Fractional Regularization to Improve Photoacoustic Tomographic Image Reconstruction

The sound of blood: photoacoustic imaging in blood analysis

Blood analysis is a ubiquitous and critical aspect of modern medicine. Analyzing blood samples requires invasive techniques, various testing systems, and samples are limited to relatively small volumes. Photoacoustic imaging (PAI) is a novel imaging modality that utilizes non-ionizing energy that shows promise as an alternative to current methods. This paper seeks to review current applications of PAI in blood analysis for clinical use. Furthermore, we discuss obstacles to implementation and future directions to overcome these challenges. Firstly, we discuss three applications to cellular analysis of blood: sickle cell, bacteria, and circulating tumor cell detection. We then discuss applications to the analysis of blood plasma, including glucose detection and anticoagulation quantification. As such, we hope this article will serve as inspiration for PAI's potential application in blood analysis and prompt further studies to ultimately implement PAI into clinical practice.

Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach

Photoacoustic imaging involves reconstructing an estimation of the absorbed energy density distribution from measured ultrasound data. The reconstruction task based on incomplete and noisy experimental data is usually an ill-posed problem that requires regularization to obtain meaningful solutions. The purpose of the work is to propose an elastic network (EN) model to improve the quality of reconstructed photoacoustic images. To evaluate the performance of the proposed method, a series of numerical simulations and tissue-mimicking phantom experiments are performed. The experiment results indicate that, compared with the L ₁-norm and L ₂-normbased regularization methods with different numerical phantoms, Gaussian noise of 10-50 dB, and different regularization parameters, the EN method with α = 0.5 has better image quality, calculation speed, and antinoise ability.

Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging

SIGNIFICANCE

Quantitative optoacoustic imaging (QOAI) continues to be a challenge due to the influence of nonlinear optical fluence distribution, which distorts the optoacoustic image representation. Nonlinear optical fluence correction in OA imaging is highly ill-posed, leading to the inaccurate recovery of optical absorption maps. This work aims to recover the optical absorption maps using deep learning (DL) approach by correcting for the fluence effect.

AIM

Different DL models were compared and investigated to enable optical absorption coefficient recovery at a particular wavelength in a nonhomogeneous foreground and background medium.

APPROACH

Data-driven models were trained with two-dimensional (2D) Blood vessel and three-dimensional (3D) numerical breast phantom with highly heterogeneous/realistic structures to correct for the nonlinear optical fluence distribution. The trained DL models such as U-Net, Fully Dense (FD) U-Net, Y-Net, FD Y-Net, Deep residual U-Net (Deep ResU-Net), and generative adversarial network (GAN) were tested to evaluate the performance of optical absorption coefficient recovery (or fluence compensation) with in-silico and in-vivo datasets.

RESULTS

The results indicated that FD U-Net-based deconvolution improves by about 10% over reconstructed optoacoustic images in terms of peak-signal-to-noise ratio. Further, it was observed that DL models can indeed highlight deep-seated structures with higher contrast due to fluence compensation. Importantly, the DL models were found to be about 17 times faster than solving diffusion equation for fluence correction.

CONCLUSIONS

The DL methods were able to compensate for nonlinear optical fluence distribution more effectively and improve the optoacoustic image quality.

Model resolution matrix based deconvolution improves over non-quadratic penalization in frequency-domain photoacoustic tomography

Frequency domain photoacoustic tomography is becoming more attractive due to low-cost and compact light-sources being used; however, frequency-domain implementation suffers from lower signal to noise compared to time-domain implementation. In this work, we have developed a non-quadratic based penalization framework for frequency-domain photoacoustic imaging, and further proposed a two-step model-resolution matrix based deconvolution approach to improve the reconstruction image quality. The model-resolution matrix was developed in the context of different penalty functions like l₂-norm, l₁-norm, Cauchy, and Geman-McClure. These model-resolution matrices were then used to perform the deconvolution operation using split augmented Lagrangian shrinkage thresholding algorithm in both full-view and limited-view configurations. The results indicated that the two-step approach outperformed the different penalty function (prior constraint) based reconstruction, with an improvement of about 20% in terms of peak signal to noise ratio and 30% in terms of structural similarity index measure. The improved image quality provided using these algorithms will have a direct impact on realizing practical frequency-domain implementation in both limited-view and full-view configurations.

MRI Information-Based Correction and Restoration of Photoacoustic Tomography

As an emerging molecular imaging modality, Photoacoustic Tomography (PAT) is capable of mapping tissue physiological metabolism and exogenous contrast agent information with high specificity. Due to its ultrasonic detection mechanism, the precise localization of targeted lesions has long been a challenge for PAT imaging. The poor soft-tissue contrast of the PAT image makes this process difficult and inaccurate. To meet this challenge, in this study, we first make use of the rich and clear structural information brought about by another advanced imaging modality, Magnetic Resonance Imaging (MRI), to assist organ segmentation and correct for the light fluence attenuation of PAT. We demonstrate improved feature visibility and enhanced localization of endogenous and exogenous agents in the fluence corrected PAT images. Compared with PAT-based methods, the contrast-to-noise ratio (CNR) of our MRI-assisted method increases by 29.1% in live animal experiments. Furthermore, we show that the co-registered MRI image can also be incorporated into PAT image restoration, and achieves improved anatomical landscape and soft-tissue contrast (CNR increased by 25.36%) while preserving similar spatial resolution. This PAT-MRI combination provides excellent structural, functional and molecular images of the subject, and may enable more comprehensive analysis of various preclinical research applications.

Acoustic Dual-Function Communication and Echo-Location in Inaudible Band

Acoustic communications are experiencing renewed interest as alternative solutions to traditional RF communications, not only in RF-denied environments (such as underwater) but also in areas where the electromagnetic (EM) spectrum is heavily shared among several wireless systems. By introducing additional dedicated channels, independent from the EM ones, acoustic systems can be used to ensure the continuity of some critical services such as communication, localization, detection, and sensing. In this paper, we design and implement a novel acoustic system that uses only low-cost off-the-shelf hardware and the transmission of a single, suitably designed signal in the inaudible band (18–22 kHz) to perform integrated sensing (ranging) and communication. The experimental testbed consists of a common home speaker transmitting acoustic signals to a smartphone, which receives them through the integrated microphone, and of an additional receiver exploiting the same signals to estimate distance information from a physical obstacle in the environment. The performance of the proposed dual-function system in terms of noise, data rate, and accuracy in distance estimation is experimentally evaluated in a real operational environment.

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.

An Excitation-Reception Collinear Probe for Ultrasonic, Photoacoustic, and Thermoacoustic Tri-Modal Volumetric Imaging

Imaging systems that integrate multiple modalities can reveal complementary anatomic and functional information as they exploit different contrast mechanisms, which have shown great application potential and advantages in preclinical studies. A portable and easy-to-use imaging probe will be more conducive to transfer to clinical practice. Here, we present a tri-modal ultrasonic (US), photoacoustic (PA), and thermoacoustic (TA) imaging system with an excitation-reception collinear probe. The acoustic field, light field, and electric field of the probe were designed to be coaxial, realizing homogeneous illumination and high-sensitivity detection at the same detection position. US images can provide detailed information about structures, PA images can delineate the morphology of blood vessels in tissues, and TA images can reveal dielectric properties of the tissues. Moreover, phantoms and in vivo human finger experiments were performed by the tri-modal imaging system to demonstrate its performance. The results show that the tri-modal imaging system with the proposed probe has the ability to detect small breast tumors with a radius of only 2.5 mm and visualize the anatomical structure of the finger in three dimensions. Our work confirms that the tri-modal imaging system equipped with a collinear probe can be applied to a variety of different scenarios, which lays a solid foundation for the application of the tri-modality system in clinical trials.

Binary photoacoustic tomography for improved vasculature imaging

SIGNIFICANCE

The proposed binary tomography approach was able to recover the vasculature structures accurately, which could potentially enable the utilization of binary tomography algorithm in scenarios such as therapy monitoring and hemorrhage detection in different organs.

AIM

Photoacoustic tomography (PAT) involves reconstruction of vascular networks having direct implications in cancer research, cardiovascular studies, and neuroimaging. Various methods have been proposed for recovering vascular networks in photoacoustic imaging; however, most methods are two-step (image reconstruction and image segmentation) in nature. We propose a binary PAT approach wherein direct reconstruction of vascular network from the acquired photoacoustic sinogram data is plausible.

APPROACH

Binary tomography approach relies on solving a dual-optimization problem to reconstruct images with every pixel resulting in a binary outcome (i.e., either background or the absorber). Further, the binary tomography approach was compared against backprojection, Tikhonov regularization, and sparse recovery-based schemes.

RESULTS

Numerical simulations, physical phantom experiment, and in-vivo rat brain vasculature data were used to compare the performance of different algorithms. The results indicate that the binary tomography approach improved the vasculature recovery by 10% using in-silico data with respect to the Dice similarity coefficient against the other reconstruction methods.

CONCLUSION

The proposed algorithm demonstrates superior vasculature recovery with limited data both visually and based on quantitative image metrics.

Non-local means improves total-variation constrained photoacoustic image reconstruction

Photoacoustic/Optoacoustic tomography aims to reconstruct maps of the initial pressure rise induced by the absorption of light pulses in tissue. This reconstruction is an ill-conditioned and under-determined problem, when the data acquisition protocol involves limited detection positions. The aim of the work is to develop an inversion method which integrates denoising procedure within the iterative model-based reconstruction to improve quantitative performance of optoacoustic imaging. Among the model-based schemes, total-variation (TV) constrained reconstruction scheme is a popular approach. In this work, a two-step approach was proposed for improving the TV constrained optoacoustic inversion by adding a non-local means based filtering step within each TV iteration. Compared to TV-based reconstruction, inclusion of this non-local means step resulted in signal-to-noise ratio improvement of 2.5 dB in the reconstructed optoacoustic images.

Another decade of photoacoustic imaging

Photoacoustic imaging - a hybrid biomedical imaging modality finding its way to clinical practices. Although the photoacoustic phenomenon was known more than a century back, only in the last two decades it has been widely researched and used for biomedical imaging applications. In this review we focus on the development and progress of the technology in the last decade (2010-2020). From becoming more and more user friendly, cheaper in cost, portable in size, photoacoustic imaging promises a wide range of applications, if translated to clinic. The growth of photoacoustic community is steady, and with several new directions researchers are exploring, it is inevitable that photoacoustic imaging will one day establish itself as a regular imaging system in the clinical practices.

Image reconstruction based on compressed sensing for sparse-data endoscopic photoacoustic tomography

Endoscopic photoacoustic tomography (EPAT) is an interventional application of photoacoustic tomography (PAT) to visualize anatomical features and functional components of biological cavity structures such as nasal cavity, digestive tract or coronary arterial vessels. One of the main challenges in clinical applicability of EPAT is the incomplete acoustic measurements due to the limited detectors or the limited-view acoustic detection enclosed in the cavity. In this case, conventional image reconstruction methodologies suffer from significantly degraded image quality. This work introduces a compressed-sensing (CS)-based method to reconstruct a high-quality image that represents the initial pressure distribution on a luminal cross-section from incomplete discrete acoustic measurements. The method constructs and trains a complete dictionary for the sparse representation of the photoacoustically-induced acoustic measurements. The sparse representation of the complete acoustic signals is then optimally obtained based on the sparse measurements and a sensing matrix. The complete acoustic signals are recovered from the sparse representation by inverse sparse transformation. The image of the initial pressure distribution is finally reconstructed from the recovered complete signals by using the time reversal (TR) algorithm. It was shown with numerical experiments that high-quality images with reduced under-sampling artifacts can be reconstructed from sparse measurements. The comparison results suggest that the proposed method outperforms the standard TR reconstruction by 40% in terms of the structural similarity of the reconstructed images.