A photoacoustic imaging tool developed by Duke University researchers accommodates the need for both speed and comprehensive detail in neurovascular imaging. The imaging modality enables an approach to visualize whole-brain hemodynamics and oxygenation in real time. Additionally, it tracks fast pathophysiological activities at the micro-vessel level. According to the researchers, the approach breaks speed and resolution barriers in brain imaging technologies, and could lead to insights into stroke, dementia, and acute brain injury. Ultrafast Functional Photoacoustic Microscopy (UFF-PAM) uses advanced hardware, stimulated Raman scattering-based dual-wavelength laser excitation, a high-sensitivity ultrasound transducer, and deep-learning-based techniques to surpass the capabilities of standard photoacoustic microscopy (PAM) systems. The hardware for UFF-PAM includes a water-immersible, 12-facet-polygon scanning system that can send more laser bursts to a larger area than standard photoacoustic imaging devices. To enable functional brain imaging at high speed, UFF-PAM features dual-wavelength excitation at 532 and 558 nm. The scanning mechanism allows the confocal beam of laser excitation and ultrasound detection to be steered simultaneously. According to professor Junjie Yao, the use of cutting-edge hardware doubled the speed of the device, making UFF-PAM the fastest photoacoustic imaging technology known to the team. The maximum line scanning rate for UFF-PAM is more than 2 kHz over an 11-mm scanning range. The vasculature of the brain, with the colors illuminating how capillaries experience varying levels of oxygenation as the brain undergoes hypoxia. Courtesy of Junjie Yao, Duke University. To improve image quality, the researchers developed a machine learning algorithm and trained it to identify vasculature in the brain, using over 400 images of mouse brains collected in previous experiments. Although each brain was different, the algorithm learned to identify common structures and used this knowledge to fill in missing information about an image. An automatic image registration method is used to overcome any misalignment of the polygon facets. “The resulting images looked as detailed as the high-resolution images we would usually get if we went at a much slower speed, and we didn’t need to sacrifice a full field of view,” Yao said. UFF-PAM has a volumetric imaging rate of 2 Hz over a field of view of 11 × 7.5 × 1.5 mm3, with a high spatial resolution of about 10 μm. The team used UFF-PAM to visualize how blood vessels in a mouse brain responded to hypoxia, drug-induced hypotension, and ischemic stroke. In response to hypoxia, UFF-PAM tracked how oxygen moved through the mouse brain and showed that low oxygen levels caused blood vessels to dilate. “Because we quickly got a high-resolution view of the smaller vessels, we saw that dilation is not actually the universal response to the drug,” Yao said. “We saw that these small vessels couldn’t provide enough oxygen and nutrients to the tissue, which caused damage.” The researchers also used UFF-PAM to observe how the brain responds to and begins to recover from stroke. Immediately after a stroke, the blood vessels in the affected area of the brain constrict. Neighboring vessels also constrict, due to an effect called a spreading depolarization wave. Because of UFF-PAM’s large field of view and rapid imaging speed, the team precisely pinpointed the wave’s starting position and tracked its movement as it propagated throughout the brain. The team aims to use UFF-PAM to explore brain disease models such as dementia, Alzheimer’s disease, and long COVID-19. It also plans to expand the tool’s use to image organs like the heart, liver, and placenta — organs that have traditionally been challenging to image because they are always in motion, which necessitates that tools be operated at a rapid speed. The research was published in Light: Science & Applications (www.doi.org/10.1038/s41377-022-00836-2).