We propose an improved version of a synthetic aperture focusing technique (SAFT) based on a delay-multiply-and-sum algorithm for acoustic-resolution photoacoustic microscopy (AR-PAM). In this method, the photoacoustic (PA) signals from multiple scan-lines are combinatorially coupled, multiplied, and then summed. This process can be considered a correlation operation of the PA signals in each scan-line, so the spatial coherent information between the PA signals can be efficiently extracted. By applying this method in conventional AR-PAM, lateral resolution and signal-to-noise ratio in out-of-focus regions are much improved compared with those estimated from the previously developed SAFT, respectively, thereby achieving the extension of the imaging focal region. Our phantom and in vivo imaging experiments prove the validity of our proposed method.

Exploring the relationship between neural activity and cerebral hemoglobin-oxygenation responses in freely behaving mice can advance our understanding of neurovascular coupling. Head-mounted microscopes enable neuroimaging in freely behaving mice; however, investigating neurovascular dynamics remains challenging because of a lack of hemodynamic information, especially blood oxygenation, or insufficient resolution. Here, we report a head-mounted microscope for neurovascular imaging that enables the simultaneous recording of neuronal burst firing and multiparametric hemodynamics such as vascular oxygen saturation at high spatiotemporal resolution. The 1.7-gram lightweight microscope integrates confocal fluorescence and photoacoustic microscopy, allowing recordings at 0.78 hertz with 1.5-micrometer lateral resolution across a field of view of 400 micrometers by 400 micrometers. We identified cell type–specific neurovascular responses to hypoxic challenges, observed active regulation of arterioles during sensory stimuli, and detected abnormal oxygen depletion and vasodilation preceding burst neuronal discharges in epileptic disorders. This technique provides valuable insights into neurovascular coupling and holds potential for studying the pathology of neurological brain diseases.

Simultaneously monitoring cortex-wide microvascular dynamics, blood oxygenation metabolism, and neuronal activities with high spatiotemporal resolution is essential for understanding neurovascular coupling (NVC). However, it remains a challenge for existing neuroimaging tools. Here, we report a linear transducer array–based hybrid microscope (LiTA-HM) that integrates photoacoustic microscopy and confocal fluorescence microscopy to achieve simultaneous neurovascular imaging with a field of view (FOV) of 6 millimeters by 5 millimeters, a spatial resolution of ~6 micrometers, and a temporal resolution of 0.8 seconds. We design an array of eight miniature transducers to enlarge FOV and optimize a polygon mirror–based ultrafast scanner to achieve real-time imaging. Proof-of-concept imaging experiments were conducted on awake mice. Different couplings between thousands of neuron somas and vessel branches down to capillaries were observed in hypoxia and anesthesia experiments. Spatiotemporal correlations and functional connectivity in NVC were uncovered for spreading depolarization waves occurred in a drug-induced epilepsy experiment. We expect that LiTA-HM will enable broad applications of neuroimaging.

Photoacoustic computed tomography (PACT) has emerged as a unique and promising technology for multiscale biomedical imaging. To fully realize its potential for various preclinical and clinical applications, development of systems with high imaging speed, reasonable cost, and manageable data flow are needed. Sparse-sampling PACT with advanced reconstruction algorithms, such as compressed-sensing reconstruction, has shown potential as a solution to this challenge. However, most such algorithms require iterative reconstruction and thus intense computation, which may lead to excessively long image reconstruction times. Here, we developed a principal component analysis (PCA)-based PACT (PCA-PACT) that can rapidly reconstruct high-quality, three-dimensional (3-D) PACT images with sparsely sampled data without requiring an iterative process. In vivo images of the vasculature of a human hand were obtained, thus validating the PCA-PACT method. The results showed that, compared with the back-projection (BP) method, PCA-PACT required $∼50%$ fewer measurements and $∼40%$ less time for image reconstruction, and the imaging quality was almost the same as that for BP with full sampling. In addition, compared with compressed sensing-based PACT, PCA-PACT had approximately sevenfold faster imaging speed with higher imaging accuracy. This work suggests a promising approach for low-cost, 3-D, rapid PACT for various biomedical applications.

The sparse transforms currently used in the model-based reconstruction method for photoacoustic computed tomography (PACT) are predefined and they typically cannot capture the underlying features of the specific data sets adequately, thus limiting the high-quality recovery of photoacoustic images. In this work, we present an advanced reconstruction model using the K-VSD dictionary learning technique and present the in vivo results after adapting the model into the 3D PACT system. The in vivo experiments were performed on an IRB approved human hand and two rats. When compared to the traditional sparse transform, experimental results using our proposed method improved accuracy and contrast to noise ration of the reconstructed photoacoustic images, on average, by 3.7 and 1.8 times in the case of 50% sparse-sampling rate, respectively. We also compared the performance of our algorithm against other techniques, and imaging speed was 60% faster than other approaches. Our system would require sparse-transducer array and lower number of data acquisition hardware (DAQs) potentially reducing the cost of the system. Thus, our work provides a new way for reconstructing photoacoustic images, and it would enable the development of new high-speed low-cost 3D PACT for various biomedical applications.