning to find its optimal value and improve boundary definition when balloon snake active contour (BSAC) segmentation was applied to a B-mode image in ultrafast imaging. To demonstrate the effect of altering in the lateral direction on FDMAS, measurements were performed on point targets, a tissue-mimicking phantom and in vivo carotid artery, by using the ultrasound array research platform II equipped with one 128-element linear array transducer, which was excited by 2-cycle sinusoidal signals. With 9-angle compounding, results showed that the lateral resolution (LR) of the point target was improved by 67.9% and 81.2%, when measured at −6 dB and −20 dB respectively, when was reduced from to . Meanwhile the image contrast ratio (CR) measured on the CIRS phantom was improved by 10.38 dB at the same reduction and the same number of compounding angles. The enhanced FDMAS results with lower side lobes and less clutter noise in the anechoic regions provides a means to improve boundary definition on a B-mode image when BSAC segmentation is applied

Fast 3D super-resolution ultrasound with adaptive weight-based beamforming

Objective: Super-resolution ultrasound (SRUS) imaging through localising and tracking sparse microbubbles has been shown to reveal microvascular structure and flow beyond the wave diffraction limit. Most SRUS studies use standard delay and sum (DAS) beamforming, where high side lobes and broad main lobes make isolation and localisation of densely distributed bubbles challenging, particularly in 3D due to the typically small aperture of matrix array probes. Method: This study aimed to improve 3D SRUS by implementing a new fast 3D coherence beamformer based on channel signal variance. Two additional fast coherence beamformers, that have been implemented in 2D were implemented in 3D for the first time as comparison: a nonlinear beamformer with p-th root compression and a coherence factor beamformer. The 3D coherence beamformers, together with DAS, were compared in computer simulation, on a microflow phantom and in vivo. Results: Simulation results demonstrated that all three adaptive weight-based beamformers can narrow the main lobe suppress the side lobes, while maintaining the weaker scatter signals. Improved 3D SRUS images of microflow phantom and a rabbit kidney within a 3-second acquisition were obtained using the adaptive weight-based beamformers, when compared with DAS. Conclusion: The adaptive weight-based 3D beamformers can improve the SRUS and the proposed variance-based beamformer performs best in simulations and experiments. Significance: Fast 3D SRUS would significantly enhance the potential utility of this emerging imaging modality in a broad range of biomedical applications

Real-time delay-multiply-and-sum beamforming with coherence factor for in vivo clinical photoacoustic imaging of humans

In the clinical photoacoustic (PA) imaging, ultrasound (US) array transducers are typically used to provide B-mode images in real-time. To form a B-mode image, delay-and-sum (DAS) beamforming algorithm is the most commonly used algorithm because of its ease of implementation. However, this algorithm suffers from low image resolution and low contrast drawbacks. To address this issue, delay-multiply-and-sum (DMAS) beamforming algorithm has been developed to provide enhanced image quality with higher contrast, and narrower main lobe compared but has limitations on the imaging speed for clinical applications. In this paper, we present an enhanced real-time DMAS algorithm with modified coherence factor (CF) for clinical PA imaging of humans in vivo. Our algorithm improves the lateral resolution and signal-to-noise ratio (SNR) of original DMAS beam-former by suppressing the background noise and side lobes using the coherence of received signals. We optimized the computations of the proposed DMAS with CF (DMAS-CF) to achieve real-time frame rate imaging on a graphics processing unit (GPU). To evaluate the proposed algorithm, we implemented DAS and DMAS with/without CF on a clinical US/PA imaging system and quantitatively assessed their processing speed and image quality. The processing time to reconstruct one B-mode image using DAS, DAS with CF (DAS-CF), DMAS, and DMAS-CF algorithms was 7.5, 7.6, 11.1, and 11.3 ms, respectively, all achieving the real-time imaging frame rate. In terms of the image quality, the proposed DMAS-CF algorithm improved the lateral resolution and SNR by 55.4% and 93.6 dB, respectively, compared to the DAS algorithm in the phantom imaging experiments. We believe the proposed DMAS-CF algorithm and its real-time implementation contributes significantly to the improvement of imaging quality of clinical US/PA imaging system.11Ysciescopu

Acoustical structured illumination for super-resolution ultrasound imaging.

Structured illumination microscopy is an optical method to increase the spatial resolution of wide-field fluorescence imaging beyond the diffraction limit by applying a spatially structured illumination light. Here, we extend this concept to facilitate super-resolution ultrasound imaging by manipulating the transmitted sound field to encode the high spatial frequencies into the observed image through aliasing. Post processing is applied to precisely shift the spectral components to their proper positions in k-space and effectively double the spatial resolution of the reconstructed image compared to one-way focusing. The method has broad application, including the detection of small lesions for early cancer diagnosis, improving the detection of the borders of organs and tumors, and enhancing visualization of vascular features. The method can be implemented with conventional ultrasound systems, without the need for additional components. The resulting image enhancement is demonstrated with both test objects and ex vivo rat metacarpals and phalanges

3D Super-Resolution Ultrasound with Adaptive Weight-Based Beamforming

Super-resolution ultrasound (SRUS) imaging through localising and tracking sparse microbubbles has been shown to reveal microvascular structure and flow beyond the wave diffraction limit. Most SRUS studies use standard delay and sum (DAS) beamforming, where large main lobe and significant side lobes make separation and localisation of densely distributed bubbles challenging, particularly in 3D due to the typically small aperture of matrix array probes. This study aims to improve 3D SRUS by implementing a low-cost 3D coherence beamformer based on channel signal variance, as well as two other adaptive weight-based coherence beamformers: nonlinear beamforming with p-th root compression and coherence factor. The 3D coherence beamformers, together with DAS, are compared in computer simulation, on a microflow phantom, and in vivo. Simulation results demonstrate that the adaptive weight-based beamformers can significantly narrow the main lobe and suppress the side lobes for modest computational cost. Significantly improved 3D SR images of microflow phantom and a rabbit kidney are obtained through the adaptive weight-based beamformers. The proposed variance-based beamformer performs best in simulations and experiments.Comment: Ultrasound localisation microscopy (ULM), super-resolution, contrast-enhanced ultrasound, 3D beamformin

Advanced signal processing methods for plane-wave color Doppler ultrasound imaging

Conventional medical ultrasound imaging uses focused beams to scan the imaging scene line-by-line, but recently however, plane-wave imaging, in which plane-waves are used to illuminate the entire imaging scene, has been gaining popularity due its ability to achieve high frame rates, thus allowing the capture of fast dynamic events and producing continuous Doppler data. In most implementations, multiple low-resolution images from different plane wave tilt angles are coherently averaged (compounded) to form a single high-resolution image, albeit with the undesirable side effect of reducing the frame rate, and attenuating signals with high Doppler shifts. This thesis introduces a spread-spectrum color Doppler imaging method that produces high-resolution images without the use of frame compounding, thereby eliminating the tradeoff between beam quality, frame rate and the unaliased Doppler frequency limit. The method uses a Doppler ensemble formed of a long random sequence of transmit tilt angles that randomize the phase of out-of-cell (clutter) echoes, thereby spreading the clutter power in the Doppler spectrum without compounding, while keeping the spectrum of in-cell echoes intact. The spread-spectrum method adequately suppresses out-of-cell blood echoes to achieve high spatial resolution, but spread-spectrum suppression is not adequate for wall clutter which may be 60 dB above blood echoes. We thus implemented a clutter filter that re-arranges the ensemble samples such that they follow a linear tilt angle order, thereby compacting the clutter spectrum and spreading that of the blood Doppler signal, and allowing clutter suppression with frequency domain filters. We later improved this filter with a redesign of the random sweep plan such that each tilt angle is repeated multiple times, allowing, after ensemble re-arrangement, the use of comb filters for improved clutter suppression. Experiments performed using a carotid artery phantom with constant flow demonstrate that the spread-spectrum method more accurately measures the parabolic flow profile of the vessel and outperforms conventional plane-wave Doppler in both contrast resolution and estimation of high flow velocities. To improve velocity estimation in pulsatile flow, we developed a method that uses the chirped Fourier transform to reduce stationarity broadening during the high acceleration phase of pulsatile flow waveforms. Experimental results showed lower standard deviations compared to conventional intensity-weighted-moving-average methods. The methods in this thesis are expected to be valuable for Doppler applications that require measurement of high velocities at high frame rates, with high spatial resolution

Ultrafast 3-D Super Resolution Ultrasound using Row-Column Array specific Coherence-based Beamforming and Rolling Acoustic Sub-aperture Processing: In Vitro, In Vivo and Clinical Study

The row-column addressed array is an emerging probe for ultrafast 3-D ultrasound imaging. It achieves this with far fewer independent electronic channels and a wider field of view than traditional 2-D matrix arrays, of the same channel count, making it a good candidate for clinical translation. However, the image quality of row-column arrays is generally poor, particularly when investigating tissue. Ultrasound localisation microscopy allows for the production of super-resolution images even when the initial image resolution is not high. Unfortunately, the row-column probe can suffer from imaging artefacts that can degrade the quality of super-resolution images as `secondary' lobes from bright microbubbles can be mistaken as microbubble events, particularly when operated using plane wave imaging. These false events move through the image in a physiologically realistic way so can be challenging to remove via tracking, leading to the production of 'false vessels'. Here, a new type of rolling window image reconstruction procedure was developed, which integrated a row-column array-specific coherence-based beamforming technique with acoustic sub-aperture processing for the purposes of reducing `secondary' lobe artefacts, noise and increasing the effective frame rate. Using an {\it{in vitro}} cross tube, it was found that the procedure reduced the percentage of `false' locations from $\sim$26\% to $\sim$15\% compared to traditional orthogonal plane wave compounding. Additionally, it was found that the noise could be reduced by $\sim$7 dB and that the effective frame rate could be increased to over 4000 fps. Subsequently, {\it{in vivo}} ultrasound localisation microscopy was used to produce images non-invasively of a rabbit kidney and a human thyroid