Enhancing image reconstruction in photoacoustic imaging using spatial coherence mean-to-standard-deviation factor beamforming.

Delay-and-sum (DAS) beamformers, when applied to photoacoustic (PA) image reconstruction, produce strong sidelobes due to the absence of transmit focusing. Consequently, DAS PA images are often severely degraded by strong off-axis clutter. For preclinical in vivo cardiac PA imaging, the presence of these noise artifacts hampers the detectability and interpretation of PA signals from the myocardial wall, crucial for studying blood-dominated cardiac pathological information and to complement functional information derived from ultrasound imaging. In this article, we present PA subaperture processing (PSAP), an adaptive beamforming method, to mitigate these image degrading effects. In PSAP, a pair of DAS reconstructed images is formed by splitting the received channel data into two complementary nonoverlapping subapertures. Then, a weighting matrix is derived by analyzing the correlation between subaperture beamformed images and multiplied with the full-aperture DAS PA image to reduce sidelobes and incoherent clutter. We validated PSAP using numerical simulation studies using point target, diffuse inclusion and microvasculature imaging, and in vivo feasibility studies on five healthy murine models. Qualitative and quantitative analysis demonstrate improvements in PAI image quality with PSAP compared to DAS and coherence factor weighted DAS (DAS CF ). PSAP demonstrated improved target detectability with a higher generalized contrast-to-noise (gCNR) ratio in vasculature simulations where PSAP produces 19.61% and 19.53% higher gCNRs than DAS and DAS CF , respectively. Furthermore, PSAP provided higher image contrast quantified using contrast ratio (CR) (e.g., PSAP produces 89.26% and 11.90% higher CR than DAS and DAS CF in vasculature simulations) and improved clutter suppression.

In order to improve the efficiency of the eigenspace-based minimum variance (ESBMV) algorithm, a low-complexity minimum variance combined with eigenvalue decomposition (IBMV) algorithm is proposed. Firstly, the echo signals are transformed into beam domain by the discrete cosine transformation. Then, the eigenvalue decomposition of the sample covariance matrix is used to extract the signal subspace. The largest eigenvalue and its corresponding eigenvector can be extracted and other eigenvalues are taken the same value when the trace of sample covariance matrix remains unchanged. By these ways, the inverse operation of matrix can be simplified to the multiplication of vectors. In order to verify the validity of the proposed algorithm, Field II is introduced to test point targets and the cyst phantom. Experimental results indicate that the proposed IBMV with robustness to noises is obviously more efficient than ESBMV. Besides, the imaging quality of IBMV is better than delay and sum (DAS) method, minimum variance (MV) algorithm and beam domain minimum variance (BMV) method in terms of resolution and contrast ratio.

Undoubtedly, photoacoustic tomography (PAT) is a promising technique unveiling physiological information in the biomedical imaging field. However, targeting optically/acoustically non-uniform biological tissues and receiving signals with finite aperture/band-limited ultrasonic transducers have been practical limitations that hinder the clinical application of PAT. This study closely analyzes the effects of four main limiting factors named limited view (LV), limited bandwidth (LB), lossy medium (LM), and heterogeneous medium (HM) and compared the adverse effects given from the four factors. First, radiofrequency (RF) data was generated assuming a realistic situation using k-Wave, a MATLAB-based wave propagation-simulating tool. A single 4 mm-sized circular target was used, and physical parameters of the detector array were designed referring to a commercial linear probe (GE9LD, General Electric). Supposing double-aperture, full-band signal reception from the lossless, homogeneous medium as a baseline, RF data were acquired under a successive accumulation of the limiting factors in order of LV, LB, LM, and HM. Second, the obtained RF datasets were reconstructed with 7 representative beamforming algorithms; delay and sum (DAS), delay multiply and sum, pth root DAS, minimum variance, filtered back-projection, frequency-domain reconstruction, and time reversal. Among obtained images, common graphical features for each limiting factor were specified. Lastly, we quantitatively assessed the similarity between the image and the actual target with peak signal-to-noise ratio and structural similarity as performance indices. As a result, the characteristic features from each limiting factor were commonly identified on the reconstructed image, and the performance indices sequentially degraded following the sequence of practical conditions.

In photoacoustic imaging, delay-and-sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely delay-multiply-and-sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a beamformer is introduced using minimum variance (MV) adaptive beamforming combined with DMAS, so-called minimum variance-based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45mm MVB-DMAS results in about 31, 18, and 8dB sidelobes reduction compared to DAS, MV, and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96%, 94%, and 45% and signal-to-noise ratio about 89%, 15%, and 35% compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33mm of the experimental images, MVB-DMAS results in about 20dB sidelobes reduction in comparison with other beamformers.

Objective:Spinal fusion surgeries require accurate placement of pedicle screws in anatomic corridors without breaching bone boundaries. We are developing a combined ultrasound and photoacoustic image guidance system to avoid pedicle screw misplacement and accidental bone breaches, which can lead to nerve damage.Methods:Pedicle cannulation was performed on a human cadaver, with co-registered photoacoustic and ultrasound images acquired at various time points during the procedure. Bony landmarks obtained from coherence-based ultrasound images of lumbar vertebrae were registered to post-operative CT images. Registration methods were additionally tested on an ex vivo caprine vertebra.Results:Locally weighted short-lag spatial coherence (LW-SLSC) ultrasound imaging enhanced the visualization of bony structures with generalized contrast-to-noise ratios (gCNRs) of 0.99 and 0.98–1.00 in the caprine and human vertebrae, respectively. Short-lag spatial coherence (SLSC) and amplitude-based delay-and-sum (DAS) ultrasound imaging generally produced lower gCNRs of 0.98 and 0.84, respectively, in the caprine vertebra and 0.84–0.93 and 0.34–0.99, respectively, in the human vertebrae. The mean ± standard deviation of the area of −6 dB contours created from DAS photoacoustic images acquired with an optical fiber inserted in prepared pedicle holes (i.e., fiber surrounded by cancellous bone) and holes created after intentional breaches (i.e., fiber exposed to cortical bone) was 10.06±5.22 mm2 and 2.47±0.96 mm2, respectively (p < 0.01).Conclusions:Coherence-based LW-SLSC and SLSC beamforming improved visualization of bony anatomical landmarks for ultrasound-to-CT registration, while amplitude-based DAS beamforming successfully distinguished photoacoustic signals within the pedicle from less desirable signals characteristic of impending bone breaches.Significance:These results are promising to improve visual registration of ultrasound and photoacoustic images with CT images, as well as to assist surgeons with identifying and avoiding impending bone breaches during pedicle cannulation in spinal fusion surgeries.

In vivo cardiac photoacoustic imaging (PAI) done with delay-and-sum (DAS) beamforming is susceptible to lower image quality due to sidelobes, along with quasistatic and incoherent clutter. These artifacts impose constraints in the localization of the myocardial wall based on these images. Here, we report on combining photoacoustic sub-aperture processing (PSAP) and spatiotemporal singular value decomposition (STSVD) to address these issues. Sub-aperture beamformed images filtered using STSVD were utilized to derive a weighting matrix to reduce DAS clutter. The proposed method is termed as coupled PSAP and STSVD (PSAP+STSVD). PSAP+STSVD was compared against DAS, PSAP and STSVD using single wavelength PAI data collected from four healthy mice. Quantitative comparison was done using contrast ratio (CR) and generalized contrast to noise ratio (gCNR). PSAP+STSVD provided the highest CR (e.g. at diastolic phase, mean CR values for DAS, STSVD, PSAP and PSAP+STSVD were 8.78, 14.58, 18.04 and 25.88 dB) while STSVD had the highest gCNR (e.g. at diastolic phase, mean gCNR values for DAS, STSVD, PSAP and PSAP+STSVD were 0.53, 0.75, 0.56 and 0.71).

Photoacoustic imaging (PAI), is a promising medical imaging technique that\nprovides the high contrast of the optical imaging and the resolution of\nultrasound (US) imaging. Among all the methods, Three-dimensional (3D) PAI\nprovides a high resolution and accuracy. One of the most common algorithms for\n3D PA image reconstruction is delay-and-sum (DAS). However, the quality of the\nreconstructed image obtained from this algorithm is not satisfying, having high\nlevel of sidelobes and a wide mainlobe. In this paper, delay-multiply-and-sum\n(DMAS) algorithm is suggested to overcome these limitations in 3D PAI. It is\nshown that DMAS algorithm is an appropriate reconstruction technique for 3D PAI\nand the reconstructed images using this algorithm are improved in the terms of\nthe width of mainlobe and sidelobes, compared to DAS. Also, the quantitative\nresults show that DMAS improves signal-to-noise ratio (SNR) and\nfull-width-half-maximum (FWHM) for about 25 dB and 0.2 mm, respectively,\ncompared to DAS.\n

Recently, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared to non-adaptive delay-and-sum (DAS) beamformer. Most of the adaptive beamformers presented in the ultrasound imaging literature are based on the minimum variance (MV) beamformer which can improve the imaging resolution while retaining the contrast. The main objective of this research is to present a beamformer which could improve the imaging resolution and contrast, at the same time. In this paper, we have applied the eigenspace-based MV (EIBMV) beamformer to medical ultrasound imaging and shown the simultaneous improvement in imaging resolution and contrast. EIBMV beamformer utilizes the eigenstructure of the covariance matrix to enhance the performance of the MV beamformer. The weight vector of the EIBMV is found by projecting the obtained MV weight vector onto a vector subspace constructed from the eigenstructure of the covariance matrix. EIBMV beamformer shows a significant reduction in the sidelobe levels while representing the same mainlobe width as the MV. Specifically, in the point targets phantom, the proposed method decreases the sidelobes about 22 dB and 29 dB compared to MV and DAS, respectively. Furthermore, the proposed method increases the contrast of simulated cyst phantom about 8.6 dB and 14.1 dB and also improves the contrast-to-noise ratio about 31% and 61%, compared to MV and DAS, respectively.

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.

Beamforming (BF) in ultrasound (US) imaging has significant impact on the quality of the final image, controlling its resolution and contrast. Despite its low spatial resolution and contrast, delay-and-sum (DAS) is still extensively used nowadays in clinical applications, due to its real-time capabilities. The most common alternatives are minimum variance (MV) method and its variants, which overcome the drawbacks of DAS, at the cost of higher computational complexity that limits its utilization in real-time applications. In this paper, we propose to perform BF in US imaging through a regularized inverse problem based on a linear model relating the reflected echoes to the signal to be recovered. Our approach presents two major advantages: 1) its flexibility in the choice of statistical assumptions on the signal to be beamformed (Laplacian and Gaussian statistics are tested herein) and 2) its robustness to a reduced number of pulse emissions. The proposed framework is flexible and allows for choosing the right tradeoff between noise suppression and sharpness of the resulted image. We illustrate the performance of our approach on both simulated and experimental data, with in vivo examples of carotid and thyroid. Compared with DAS, MV, and two other recently published BF techniques, our method offers better spatial resolution, respectively contrast, when using Laplacian and Gaussian priors.

Constraint matrix projection minimum variance beamformer based on adaptive decomposition threshold for ultrasound imaging

Application of condition coherence factor based on truncated composite method in ultrasound imaging

Photoacoustic imaging (PAI) is a promising medical imaging modality providing the spatial resolution of ultrasound imaging and the contrast of optical imaging. For linear-array PAI, a beamformer can be used as the reconstruction algorithm. Delay-and-sum (DAS) is the most prevalent beamforming algorithm in PAI. However, using DAS beamformer leads to low-resolution images as well as high sidelobes due to nondesired contribution of off-axis signals. Coherence factor (CF) is a weighting method in which each pixel of the reconstructed image is weighted, based on the spatial spectrum of the aperture, to mainly improve the contrast. We demonstrate that the numerator of the formula of CF contains a DAS algebra and propose the use of a delay-multiply-and-sum beamformer instead of the available DAS on the numerator. The proposed weighting technique, modified CF (MCF), has been evaluated numerically and experimentally compared to CF. It was shown that MCF leads to lower sidelobes and better detectable targets. The quantitative results of the experiment (using wire targets) show that MCF leads to for about 45% and 40% improvement, in comparison with CF, in the terms of signal-to-noise ratio and full-width-half-maximum, respectively.

In this paper, the adaptive, minimum variance (MV) beam-former is applied to medical ultrasound imaging. The significant resolution and contrast gain provided by the adaptive, minimum variance (MV) beamformer, introduces the possibility of plane wave (PW) ultrasound imaging. Data is obtained using Field II and a 7 MHz, 128-elements, linear array transducer with lambda/2-spacing. MV is compared to the conventional delay-and-sum (DS) beamformer with Boxcar and Hanning weights. Furthermore, the PW images are compared to the a conventional ultrasound image, obtained from a linear scan sequence. The four approaches, {Linear Scan, DS Boxcar, DS Hanning, MV}, have full width at half maximum of {0.82, 0.71, 1.28, 0.12} mm and peak side-lobe levels of {-40.1,-16.8,-34.4,-57.0} dB.

Although the minimum variance (MV) beamformer can provide enhancement in both resolution and contrast of ultrasound images when compared with conventional delay-and-sum (DAS) beamforming, its clinical application is limited by its sensitivity to phase aberrations. Several robust MV beamformers have been proposed, but present limitations when faced with second order phase aberration. Additionally, the information that can be obtained from phase aberration correction (PAC) methods is not utilized and the gain in robustness is usually accompanied by a loss in resolution or contrast. In the current work, we propose a new robust MV beamformer that uses a locally adaptive PAC (LAPAC-MV) in order to cope with the aformentioned difficulties. The method is based on a PAC estimation at every point in the image domain and a profile decision logic is included in the algorithm to take into account regions with low signal power. Preliminary results in full wave simulations and experimental phantom data are reported. In the resulting images, LAPAC-MV achieved enhancement in resolution of 0.03 mm, and reduction in sidelobe levels of around 8 dB when compared with precorrected DAS (DAS-PC) or precorrected MV (MV-PC). Contrast of LAPAC-MV is equivalent to MV-PC in cyst target simulations but the former showed improvements of approximately 1 dB in contrast in experiments with a 20 mm thick tissue aberrator.