Maximum Entropy Based Non-Negative Optoacoustic Tomographic Image Reconstruction

Dual photoacoustic/ultrasound technologies for preclinical research: current status and future trends

Photoacoustic (PA) imaging, by integrating optical and ultrasound (US) modalities, combines high spatial resolution with deep tissue penetration, making it a transformative tool in biomedical research. This review presents a comprehensive analysis of the current status of dual PA/US imaging technologies, emphasising their applications in preclinical research. It details advancements in light excitation strategies, including tomographic and microscopic modalities, innovations in pulsed laser and alternative light sources, and US instrumentation. The review further explores preclinical methodologies, encompassing dedicated instrumentation, signal processing, and data analysis techniques essential for PA/US systems. Key applications discussed include the visualisation of blood vessels, micro-circulation, and tissue perfusion; diagnosis and monitoring of inflammation; evaluation of infections, atherosclerosis, burn injuries, healing, and scar formation; assessment of liver and renal diseases; monitoring of epilepsy and neurodegenerative conditions; studies on brain disorders and preeclampsia; cell therapy monitoring; and tumour detection, staging, and recurrence monitoring. Challenges related to imaging depth, resolution, cost, and the translation of contrast agents to clinical practice are analysed, alongside advancements in high-speed acquisition, artificial intelligence-driven reconstruction, and innovative light-delivery methods. While clinical translation remains complex, this review underscores the crucial role of preclinical studies in unravelling fundamental biomedical questions and assessing novel imaging strategies. Ultimately, this review delves into the future trends of dual PA/US imaging, highlighting its potential to bridge preclinical discoveries with clinical applications and drive advances in diagnostics, therapeutic monitoring, and personalised medicine.

Model-based quantitative photoacoustic tomography with directional total variation

In photoacoustic tomography (PAT), acoustic inversion aims to recover the spatial distribution of light energy deposition within the imaging object from the signals captured by detectors. To achieve quantitative imaging, optical inversion is further employed to derive absorption coefficient (AC) images. However, limitations such as restricted detection angles and inherent noise lead to substantial artifacts and degradation in the quality of PAT images, consequently affecting the accuracy of optical inversion results. In this study, we propose a directional total variation constrained optical inversion model to reconstruct the AC image. By incorporating anatomy prior information into the optical inversion process, our method can effectively suppress artifacts in AC images while maintaining structural integrity. Simulation, phantom, and in vivo experimental results demonstrate that our method significantly improves the reconstructed AC image quality. Our method provides a reliable foundation for achieving high-quality quantitative PAT imaging.

Photoacoustic maximum amplitude projection microscopy by ultra-low data sampling

Photoacoustic microscopy (PAM) has attracted increasing research interest in the biomedical field due to its unique merit of combining light and sound. In general, the bandwidth of a photoacoustic signal reaches up to tens or even hundreds of MHz, which requires a high-performance acquisition card to meet the high requirement of precision of sampling and control. For most depth-insensitive scenes, it is complex and costly to capture the photoacoustic maximum amplitude projection (MAP) images. Herein, we propose a simple and low-cost MAP-PAM system based on a custom-made peak holding circuit to obtain the extremum values by Hz data sampling. The dynamic range of the input signal is 0.01-2.5 V, and the -6-dB bandwidth of the input signal can be up to 45 MHz. Through in vitro and in vivo experiments, we have verified that the system has the same imaging ability as conventional PAM. Owing to its compact size and ultra-low price (approximately $18), it provides a new performance paradigm for PAM and opens up a new way for an optimal photoacoustic sensing and imaging device.

On the Image Reconstruction of Capacitively Coupled Electrical Resistance Tomography (CCERT) with Entropy Priors

Regularization with priors is an effective approach to solve the ill-posed inverse problem of electrical tomography. Entropy priors have been proven to be promising in radiation tomography but have received less attention in the literature of electrical tomography. This work aims to investigate the image reconstruction of capacitively coupled electrical resistance tomography (CCERT) with entropy priors. Four types of entropy priors are introduced, including the image entropy, the projection entropy, the image-projection joint entropy, and the cross-entropy between the measurement projection and the forward projection. Correspondingly, objective functions with the four entropy priors are developed, where the first three are implemented under the maximum entropy strategy and the last one is implemented under the minimum cross-entropy strategy. Linear back-projection is adopted to obtain the initial image. The steepest descent method is utilized to optimize the objective function and obtain the final image. Experimental results show that the four entropy priors are effective in regularization of the ill-posed inverse problem of CCERT to obtain a reasonable solution. Compared with the initial image obtained by linear back projection, all the four entropy priors make sense in improving the image quality. Results also indicate that cross-entropy has the best performance among the four entropy priors in the image reconstruction of CCERT.

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.

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.

Medical image registration utilizing tissue P systems

The tissue P system (TPS) possesses intrinsic attributes of parallel execution in comprehensive data and instruction space, which provides fast convergence during the transition from local to global optima. Method- In this study, we have proposed and built a TPSysIR framework using the TPS for image registration that optimizes upon the mutual information (MI) similarity metric to find a global solution. Result- The model was tested on single- and multimodal brain MRI scans and other prominent optimization-based image registration techniques. Conclusion- Results show that, among all methods, TPSysIR provides better MI values with minimum deviation in a range of experiment setups conducted iteratively.

Weighted model-based optoacoustic reconstruction for partial-view geometries

Acoustic heterogeneities in biological samples are known to cause artifacts in tomographic optoacoustic (photoacoustic) image reconstruction. A statistical weighted model-based reconstruction approach was previously introduced to mitigate such artifacts. However, this approach does not reliably provide high-quality reconstructions for partial-view imaging systems, which are common in preclinical and clinical optoacoustics. In this article, the capability of the weighted model-based algorithm is extended to generate optoacoustic reconstructions with less distortions for partial-view geometry data. This is achieved by manipulating the weighting scheme based on the detector geometry. Using partial-view optoacoustic tomography data from a tissue-mimicking phantom containing a strong acoustic reflector, tumors grafted onto mice, and a mouse brain with intact skull, the proposed partial-view-corrected weighted model-based algorithm is shown to mitigate reflection artifacts in reconstructed images without distorting structures or boundaries, compared with both conventional model-based and the weighted model-based algorithms. It is also demonstrated that the partial-view-corrected weighted model-based algorithm has the additional advantage of suppressing streaking artifacts due to the partial-view geometry itself in the presence of a very strong optoacoustic chromophore. Due to its enhanced performance, the partial-view-corrected weighted model-based algorithm may prove useful for improving the quality of partial-view multispectral optoacoustic tomography, leading to enhanced visualization of functional parameters such as tissue oxygenation.

Superpixel spectral unmixing framework for the volumetric assessment of tissue chromophores: A photoacoustic data-driven approach

The assessment of tissue chromophores at a volumetric scale is vital for an improved diagnosis and treatment of a large number of diseases. Spectral photoacoustic imaging (sPAI) co-registered with high-resolution ultrasound (US) is an innovative technology that has a great potential for clinical translation as it can assess the volumetric distribution of the tissue components. Conventionally, to detect and separate the chromophores from sPAI, an input of the expected tissue absorption spectra is required. However, in pathological conditions, the prediction of the absorption spectra is difficult as it can change with respect to the physiological state. Besides, this conventional approach can also be hampered due to spectral coloring, which is a prominent distortion effect that induces spectral changes at depth. Here, we are proposing a novel data-driven framework that can overcome all these limitations and provide an improved assessment of the tissue chromophores. We have developed a superpixel spectral unmixing (SPAX) approach that can detect the most and less prominent absorber spectra and their volumetric distribution without any user interactions. Within the SPAX framework, we have also implemented an advanced spectral coloring compensation approach by utilizing US image segmentation and Monte Carlo simulations, based on a predefined library of optical properties. The framework has been tested on tissue-mimicking phantoms and also on healthy animals. The obtained results show enhanced specificity and sensitivity for the detection of tissue chromophores. To our knowledge, this is a unique framework that accounts for the spectral coloring and provides automated detection of tissue spectral signatures at a volumetric scale, which can open many possibilities for translational research.

Spectral crosstalk in photoacoustic computed tomography

Multispectral photoacoustic (PA) imaging faces two major challenges: the spectral coloring effect, which has been studied extensively as an optical inversion problem, and the spectral crosstalk, which is basically a result of non-ideal acoustic inversion. So far, there is no systematic work to analyze the spectral crosstalk because acoustic inversion and spectroscopic measurement are always treated as decoupled. In this work, we theorize and demonstrate through a series of simulations and experiments how imperfect acoustic inversion induces inaccurate PA spectrum measurement. We provide detailed analysis to elucidate how different factors, including limited bandwidth, limited view, light attenuation, out-of-plane signal, and image reconstruction schemes, conspire to render the measured PA spectrum inaccurate. We found that the model-based reconstruction outperforms universal back-projection in suppressing the spectral crosstalk in some cases.

Reporter gene-based optoacoustic imaging of E. coli targeted colon cancer in vivo

Bacteria-mediated cancer-targeted therapy is a novel experimental strategy for the treatment of cancers. Bacteria can be engineered to overcome a major challenge of existing therapeutics by differentiating between malignant and healthy tissue. A prerequisite for further development and study of engineered bacteria is a suitable imaging concept which allows bacterial visualization in tissue and monitoring bacterial targeting and proliferation. Optoacoustics (OA) is an evolving technology allowing whole-tumor imaging and thereby direct observation of bacterial colonization in tumor regions. However, bacterial detection using OA is currently hampered by the lack of endogenous contrast or suitable transgene fluorescent labels. Here, we demonstrate improved visualization of cancer-targeting bacteria using OA imaging and E. coli engineered to express tyrosinase, which uses L-tyrosine as the substrate to produce the strong optoacoustic probe melanin in the tumor microenvironment. Tumors of animals injected with tyrosinase-expressing E. coli showed strong melanin signals, allowing to resolve bacterial growth in the tumor over time using multispectral OA tomography (MSOT). MSOT imaging of melanin accumulation in tumors was confirmed by melanin and E. coli staining. Our results demonstrate that using tyrosinase-expressing E. coli enables non-invasive, longitudinal monitoring of bacterial targeting and proliferation in cancer using MSOT.

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.

Sound Out the Deep Colors: Photoacoustic Molecular Imaging at New Depths

Photoacoustic tomography (PAT) has become increasingly popular for molecular imaging due to its unique optical absorption contrast, high spatial resolution, deep imaging depth, and high imaging speed. Yet, the strong optical attenuation of biological tissues has traditionally prevented PAT from penetrating more than a few centimeters and limited its application for studying deeply seated targets. A variety of PAT technologies have been developed to extend the imaging depth, including employing deep-penetrating microwaves and X-ray photons as excitation sources, delivering the light to the inside of the organ, reshaping the light wavefront to better focus into scattering medium, as well as improving the sensitivity of ultrasonic transducers. At the same time, novel optical fluence mapping algorithms and image reconstruction methods have been developed to improve the quantitative accuracy of PAT, which is crucial to recover weak molecular signals at larger depths. The development of highly-absorbing near-infrared PA molecular probes has also flourished to provide high sensitivity and specificity in studying cellular processes. This review aims to introduce the recent developments in deep PA molecular imaging, including novel imaging systems, image processing methods and molecular probes, as well as their representative biomedical applications. Existing challenges and future directions are also discussed.

Brilliant cresyl blue enhanced optoacoustic imaging enables non-destructive imaging of mammalian ovarian follicles for artificial reproduction

In the field of reproductive biology, there is a strong need for a suitable tool capable of non-destructive evaluation of oocyte viability and function. We studied the application of brilliant cresyl blue (BCB) as an intra-vital exogenous contrast agent using multispectral optoacoustic tomography (MSOT) for visualization of porcine ovarian follicles. The technique provided excellent molecular sensitivity, enabling the selection of competent oocytes without disrupting the follicles. We further conducted in vitro embryo culture, molecular analysis (real-time and reverse transcriptase polymerase chain reaction) and DNA fragmentation analysis to comprehensively establish the safety of BCB-enhanced MSOT imaging in monitoring oocyte viability. Overall, the experimental results suggest that the method offers a significant advance in the use of contrast agents and molecular imaging for reproductive studies. Our technique improves the accurate prediction of ovarian reserve significantly and, once standardized for in vivo imaging, could provide an effective tool for clinical infertility management.

A Synthetic Total Impulse Response Characterization Method for Correction of Hand-Held Optoacoustic Images

The impulse response of optoacoustic (photoacoustic) tomographic imaging system depends on several system components, the characteristics of which can influence the quality of reconstructed images. The effect of these system components on reconstruction quality have not been considered in detail so far. Here we combine sparse measurements of the total impulse response (TIR) with a geometric acoustic model to obtain a full characterization of the TIR of a handheld optoacoustic tomography system with concave limited-view acquisition geometry. We then use this synthetic TIR to reconstruct data from phantoms and healthy human volunteers, demonstrating improvements in image resolution and fidelity. The higher accuracy of optoacoustic tomographic reconstruction with TIR correction further improves the diagnostic capability of handheld optoacoustic tomographic systems.