Impact of sensor apodization on the tangential resolution in photoacoustic tomography

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.

Feasibility of photoacoustic-guided ultrasound treatment for port wine stains

BACKGROUND AND OBJECTIVES

Port wine birthmark, also known as port wine stain (PWS) is a skin discoloration characterized by red/purple patches caused by vascular malformation. PWS is typically treated by using lasers to destroy abnormal blood vessels. The laser heating facilitates selective photothermolysis of the vessels and attenuates quickly in the tissue due to high optical scattering. Therefore, residual abnormal capillaries deep in the tissue survive and often lead to the resurgence of PWS. Ultrasound (US) has also been proposed to treat PWS, however, it is nonselective with respect to the vasculature but penetrates deeper into the tissue. We aim to study the feasibility of a hybrid PWS treatment modality combining the advantages of both modalities.

MATERIALS AND METHODS

In this manuscript, we propose a photoacoustic (PA) guided US focusing methodology for PWS treatment which combines the optical contrast-based selectivity with US penetration to focus the US energy onto the vasculature. The PA signals collected by the transducers, when time-reversed, amplified, and transmitted, converge onto the PWS, thus minimally affecting the neighboring tissue. We performed two- and three-dimensional simulations that mimic realistic transducers and medium properties in this proof of concept study.

RESULTS

The time-reversed PA signals when transmitted from the transducers converged onto the vasculature, as expected, thus reducing the heating of the neighboring tissue. We observed that while the US focus is indeed affected due to experimental factors such as limited-view, large detector separation and finite detection bandwidth, and so forth, the US did focus completely or partially onto the vasculature demonstrating the feasibility of the proposed methodology.

CONCLUSION

The results demonstrate the potential of the proposed methodology for PWS treatment. This treatment method can destroy the deeper capillaries while minimally heating the neighboring tissue, thus reducing the chances of the resurgence of PWS and as well as cosmetic scarring.

Higher-order correlation based real-time beamforming in photoacoustic imaging

Although a delay-and-sum (DAS) beamformer is best suited for real-time photoacoustic (PA) image formation, the reconstructed images are often afflicted by noises, sidelobes, and other intense artifacts due to inaccurate assumptions in PA signal correlation. The present work aims to develop a reconstruction method that reduces the occurrence of sidelobes and artifacts and thus improves the reconstructed image quality or imaging performance. This beamformer is fundamentally based on higher-order signal correlation wherein a higher number of delayed PA signals-compared to conventional delay-multiply-and-sum (DMAS)-are combined and summed up. The proposed technique provides significant improvements in resolution, contrast, and signal-to-noise ratio (SNR) compared to traditional beamformers. For real-time implementation, the proposed algorithms were simplified, and their computational complexities were shrunk to the order of DAS [O(N)]. A GPU based study was also performed to validate the real-time capability of the proposed beamformers. For validation studies, both numerical simulation and experiments were conducted. Quantitative evaluation studies involving SNR, contrast ratio, generalized contrast-to-noise ratio, and FWHM demonstrate that the proposed higher-order DMAS beamformer is superior in PA image reconstruction. Conclusively, the proposed beamformer uniquely facilitates real-time PA image reconstruction with an achievable frame rate close to DAS and DMAS but with better imaging performance, which holds promise for real-time PA imaging and its clinical applications.

Exploring polynomial based interpolation schemes for photoacoustic tomographic image reconstruction

Photoacoustic tomography (PAT) imaging employing polynomial-based interpolation methods is discussed. Nearest-neighbor, bilinear, bicubic and biquintic algorithms were implemented for the construction of the model matrix, and images were formed using the Tikhonov regularization and total variation (TV) minimization procedures. The performance of the interpolation methods was assessed by comparing the reconstructed images of three numerical and two experimental phantoms. The numerical and experimental studies demonstrate that the performance of the interpolation schemes is nearly equal for large PA sources. The simplest nearest-neighbor technique provides better image reconstruction for a sparse source compared to the others. The nearest-neighbor protocol may be adopted in practice for vascular imaging using PAT.

Photoacoustic Signal Simulation Using Discrete Particle Approach and its Application in Tomography

A theoretical framework for photoacoustic (PA) signal simulation using a discrete particle approach is discussed, and the tomographic image reconstruction using such signals is reported. Various numerical phantoms in two dimensions were constructed by inserting monodisperse/polydisperse solid circles/disks of uniform strength occupying regular or random locations within the imaging region. In particular, a blood vessel network phantom was simulated by positioning solid circles mimicking red blood cells randomly within the vessel using a Monte Carlo method. The PA signal from a single disk was obtained by numerically evaluating the analytical formula, and then, such signals from many disks were summed up linearly to generate the resultant signals at detector locations. Classical backprojection and time-reversal algorithms were employed to form reconstructed images. Two model-based approaches, namely impulse response-based (IRB) and interpolation-based (IPB) methods, were also deployed for image reconstruction. Some standard parameters were calculated to assess the performance of these reconstruction algorithms. The simulation results demonstrate that the Monte Carlo method can be applied in practice for the fast simulation of tissue realization keeping microscopic details intact, and accordingly, PA signals can be calculated for photoacoustic tomography (PAT) imaging. Furthermore, the IRB technique produces images with superior quality and outperforms other algorithms.

Deep learning approach to improve tangential resolution in photoacoustic tomography

In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. In particular, the tangential resolution improves with the decreasing aperture size. However, using a detector with a smaller aperture reduces the sensitivity of the transducer. Thus, large aperture size detectors are widely preferred in circular scan PAT imaging systems. Although several techniques have been proposed to improve the tangential resolution, they have inherent limitations such as high cost and the need for customized detectors. Herein, we propose a novel deep learning architecture to counter the spatially variant tangential resolution in circular scanning PAT imaging systems. We used a fully dense U-Net based convolutional neural network architecture along with 9 residual blocks to improve the tangential resolution of the PAT images. The network was trained on the simulated datasets and its performance was verified by experimental in vivo imaging. Results show that the proposed deep learning network improves the tangential resolution by eight folds, without compromising the structural similarity and quality of image.

Video-Rate Ring-Array Ultrasound and Photoacoustic Tomography

Ultrasonography and photoacoustic tomography provide complementary contrasts in preclinical studies, disease diagnoses, and imaging-guided interventional procedures. Here, we present a video-rate (20 Hz) dual-modality ultrasound and photoacoustic tomographic platform that has a high resolution, rich contrasts, deep penetration, and wide field of view. A three-quarter ring-array ultrasonic transducer is used for both ultrasound and photoacoustic imaging. Plane-wave transmission/receiving approach is used for ultrasound imaging, which improves the imaging speed by nearly two folds and reduces the RF data size compared with the sequential single-channel scanning approach. GPU-based image reconstruction is developed to advance computational speed. We demonstrate fast dual-modality imaging in phantom, mouse, and human finger joint experiments. The results show respiration motion, heart beating, and detailed features in the mouse internal organs. To our knowledge, this is the first report on fast plane-wave ultrasound imaging and single-shot photoacoustic computed tomography in a ring-array system.