Linear-array Photoacoustic Tomography Research Articles

Learning-based sound speed estimation and aberration correction for linear-array photoacoustic imaging

Photoacoustic (PA) image reconstruction involves acoustic inversion that necessitates the specification of the speed of sound (SoS) within the medium of propagation. Due to the lack of information on the spatial distribution of the SoS within heterogeneous soft tissue, a homogeneous SoS distribution (such as 1540 m/s) is typically assumed in PA image reconstruction, similar to that of ultrasound (US) imaging. Failure to compensate for the SoS variations leads to aberration artefacts, deteriorating the image quality. Various methods have been proposed to address this issue, but they usually involve complex hardware and/or time-consuming algorithms, hindering clinical translation. In this work, we introduce a deep learning framework for SoS estimation and subsequent aberration correction in a dual-modal PA/US imaging system exploiting a clinical US probe. As the acquired PA and US images were inherently co-registered, the estimated SoS distribution from US channel data using a deep neural network was incorporated for accurate PA image reconstruction. The framework comprised an initial pre-training stage based on digital phantoms, which was further enhanced through transfer learning using physical phantom data and associated SoS maps obtained from measurements. This framework achieved a root mean square error of 10.2 m/s and 15.2 m/s for SoS estimation on digital and physical phantoms, respectively and structural similarity index measures of up to 0.88 for PA reconstructions compared to the conventional approach of 0.69. A maximum of 1.2 times improvement in the signal-to-noise ratio of PA images was further demonstrated with a human volunteer study. Our results show that the proposed framework could be valuable in various clinical and preclinical applications to enhance PA image reconstruction.

Line Illumination in Linear Array Photoacoustic Imaging Using a Powell Lens: A Proof-of-Concept Study

Photoacoustic imaging (PAI) is a rapidly developing biomedical imaging technology. Linear array-based photoacoustic tomography (LA-PAT) is one of the most popular configurations of cross-sectional PAI due to its simplicity and clinical translatability. However, when using an optical fiber for LA-PAT, the optical beam shape is deformed due to rapid divergence and, therefore, a larger area on the tissue is illuminated (and the illumination across the linear array is non-uniform), leading to the acquisition of PA signals outside the desired cross-section, which generates artifacts and degrades image resolution. A Powell lens is an optical element that converts a circular beam profile to a nearly linear flat-top profile. In this paper, a Powell lens is used to generate a uniform line illumination scheme that is evaluated with Zemax OpticStudio 2023 R1.02. The system is then characterized experimentally, and the performance is compared with a conventional illumination scheme in LA-PAT.

Evaluation of 10 current image reconstruction algorithms for linear array photoacoustic imaging.

Various reconstruction algorithms have been implemented for linear array photoacoustic imaging systems with the goal of accurately reconstructing the strength absorbers within the tissue being imaged. Since the existing algorithms have been introduced by different research groups and the context of performance evaluation was not consistent, it is difficult to make a fair comparison between them. In this study, we systematically compared the performance of 10 published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. Evaluations were conducted based on lateral resolution of the reconstructed images, computational time, target detectability, and noise sensitivity. We anticipate the outcome of this study will assist researchers in selecting appropriate algorithms for their linear array PA imaging applications.

The researches on convolutional beamforming for linear-array photoacoustic tomography

The convolutional beamforming algorithm (COBA) which can be easily implemented by fast Fourier transform (FFT) and is suitable for real-time photoacoustic tomography (PAT) is introduced. In order to reveal the imaging effects of COBA, the photoacoustic images reconstructed by COBA, delay-and-sum algorithm (DAS), and minimum variance (MV) are investigated. The results demonstrate that the COBA significantly improves the lateral resolution and the contrast of the photoacoustic images. Moreover, the coherence factor (CF) as an adaptive weighting is used in COBA, the combined method shows that better lateral resolution and contrast of photoacoustic images can be obtained, which can be a potential imaging method for linear-array PAT.

Eigenspace-based minimum variance beamformer combined with sign coherence factor: Application to linear-array photoacoustic imaging

Photoacoustic (PA) imaging combining the advantages of high resolution of ultrasound imaging and high contrast of optical imaging provides images with good quality. PA imaging often suffers from disadvantages such as clutter noises and decreased signal-to-noise-ratio at higher depths. One studied method to reduce clutter noises is to use weighting factors such as coherence factor (CF) and its modified versions that improve resolution and contrast of images. In this study, we combined the Eigen-space based minimum variance (EIBMV) beamformer with the sign coherence factor (SCF) and show the ability of these methods for noise reduction when they are used in combination with each other. In addition, we compared the proposed method with delay-and-sum (DAS) and minimum variance (MV) beamformers in simulated and experimental studies. The simulation results show that the proposed EIBMV-SCF method improves the SNR about 94 dB, 87.65 dB, and 62.29 dB compared to the DAS, MV, and EIBMV, respectively, and the corresponding improvements were 79.37/34.43 dB, 77.25/26.96 dB, and 33.19/25.56 dB in the ex vivo/in vivo experiments.

Image improvement in linear-array photoacoustic imaging using high resolution coherence factor weighting technique

BackgroundIn Photoacoustic imaging (PAI), the most prevalent beamforming algorithm is delay-and-sum (DAS) due to its simple implementation. However, it results in a low quality image affected by the high level of sidelobes. Coherence factor (CF) can be used to address the sidelobes in the reconstructed images by DAS, but the resolution improvement is not good enough, compared to the high resolution beamformers such as minimum variance (MV). In this paper, it is proposed to use high-resolution-CF (HRCF) weighting technique in which MV is used instead of the existing DAS in the formula of the conventional CF.ResultsThe higher performance of HRCF is proved numerically and experimentally. The quantitative results obtained with the simulations show that at the depth of 40 mm, in comparison with DAS+CF and MV+CF, HRCF improves the full-width-half-maximum of about 91% and 15% and the signal-to-noise ratio about 40% and 14%, respectively.ConclusionProposed method provides a high resolution along with a low level of sidelobes for PAI.

Sparsity-based beamforming to enhance two-dimensional linear-array photoacoustic tomography

In linear-array photoacoustic imaging (PAI), beamforming methods can be used to reconstruct the images. Delay-and-sum (DAS) beamformer is extensively used due to its simple implementation. However, this algorithm results in high level of sidelobes and low resolution. In this paper, it is proposed to form the photoacoustic (PA) images through a regularized inverse problem to address these limitations and improve the image quality. We define a forward/backward problem of the beamforming and solve the inverse problem using a sparse constraint added to the model which forces the sparsity of the output beamformed data. It is shown that the proposed Sparse beamforming (SB) method is robust against noise due to the sparsity nature of the problem. Numerical results show that the SB method improves the signal-to-noise ratio (SNR) for about 98.69 dB, 82.26 dB and 74.73 dB, in average, compared to DAS, delay-multiply-and-sum (DMAS) and double stage-DMAS (DS-DMAS), respectively. Also, quantitative evaluation of the experimental results shows a significant noise reduction using SB algorithm. In particular, the contrast ratio of the wire phantom at the depth of 30 mm is improved about 103.97 dB, 82.16 dB and 65.77 dB compared to DAS, DMAS and DS-DMAS algorithms, respectively, indicating a better performance of the proposed SB in terms of noise reduction.

Eigenspace-Based Minimum Variance Combined With Delay Multiply and Sum Beamformer: Application to Linear-Array Photoacoustic Imaging

In Photoacoustic imaging, Delay-and-Sum (DAS) algorithm is the most commonly used beamformer. However, it leads to a low resolution and high level of sidelobes. Delay-Multiply-and-Sum (DMAS) was introduced to provide lower sidelobes compared to DAS. In this paper, to improve the resolution and sidelobes of DMAS, a novel beamformer is introduced using Eigenspace-Based Minimum Variance (EIBMV) method combined with DMAS, namely EIBMV-DMAS. It is shown that expanding the DMAS algebra leads to several terms which can be interpreted as DAS. Using the EIBMV adaptive beamforming instead of the existing DAS (inside the DMAS algebra expansion) is proposed to improve the image quality. EIBMV-DMAS is evaluated numerically and experimentally. It is shown that EIBMV-DMAS outperforms DAS, DMAS and EIBMV in terms of resolution and sidelobes. In particular, at the depth of 11 mm of the experimental images, EIBMV-DMAS results in about 113 dB and 50 dB sidelobe reduction, compared to DMAS and EIBMV, respectively. At the depth of 7 mm, for the experimental images, the quantitative results indicate that EIBMV-DMAS leads to improvement in Signal-to-Noise Ratio (SNR) of about 75% and 34%, compared to DMAS and EIBMV, respectively.

Synthetic aperture-based linear-array photoacoustic tomography considering the aperture orientation effect

The synthetic aperture-based linear-array photoacoustic tomography (PAT) was proposed to address the limited-view shortcomings of the single aperture, but the detection field of view (FOV) determined by the aperture orientation effect was not fully considered yet, leading to the limited-view observation and image resolution degradation. Herein, the aperture orientation effect was proposed from the theoretical model and then it was verified via both the numerical simulation and phantom experiment. Different orientations were enumerated sequentially in the simulation to approximate the ideal full-view case for the optimal detection FOV, considering the detection pattern of the linear-array transducer. As a result, the corresponding optimal aperture orientation was 60[Formula: see text] if the synthetic aperture was seamlessly established by three single linear arrays, where the overlapped detection pattern was optimized from the individual linear-array transducer at the adjacent positions. Therefore, the limited-view artifacts were minimized and the image resolution was enhanced in this aperture orientation. This study showed that the aperture orientation had great influence on the optimal detection FOV in the synthetic aperture configuration, where the full-view imaging quality and enhanced image resolution could be achieved.

Efficient nonlinear beamformer based on P'th root of detected signals for linear-array photoacoustic tomography: application to sentinel lymph node imaging.

In linear-array transducer-based photoacoustic (PA) imaging, B-scan PA images are formed using the raw channel PA signals. Delay-and-sum (DAS) is the most prevalent algorithm due to its simple implementation, but it leads to low-quality images. Delay-multiply-and-sum (DMAS) provides a higher image quality in comparison with DAS while it imposes a computational burden of O ( M2 ) . We introduce a nonlinear (NL) beamformer for linear-array PA imaging, which uses the p'th root of the detected signals and imposes the complexity of DAS [O ( M ) ]. The proposed algorithm is evaluated numerically and experimentally [wire-target and in-vivo sentinel lymph node (SLN) imaging], and the effects of the parameter p are investigated. The results show that the NL algorithm, using a root of p (NL_p), leads to lower sidelobes and higher signal-to-noise ratio compared with DAS and DMAS, for (p > 2). The sidelobes level (for the wire-target phantom), at the depth of 11.4mm, are about -31, -52, -52, -67, -88, and -109 dB, for DAS, DMAS, NL_2, NL_3, NL_4, and NL_5, respectively, indicating the superiority of the NL_p algorithm. In addition, the best value of p for SLN imaging is reported to be 12.

Variable speed of sound compensation in the linear-array photoacoustic tomography using a multi-stencils fast marching method

Abstract Despite the promising clinical application of linear-array photoacoustic tomography, it has been shown the variable speed of sound would severely affect the photoacoustic imaging quality, resulting in the target deterioration and inaccurate depth positioning in the conventional constant speed assumed delay and sum (DAS) reconstruction. By contrast, multi-stencils fast marching (MSFM) method is able to produce accurate time delay map for the tissue with inhomogeneous acoustic speed. This study explored the combination of DAS reconstruction algorithm with the MSFM approach to reduce the imaging distortions due to the speed spatial variation, where the target structure and target position in depth could be measured more precisely. To validate the performance of the proposed method, numerical, phantom, and in vivo photoacoustic studies were conducted with the qualitative and quantitative analysis, especially in the detection of mouse deep brain tumor with an intact skull.

Preoperative measurement of cutaneous melanoma and nevi thickness with photoacoustic imaging.

.Photoacoustic imaging (PAI) is an emerging biomedical imaging technology, which can potentially be used in the clinic to preoperatively measure melanoma thickness and guide biopsy depth and sample location. We recruited 27 patients with pigmented cutaneous lesions suspicious for melanoma to test the feasibility of a handheld linear-array photoacoustic probe in imaging lesion architecture and measuring tumor depth. The probe was assessed in terms of measurement accuracy, image quality, and ease of application. Photoacoustic scans included single wavelength, spectral unmixing, and three-dimensional (3-D) scans. The photoacoustically measured lesion thickness gave a high correlation with the histological thickness measured from resected surgical samples (, for melanomas, , for nevi). Thickness measurements were possible for 23 of 26 cases for nevi and all (6) cases for melanoma. Our results show that handheld, linear-array PAI is highly reliable in measuring cutaneous lesion thickness in vivo, and can potentially be used to inform biopsy procedure and improve patient management.

Effects of Element Directivity in Linear-Array Photoacoustic Imaging

Effects of Element Directivity in Linear-Array Photoacoustic Imaging

Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm.

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.

Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging.

Photoacoustic imaging (PAI) is an emerging medical imaging modality capable of providing high spatial resolution of Ultrasound (US) imaging and high contrast of optical imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in PAI. However, using DAS beamformer leads to low resolution images and considerable contribution of off-axis signals. A new paradigm namely delay-multiply-and-sum (DMAS), which was originally used as a reconstruction algorithm in confocal microwave imaging, was introduced to overcome the challenges in DAS. DMAS was used in PAI systems and it was shown that this algorithm results in resolution improvement and sidelobe degrading. However, DMAS is still sensitive to high levels of noise, and resolution improvement is not satisfying. Here, we propose a novel algorithm based on DAS algebra inside DMAS formula expansion, double stage DMAS (DS-DMAS), which improves the image resolution and levels of sidelobe, and is much less sensitive to high level of noise compared to DMAS. The performance of DS-DMAS algorithm is evaluated numerically and experimentally. The resulted images are evaluated qualitatively and quantitatively using established quality metrics including signal-to-noise ratio (SNR), full-width-half-maximum (FWHM) and contrast ratio (CR). It is shown that DS-DMAS outperforms DAS and DMAS at the expense of higher computational load. DS-DMAS reduces the lateral valley for about 15 dB and improves the SNR and FWHM better than 13% and 30%, respectively. Moreover, the levels of sidelobe are reduced for about 10 dB in comparison with those in DMAS.

Slit-enabled linear-array photoacoustic tomography with near isotropic spatial resolution in three dimensions.

Due to its unique capability of visualizing optical absorption in deep tissues, photoacoustic tomography is increasingly used in biomedical imaging. Among various types of transducer arrays, the linear array is perhaps the most widely used in photoacoustic tomography because it is commercially available and readily allows ultrasound imaging. However, the three-dimensional imaging capability of a linear array is limited due to its poor elevational resolution. While various scanning schemes have been proposed to address this problem, they all suffer from long scanning time to the best of our knowledge. To address this issue, we introduce slit-enabled three-dimensional photoacoustic tomography. The metal slit, placed at the array focus, causes the incoming photoacoustic waves to diffract along the elevation direction and, hence, significantly improves the elevation detection aperture and resolution. We tested the new system in both phantoms and animals. The slit improves the elevation resolution by 10 times without compromising scanning time.