Plane wave ultrasound imaging using compressive sensing and minimum variance beamforming

Improvement in Multi-Angle Plane Wave Image Quality Using Minimum Variance Beamforming with Adaptive Signal Coherence

For ultrasound multi-angle plane wave (PW) imaging, the coherent PW compounding (CPWC) method provides limited image quality because of its conventional delay-and-sum beamforming. The delay-multiply-and-sum (DMAS) method is a coherence-based algorithm that improves image quality by introducing signal coherence among either receiving channels or PW transmit angles into the image output. The degree of signal coherence in DMAS is conventionally a global value for the entire image and thus the image resolution and contrast in the target region improves at the cost of speckle quality in the background region. In this study, the adaptive DMAS (ADMAS) is proposed such that the degree of signal coherence relies on the local characteristics of the image region to maintain the background speckle quality and the corresponding contrast-to-noise ratio (CNR). Subsequently, the ADMAS algorithm is further combined with minimum variance (MV) beamforming to increase the image resolution. The optimal MV estimation is determined to be in the direction of the PW transmit angle (Tx) for multi-angle PW imaging. Our results show that, using the PICMUS dataset, TxMV-ADMAS beamforming significantly improves the image quality compared with CPWC. When the p value is globally fixed to 2 as in conventional DMAS, though the main-lobe width and the image contrast in the experiments improve from 0.57 mm and 27.0 dB in CPWC, respectively, to 0.24 mm and 38.0 dB, the corresponding CNR decreases from 12.8 to 11.3 due to the degraded speckle quality. With the proposed ADMAS algorithm, however, the adaptive p value in DMAS beamforming helps to restore the CNR value to the same level of CPWC while the improvement in image resolution and contrast remains evident.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Frame rate improvement in ultrafast coherent plane wave compounding

Coherent plane wave compounding (CPWC), as an ultrafast ultrasound imaging technique, makes a significant breakthrough in frame rate enhancement. However, there exists a compromise between the quality of the final image and the frame rate in CPWC. In this paper, we propose an efficient method to minimize the number of required emissions, and consequently, improve the frame rate, while maintaining the image quality. To this end, we down-sample the angle interval using two specific sampling factors. More precisely, we construct two different subsets, each of which consists of a few numbers of emissions. The optimal values of the angle intervals are achieved based on the beampattern that corresponds to the reference case (that is, the case where all plane waves are used). Finally, in order to keep the image quality comparable with the reference case, we apply some modifications to the image reconstruction procedure. In the proposed algorithm, the Delay-and-Sum beamformed images of two considered subsets are convolved to achieve the final reconstructed image. The obtained results confirm the efficiency of the proposed method in terms of frame rate improvement compared to the reference case. In particular, by using the proposed method, the required emissions in PICMUS data reduce to 16, which is 4.6 times smaller compared to the reference case. Also, the gCNR values of the proposed method and the reference case are obtained as 0.98 and 0.97, respectively, for in-vivo dataset. This demonstrates that the proposed method successfully preserves the quality of the reconstructed image by using much fewer emissions.

Texture analysis of ultrasound images obtained with different beamforming techniques and dynamic ranges - A robustness study

Texture analysis of medical images gives quantitative information about the tissue characterization for possible pathology discrimination. Ultrasound B-mode images are generated through a process called beamforming. Then, to obtain the final 8-bit image, the dynamic range value must be set. It is currently unknown how different beamforming techniques or dynamic range values may alter the final image texture. We provide here a robustness analysis of first and higher order texture features using six beamforming methods and seven dynamic range values, on experimental phantom and in vivo musculoskeletal images acquired using two different ultrasound research scanners. To investigate the repeatability of the texture parameters, we applied the multivariate analysis of variance (MANOVA) and estimated the intraclass correlation coefficient (ICC) on the texture features calculated on the B-mode images created with different beamforming methods and dynamic range values. We demonstrated the high repeatability of texture features when varying the dynamic range and showed texture features can differentiate between beamforming methods through a MANOVA analysis, hinting at the potential future clinical application of specific beamformers.

Ultrafast Plane Wave Imaging Using Tensor Completion-Based Minimum Variance Algorithm

OBJECTIVE

Coherent plane wave compounding (CPWC) imaging is an efficient technique in high-frame-rate ultrasound imaging. To improve the image quality obtained from the CPWC, the adaptive minimum variance (MV) algorithm can be used. However, the high computational complexity of this algorithm negatively affects the frame rate. In other words, achieving a high frame rate and high-quality features simultaneously remains a challenge in medical ultrasound imaging. The aim of the work described here was to develop an algorithm to tackle this challenge and improve the frame rate while preserving the good quality of the resulting image.

METHODS

A tensor completion (TC)-based MV algorithm is proposed to simultaneously improve the frame rate and image quality in CPWC. In the proposed method, the MV algorithm is applied to a limited number of pixels in the beamforming grid. Then, the appropriate values are assigned to the remaining unprocessed pixels by using the TC algorithm. The proposed algorithm speeds up the beamforming process, and consequently, improves the frame rate.

RESULTS

The computational complexity of the proposed TC-based MV algorithm is reduced compared with that of the conventional MV algorithm while the good quality of this algorithm is preserved. The results indicate that, in particular, by processing 40% of the beamforming grid using the MV beamformer followed by the TC algorithm, a reconstructed image comparable to that in the case in which the MV algorithm is performed on the full beamforming grid is obtained; the difference between the contrast-to-noise ratio evaluation metric between these two cases is about 0.16 dB for the experimental-resolution phantom. Also, the resulting images obtained from the MV algorithm and the TC-based MV method have the same resolution, indicating that the TC-based MV algorithm can successfully achieve the quality of the MV algorithm with a lower computational complexity.

CONCLUSION

The TC-based MV algorithm is proposed in CPWC with the goal of improving frame rate and image quality. Qualitative and quantitative results reveal that by use of the proposed algorithm, the quality of the reconstructed image will be comparable to that of the conventional MV algorithm, and the frame rate will be improved.