Plane-wave Imaging Challenge In Medical UltraSound Research Articles

WSRGAN: A wavelet-based GAN for super-resolution of plane-wave ultrasound images without sampling loss

Plane-wave ultrasound imaging has ultra-fast temporal resolution and can image at frame rates exceeding 1 kHz, enabling breakthrough technologies like shear wave elastography. However, the imaging process of this technology lacks focus and poor imaging quality and usually requires beamforming technology to assist imaging, such as Coherent Plane-Wave Compounding (CPWC). The imaging quality of this method is often directly related to the number of beams, sacrificing the time advantage of single-beam plane wave imaging. This study proposes a Wavelet-based Generative Adversarial Network for Super-Resolution (WSRGAN) to meet the growing demand for high-quality imaging of single-beam plane waves. WSRGAN uses the encoding and decoding network as the generator and uses wavelet transform to replace the sampling process. Furthermore, we further designed adaptive wavelet attention to enable the model to focus attention on different levels of features at different stages. Wavelet GAN loss was proposed as GAN loss, and a new combined loss function was designed for the generator. The experiment was conducted on the Plane-wave Imaging Challenge in Medical UltraSound (PICMUS) 2016 dataset. On the point target, the Full Width at Half Maximum (FWHM) of WSRGAN reached 0.268 mm to 0.502 mm. On the cyst target, the contrast of WSRGAN reaches 26.156 dB to 37.223 dB. Among invivo targets, the Peak Signal-to-Noise Ratio (PSNR) of WSRGAN is 41.518 dB, and the Structural Similarity Index Measurement (SSIM) is 0.984. Experiments show that each module proposed by the model has a total contribution and is helpful for various experiments. All targets performed well.

Ultrasound Plane-Wave Compressed Sensing Reconstruction Method Using Intra-frame and Inter-frame Joint Multi-hypothesis and Reference Frame Multi-hypothesis Predictions

Ultrasound plane-wave imaging has the advantage of high frame rate in addition to high data volume. High data sampling rates and large amounts of data storage can become bottlenecks in ultrasound system design. Although compressed sensing technology can help reduce the burden of sampling and transmission, it achieves relatively low image quality because of its reliance solely on signal sparsity. Therefore, we proposed reconstructing the ultrasound signal by applying additional prior knowledge, such as plane-wave imaging and its echo characteristics. Inspired by multi-hypothesis prediction methods in video compression coding, the plane-wave multi-hypothesis prediction compressed sensing reconstruction method was proposed to improve the accuracy of reconstructions. We applied multi-hypothesis prediction and residual reconstruction on the plane wave to enhance the quality of reconstruction and correct predicted values. Also, to acquire high-quality hypotheses, two hypothesis acquisition schemes were evaluated, constructing search windows on both preceding and subsequent frames as well as the reference frame. Compared with traditional reconstruction methods that rely on sparsity, multi-hypothesis prediction compressed sensing methods can reduce signal reconstruction errors and significantly eliminate image artifacts. Furthermore, by using improved hypotheses, signal reconstruction and image quality can be enhanced, resulting in higher contrast. Comparative simulation experimental results based on the publicly available Plane-Wave Imaging Challenge in Medical Ultrasound (PICMUS) and acoustic radiation force imaging data sets demonstrate that the proposed method outperforms other methods in both reconstruction errors and image quality. This helps to reduce the complexity of sampling and transmission of the ultrasound system.

ARU-GAN: U-shaped GAN based on Attention and Residual connection for super-resolution reconstruction

Plane-wave ultrasound imaging technology offers high-speed imaging but lacks image quality. To improve the image spatial resolution, beam synthesis methods are used, which often compromise the temporal resolution. Herein, we propose ARU-GAN, a super-resolution reconstruction model based on residual connectivity and attention mechanisms, to address this issue. ARU-GAN comprises a Full-scale Skip-connection U-shaped Generator (FSUG) with an attention mechanism and a Residual Attention Patch Discriminator (RAPD). The former captures global and local features of the image by using full-scale skip-connections and attention mechanisms. The latter focuses on changes in the image at different scales to enhance its discriminative ability at the patch level. ARU-GAN was trained using a combined loss function on the Plane-Wave Imaging Challenge in Medical Ultrasound (PICMUS) 2016 dataset, which includes three types of targets: point targets, cyst targets, and in-vivo targets. Compared to Coherent Plane-Wave Compounding (CPWC), ARU-GAN achieved a reduction in Full Width at Half Maximum (FWHM) by 5.78%–20.30% on point targets, improved Contrast (CR) by 7.59–11.29 percentage points, and Contrast to Noise Ratio (CNR) by 30.58%–45.22% on cyst targets. On in-vivo target, ARU-GAN improved the Peak Signal-to-Noise Ratio (PSNR) by 11.94%, the Complex-Wavelet Structural Similarity Index Measurement (CW-SSIM) by 17.11%, and the Normalized Cross Correlation (NCC) by at least 2.17% compared to existing deep learning methods. In conclusion, ARU-GAN is a promising model for the super-resolution reconstruction of plane-wave medical ultrasound images. It provides a novel solution for improving image quality, which is essential for clinical practice.

Application of a 2D interpolation technique to reduce the number of steering angles required for high-quality ultrasound images in plane-wave compounding

Plane wave imaging (PWI) can reach frame rates in the kHz range. However, PWI suffers from reduced image quality due to the lack of focusing. To tackle this limitation, plane wave compounding (PWC) was introduced. PWC is based on recombining multiple PWI images generated using different steering angles. However, the number of steering angles required to achieve reasonable image quality significantly reduces the frame rate. In this study, we propose a two-dimensional (2D) interpolation technique, based on radial basis functions, which allows reducing the number of steering angles in PWC without degrading image quality. The idea is to reduce the number of steering angles and then apply 2D interpolation, along the angle dimension and directly to the RF data, to reconstruct the data related to those angles for which data were not collected. The full dataset is then utilized to generate an image. To compare this technique with standard PWC, we utilized the dataset from the Plane-wave Imaging Challenge in Medical UltraSound (PICMUS). The results show that the number of steering angles can be reduced by factor 3, thus tripling the frame rate, while achieving comparable performance with standard PWC (with 75 angles) in terms of contrast and spatialresolution.

Plane wave ultrasound imaging using compressive sensing and minimum variance beamforming

Coherent plane-wave compounding (CPWC) is a widely used technique in medical ultrasound imaging due to its high frame rate property. It is well-known that increasing the plane waves leads to improving the image quality. However, the image quality still needs to be further improved in CPWC. In this regard, a variety of methods have been proposed. In this paper, a new compressive sensing (CS) based approach is introduced with the combination of the adaptive minimum variance (MV) algorithm to further improve the image quality in terms of resolution and contrast. In the proposed method, which is called the CS-based MV technique, the CS method is used in the receive direction to produce the beamformed data for each plane wave. Then, the MV algorithm is performed in the plane wave transmit angle direction to coherently compound the images and improve the resolution. Moreover, to deal with the high computational complexity and also, the needing for high memory space during the CS method implementation, an approximation is considered which results in considerably reduced computational burden and memory space. The results obtained from the simulated point targets show that the proposed method leads to resolution improvement for about 71%, 5.5%, and 37% respectively, compared to DAS, DAS+MV, and CS+DAS beamformers. Also, the quantitative results obtained from the experimental contrast phantom in plane wave imaging challenge in medical ultrasound (PICMUS) data show a 3.02 dB, 2.57 dB, and 2.24 dB improvement of the contrast ratio metric using the proposed method compared to DAS, DAS+MV, and double-MV methods, respectively, indicating the good performance of the proposed method in image quality improvement.

High-resolution and high-contrast ultrafast ultrasound imaging using coherent plane wave adaptive compounding

Plane-wave imaging (PWI) is an interesting technique for increasing frame rate in medical ultrasound imaging, but it suffers from poor image quality. To overcome this problem, the coherent plane-wave compounding (CPWC) approach has been introduced, which provides the possibility of achieving the desired resolution and contrast by combining images obtained from different angles. In this paper, we proposed a new technique based on adaptive compounding of resulted radio frequency (RF) image data at different angles to obtain a high-resolution image. We called this method coherent plane wave adaptive compounding (CPWAC). The adaptive method used here is the minimum variance (MV) approach. An important benefit of the CPWAC method is that the calculation and inversion of the covariance matrix is done only once. We applied the proposed method on simulated, experimental, and in vivo datasets of the plane wave imaging challenge in medical ultrasound (PICMUS) data by considering 75 and 25 plane waves (PW). The results showed that the proposed CPWAC improves the lateral full width at half maximum (FWHM) by 35% for both simulated and experimental datasets, even by using 33% of the total angles compared to CPWC, which indicates simultaneous improvement of lateral resolution and frame rate. In addition, for the experimental dataset, this method has improved the {CR, CNR} about {103.8%, 100%} and {30.28%, 28%} in the near-field and far-field, respectively. Therefore, while the proposed method does not add much time overhead compared to the original CPWC, it improves the image quality of the CPWC significantly.

Coherent Plane Wave Compounding Combined With Tensor Completion Applied for Ultrafast Imaging.

To solve the problem of resolution and contrast in plane wave imaging (PWI), coherent plane wave compounding (CPWC) was introduced, in which scanning was performed at different angles, which can achieve the desired image quality by combining the images obtained from PWI at different angles. However, the application of this idea reduces the frame rate in proportion to the number of plane waves (PWs) or angles, so that in this modality, when dealing with some applications such as shear wave imaging (SWI) and strain imaging, there is always a compromise between the frame rate and the image quality. Tensor completion (TC) is a powerful technique to recover missing information of a low-rank tensor from limited observations based on rank minimization. In this article, we present an idea based on TC to make this compromise lighter; in other words, with a smaller number of angles, we can achieve the desired quality of the output image. To evaluate the proposed idea, plane wave imaging challenge in medical ultrasound (PICMUS) datasets was used, which were recorded at 75 different angles. The results of the resolution evaluation showed that using 20% of the coherent PWs and reconstructing other 80% by TC, compared with the situation of using only 20% of the coherent PWs provided a resolution improvement of 14.97% and 17.4% in the simulated and experimental point targets, respectively. Also, the results of the contrast investigation showed that the contrast ratio (CR) improved by 72.6%, 62.9%, and 111.4% in the simulated cyst target data, experimental cyst targets, and in vivo carotid cross section, respectively. The results confirmed that using 20% of the coherent PWs and reconstructing other 80% by TC, the image quality is very close to that obtained by considering all 75 angles, so that the difference in resolution is less than 2% and the difference in contrast to noise ratio (CNR) is less than 5 dB. Therefore, with this idea, it can be said that less compromise is needed; in other words, despite having a higher frame rate, an acceptable quality can be achieved.

Plane-Wave Image Reconstruction via Generative Adversarial Network and Attention Mechanism

Plane-wave imaging (PWI) has been designed and widely applied in ultrasound imaging in recent years. This technique achieves high frame rate while compromising with the image quality, such as spatial resolution and contrast. Although the compounding method is capable to improve reconstruction effect, this method limits the temporal resolution. The existing generative adversarial network (GAN)-based methods are not capable to fully extract shallow, high-frequency, and high-level information. To reconstruct high-resolution plane-wave images, we propose a reconstruction method via attention mechanism and Unet-based GAN (AUGAN). The generator of AUGAN adopts the Unet-based architecture that containing skip concatenate pathways, which can capture low-level features of different scales. Besides, in order to excavate image information comprehensively, we adopt the pixel attention to strengthen the extraction of shallow features and local aware attention to put more attention to limited high-frequency features. Moreover, perceptual loss is introduced to make the model keep more high-level information of target images. The proposed method are evaluated on datasets acquired from simulation, experimental phantom, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> targets provided by the PWI Challenge in Medical Ultrasound (PICMUS). AUGAN can achieve maximal improvements of full-width at half-maximum (FWHM) by 8.27% (simulation) and 33.21% (experiment) compared with coherent plane-wave compounding (CPWC) while 2.46% (simulation) and 5.05% (experiment) compared with Unet-based GAN (UnetGAN). The maximum contrast ratio (CR) improvements are 11.32% upon CPWC and 2.3% upon UnetGAN. For <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> data, AUGAN shows its advantage in preserving anatomical structures compared with other state-of-the-art methods in plane-wave image reconstruction. The attention ablation experiment illustrates that both pixel attention and local aware attention have further improved the plane-wave image reconstruction.

Adaptive scaling Wiener postfilter using generalized coherence factor for coherent plane-wave compounding

This paper proposes an adaptive scaling Wiener postfilter (AScW) for coherent plane-wave compounding (CPWC) to improve the image quality. AScW introduces an adaptive scale factor dependent on the signal incoherence to maintain the performance balance between good noise suppression and good robustness. AScW utilizes several plane waves with a small angular difference to calculate generalized coherence factor (GCF) for the estimation of signal incoherence and noise power. And a standard depth parameter is used to further adjust the beamforming performance of AScW and improve speckle quality. The proposed method was tested on datasets acquired from simulation, experimental phantom and in-vivo study provided by the Plane-wave Imaging Challenge in Medical Ultrasound (PICMUS). Results show that AScW applied to CPWC can provide a good speckle quality and lateral resolution, while attaining a satisfying contrast. AScW can achieve maximal improvements of speckle signal-to-noise ratio (sSNR) by 36.4% (simulation) and 44.8% (experiment) compared with GCF, while 14.4% (simulation) and 12.5% (experiment) compared with the scaled Wiener postfilter (ScW). And the maximal lateral full width at half maximum (FWHM) improvements are 51.9% upon GCF and 53.7% upon ScW. Meanwhile, the contrast of AScW is comparable to that of GCF. From the results of in-vivo study, AScW also shows its clinical application potential for the visualization of anatomical structures and hyper echoic structures in ultrasound imaging.

A Nonlinear Beamformer Based on p-th Root Compression—Application to Plane Wave Ultrasound Imaging

Ultrafast medical ultrasound imaging is necessary for 3D and 4D ultrasound imaging, and it can also achieve high temporal resolution (thousands of frames per second) for monitoring of transient biological phenomena. However, reaching such frame rates involves reduction of image quality compared with that obtained with conventional ultrasound imaging, since the latter requires each image line to be reconstructed separately with a thin ultrasonic focused beam. There are many techniques to simultaneously acquire several image lines, although at the expense of resolution and contrast, due to interference from echoes from the whole medium. In this paper, a nonlinear beamformer is applied to plane wave imaging to improve resolution and contrast of ultrasound images. The method consists of the introduction of nonlinear operations in the conventional delay-and-sum (DAS) beamforming algorithm. To recover the value of each pixel, the raw radiofrequency signals are first dynamically focused and summed on the plane wave dimension. Then, their amplitudes are compressed using the signed p t h root. After summing on the element dimension, the signed p-power is applied to restore the original dimensionality in volts. Finally, a band-pass filter is used to remove artificial harmonics introduced by these nonlinear operations. The proposed method is referred to as p-DAS, and it has been tested here on numerical and experimental data from the open access platform of the Plane wave Imaging Challenge in Medical UltraSound (PICMUS). This study demonstrates that p-DAS achieves better resolution and artifact rejection than the conventional DAS (for p = 2 with eleven plane wave imaging on experimental phantoms, the lateral resolution is improved by 21 % , and contrast ratio (CR) by 59 % ). However, like many coherence-based beamformers, it tends to distort the conventional speckle structure (contrast-to-noise-ratio (CNR) decreased by 45 % ). It is demonstrated that p-DAS, for p = 2 , is very similar to the nonlinear filtered-delay-multiply-and-sum (FDMAS) beamforming, but also that its impact on image quality can be tuned changing the value of p.

Inverse Problem of Ultrasound Beamforming With Sparsity Constraints and Regularization.

Ultrasound (US) beamforming is the process of reconstructing an image from acquired echo traces on several transducer elements. Typical beamforming approaches, such as delay-and-sum, perform simple projection operations, while techniques using statistical information also exist, e.g., adaptive, phase coherence, delay-multiply-and-sum, and sparse coding approaches. Inspired by the feasibility and success of inverse problem (IP) formulations in several image reconstruction problems, such as computed tomography, we herein devise an IP approach for US beamforming. We define a linear forward model for the synthesis of the beamformed image, and solve its IP thanks to several intuitive and physics-based constraints and regularization terms proposed. These reflect the prior knowledge about the spectra of carrier signal and spatial coherence of modulated signal. These constraints admit effective formulation through sparse representations. Our proposed method was evaluated for plane-wave imaging (PWI) that transmits unfocused waves, enabling high frame rates with large field of view at the expense of much lower image quality with conventional beamforming techniques. Results are evaluated in numerical simulations, as well as tissue-mimicking phantoms and in vivo data provided by PWI challenge in medical US. The best results achieved by our proposed techniques are 0.39-mm full-width at half-maximum for spatial resolution and 16.3-dB contrast-to-noise ratio, using a single plane-wave transmit.