One of the primary challenges in breast cancer diagnosis and treatment is intratumor heterogeneity (ITH), i.e., the coexistence of different genetically and epigenetically distinct malignant cells within the same tumor. Thus, the identification of ITH is critical for designing better treatments and hence to increase patient survival rates. Herein, we report a noninvasive hybrid imaging technology that integrates multitargeted and multiplexed patchy polymeric photoacoustic contrast agents (MTMPPPCAs) with single-impulse panoramic photoacoustic computed tomography (SIP-PACT). The target specificity ability of MTMPPPCAs to distinguish estrogen and progesterone receptor-positive breast tumors was demonstrated through both fluorescence and photoacoustic measurements and validated by tissue pathology analysis. This work provides the proof-of-concept of the MTMPPPCAs/SIP-PACT system to identify ITH in nonmetastatic tumors, with both high molecular specificity and real-time detection capability.
Abstract Metastasis causes as many as 90% of cancer-related deaths, especially for the deadliest skin cancer, melanoma. Since hematogenous dissemination of circulating tumor cells is the major route of metastasis, detection and destruction of circulating tumor cells are vital for impeding metastasis and improving patient prognosis. Exploiting the exquisite intrinsic optical absorption contrast of circulating melanoma cells, we developed dual-wavelength photoacoustic flow cytography coupled with a nanosecond-pulsed melanoma-specific laser therapy mechanism. We have successfully achieved in vivo label-free imaging of rare single circulating melanoma cells in both arteries and veins of mice. Further, the photoacoustic signal from a circulating melanoma cell immediately hardware-triggers a lethal pinpoint laser irradiation to kill it on the spot in a thermally confined manner without causing collateral damage. A pseudo-therapy study including both in vivo and in vitro experiments demonstrated the performance and the potential clinical value of our method, which can facilitate early treatment of metastasis by clearing circulating tumor cells from vasculature.
Accurate estimation of the initial pressure distribution in photoacoustic computed tomography (PACT) requires some knowledge of the sound speed distribution. However, the sound speed distribution is typically unknown. Further, the initial pressure and sound speed distributions cannot both, in general, be stably recovered from PACT measurements alone. In this work, a joint reconstruction method for the initial pressure distribution and a low-dimensional parameterized model of the sound speed distribution is proposed. By employing a priori information about the structure of the sound speed distribution, both the initial pressure and sound speed can be accurately recovered. The joint reconstruction problem is solved by use of a proximal optimization method that allows constraints and non-smooth regularization functions for initial pressure distribution. The gradients of the cost function with respect to the initial pressure and sound speed distributions are calculated by use of an adjoint state method that has the same per-iteration computational cost as calculating the gradient with respect to the initial pressure distribution alone. This approach is quantitatively evaluated through 2D computer-simulation studies for a small animal imaging model. The impact of the choice of the parameterized sound speed model is investigated. Even when the assumed parameterized sound speed model is inconsistent with the true sound speed distribution, the estimated initial pressure distribution is more accurate than that obtained by assuming a constant sound speed. The utility of the proposed approach is also demonstrated through application to experimental in vivo measurements of a mouse.
A superresolution imaging approach that localizes very small targets, such as red blood cells or droplets of injected photoacoustic dye, has significantly improved spatial resolution in various biological and medical imaging modalities. However, this superior spatial resolution is achieved by sacrificing temporal resolution because many raw image frames, each containing the localization target, must be superimposed to form a sufficiently sampled high-density superresolution image. Here, we demonstrate a computational strategy based on deep neural networks (DNNs) to reconstruct high-density superresolution images from far fewer raw image frames. The localization strategy can be applied for both 3D label-free localization optical-resolution photoacoustic microscopy (OR-PAM) and 2D labeled localization photoacoustic computed tomography (PACT). For the former, the required number of raw volumetric frames is reduced from tens to fewer than ten. For the latter, the required number of raw 2D frames is reduced by 12 fold. Therefore, our proposed method has simultaneously improved temporal (via the DNN) and spatial (via the localization method) resolutions in both label-free microscopy and labeled tomography. Deep-learning powered localization PA imaging can potentially provide a practical tool in preclinical and clinical studies requiring fast temporal and fine spatial resolutions.
Full-ring ultrasonic transducer array is widely used in photoacoustic computed tomography (PACT) due to its high inplane resolution and full-view fidelity. Image in PACT is often contaminated by spatial aliasing, which has not been studied in detail for full-ring geometry. In this research, using spatiotemporal analysis, we clarified the sources of spatial aliasing. Based this clarification, we demonstrated that the combination of spatial interpolation and temporal filtering can effectively mitigate artifacts caused by aliasing in image reconstruction and spatial sampling. We validated our theory using numerical simulations and in vivo experiments.
Photoacoustic tomography (PAT) that integrates the molecular contrast of optical imaging with the high spatial resolution of ultrasound imaging in deep tissue has widespread applications in basic biological science, preclinical research and clinical trials. Recently, tremendous progress has been made in PAT regarding technical innovations, preclinical applications, and clinical translations. Here, we selectively review the recent progresses and advances in PAT, including the development of advanced PAT systems for small-animal and human imaging, newly engineered optical probes for molecular imaging, broad-spectrum PAT for label-free imaging of biological tissues, high-throughput snapshot photoacoustic topography, and integration of machine learning for image reconstruction and processing. We envision that PAT will have further technical developments and more impactful applications in biomedicine.
The Journal of Biomedical Optics (JBO) is a Gold Open Access journal that publishes peer-reviewed papers on the use of novel optical systems and techniques for improved health care and biomedical research.
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