Agent-free localization photoacoustic microscopy with a galvanometer scanner (L-PAM-GS) and performance benchmark

Figure 1 and Supplementary Fig. S1 show a schematic of the L-PAM-GS system. A nanosecond pulsed laser with a PRR of 800 kHz and an optical wavelength of 532 nm was used to enable ultra-fast and high contrast vascular imaging capabilities. Uniquely, we installed the galvanometer scanner vertically, submerging only the mirror part of the scanner in the water, preventing the scanner from breaking. In addition, we reshaped the mirror part from a flat structure to a half-cylindrical shaft with an aluminum-coated silicon wafer to minimize drag (i.e., fluid resistance), which is proportional to the dynamic pressure projected on a plane perpendicular to the direction of motion in the water environment (Supplementary Fig. S2). Based on computational-fluid-dynamics (CFD) simulation results, the maximum pressure applied to the half-cylindrical mirror part is two times less than that applied to the flat one (Supplementary Fig. S3 and Supplementary Movie 1). The average pressure applied to the half-cylindrical mirror part is four times less because of the larger area of the half-cylindrical mirror part. Our new L-PAM-GS system can achieve a maximum B-scan rate of 650 Hz in water, but stability is guaranteed up to a B-scan rate of 500 Hz. A slow-motion video (Supplementary Movie 2) shows the underwater operation of the galvanometric mirror system at a frequency of 100 Hz. The maximum imaging range (x-axis) of the scanner at the 500 Hz B-scan rate was 2.4 mm, and the maximum mechanical scanning range (y-axis) was 26 mm. The opto-acoustic combiner was used to align the optical excitation beams and the emitted PA waves coaxially and confocally to increase the SNR. The SNR was calculated to be 35.6 dB in the PA image of a mouse ear in vivo, which is comparable to previously reported results4,18. We measured the spatial resolutions of our system by photoacoustically imaging a patterned microstructure and carbon fiber for lateral and axial resolutions, respectively (Supplementary Fig. S4 and Supplementary Text). The optical NA was 0.039, and the lateral resolution was measured to be 7.5 μm, which is very close to the theoretical lateral resolution limit of 7.0 μm2. The measured axial resolution was 33.0 μm. This axial resolution matched well with the previously reported resolution of 35.0 μm and the theoretical axial resolution limit of 32.2 μm for a transducer with a center frequency of 50 MHz and a −6 dB bandwidth of 82%2,39.

Fig. 1: Localization photoacoustic microscopy using a galvanometer scanner (L-PAM-GS). a Configuration of the L-PAM-GS system. b Close-up view of the scanning part outlined by the red dashed box in a. c Photograph of the scanning part. BS, beam splitter; OL, objective lens; PD, photodiode; GS, galvanometer scanner; UT, ultrasound transducer; MS, motor stage; OAC, optical-acoustic combiner; M, mirror; FWHM, full width at half maximum. Full size image

Microvascular imaging of small animals and humans in vivo

To show the utility of our new system in life science research, we successfully imaged microvasculatures in the ears, eyes, and brains of mice. (Fig. 2). First, we acquired PA MAP images of the mouse ear. The image size of the mouse ear using the L-PAM-GS system was 12.9 mm × 8 mm along the x- and y-axis, respectively (Fig. 2a). The step sizes were 4.8 and 10 μm along the x- and y-axis, respectively. Six segmented images, each with a size of 2.4 mm × 8 mm (x- and y-axis, respectively), were used to synthesize the wider-FOV image. After acquiring one segmented image (2.4 mm × 8 mm along the x- and y-axis, respectively), we moved the sample stage along the x-axis via the manual linear stage, which was attached under the motorized stage (Supplementary Fig. S1b), and then acquired another segmented image. During the image mosaic, 0.3 mm along the x-direction in the adjacent images was overlapped to align each segmented image. Theoretically, under an ideal condition, it takes approximately 9.6 s to obtain all six segmented images (12.9 mm × 8 mm along the x- and y-axis, respectively), because it takes 1.6 s for one segmented image (2.4 mm × 8 mm along the x- and y-axis, respectively). However, we manually moved the manual stage between each segmented image. Therefore, five manual movements are involved in this case. Therefore, the true total acquisition time for a wide-FOV image was approximately 15 s. The average laser pulse energy used was 200 nJ. The PA MAP image of the mouse ear shows both capillary beds and single capillaries. The capillaries at the edge of the ear generated stronger PA signals compared to the capillaries inside. One possible reason for this difference in the signal signature could be that the skin inside is thicker than that on the edge, which results in more optical scattering.

Fig. 2: Photoacoustic (PA) images of microvasculatures in small animals in vivo. a Wide-FOV PA MAP image of a mouse ear. The region including a capillary bed is outlined by the white dashed box. b Depth-encoded PA image of a mouse eye. Circulus arteriosus major (I), iris (II), circulus arteriosus minor (III) and choroid (IV) blood vessels are highlighted by the white arrows. c Wide FOV PA MAP image of a mouse brain with color-encoded depths and amplitudes. Superior sagittal sinus (V) and transverse sinus (VI) are highlighted by the white arrows. FOV, field of view; MAP, maximum amplitude projection. Full size image

Next, we imaged the mouse eyes in vivo (Fig. 2b). The FOV of the ocular image is 2.4 mm × 4 mm along the x- and y-axis, respectively, and the step sizes are 4.8 and 10 μm along the x- and y-axis, respectively. The acquisition time for one ocular image was 0.8 s. The average laser pulse energy used for ocular imaging was 152 nJ. A random sample consensus machine learning algorithm was applied to accurately segment the ocular structures11. The surface-based depth-encoded ocular image visualized the vascular anatomy of eye orbits. Circulus arteriosus major, iris, circulus arteriosus minor and choroid blood vessels were clearly distinguished (highlighted by arrows I, II, III, and IV in Fig. 2b, respectively).

Third, we imaged a wide region (10.1 mm × 6 mm along the x- and y-axis, respectively) of a mouse brain (Fig. 2c). Before imaging, the mouse’s scalp was removed, but the skull was left intact. Five segmented images with a size of 2.4 mm × 6 mm (x- and y-axis, respectively) were mosaicked. Similar to the wide FOV ear image of the mouse, each segmented image (acquisition time: 1.2 s) was overlapped by 0.5 mm along the x-direction. The true total acquisition time for a wide-FOV image was ~10 s. The x- and y-axis step sizes were 4.8 and 10 μm, respectively. The average laser pulse energy used for imaging was 200 nJ. The mouse brain image was expressed in a 2D color map showing both depth and amplitude information at once, revealing the detailed angiographic structures of the brain. The superior sagittal sinus and transverse sinus were differentiated from cortical arteries and veins (highlighted by arrows V and VI in Fig. 2c, respectively). In addition, we also quantified the maximum imaging depth in the PA MAP image along the y-axis, and the maximum imaging depth for the mouse brain in vivo was ~760 μm (Supplementary Fig. S5a).

Finally, we obtained a microvascular PA image of a human fingertip noninvasively (Fig. 3). The region outlined by the black dashed box in Fig. 3a was imaged with our system. Unlike the imaging of small animals, strong PA signals from the skin appeared in the PA MAP images because the skin absorbed much energy from the optical beams. The microvasculature was distinguished relatively weakly in the regions outlined by the white dashed circles in Fig. 3b. Additional post-processing software to remove the skin signals was applied to the 3D volume data before constructing the PA MAP images (Supplementary Fig. S6 and Supplementary Text)40. The skin-removed PA MAP image (Fig. 3c) clearly shows the microvessel structures. In the cross-sectional PA B-scan image of the plane marked by the white dashed lines a–a′ in Fig. 3b, the skin signals are clearly revealed (Fig. 3d, green dashed line). However, the skin signals are completely removed in the B-scan image (Fig. 3e) of the plane marked by the white dashed lines b–b′ in Fig. 3c. This difference is even more obvious in the 3D volume rendered movie (Supplementary Movie 3). The FOV of the human cuticle image was 2.4 mm × 4.4 mm, and the step sizes were 4.8 and 5 μm along the x- and y-axis, respectively. The acquisition time for the human cuticle image was 2 s. The average laser pulse energy used for imaging was 200 nJ. Our system could image the microvasculatures up to ~560 μm in the human cuticle (Supplementary Fig. S5b).

Fig. 3: Photoacoustic (PA) images of microvasculatures in humans in vivo. a Photograph of a little finger of a volunteer. b PA MAP image of the region outlined by the black dashed box in a. c Skin-removed PA MAP image of b. After application to the skin-remover processing, the blood vessels outlined by the white dashed circles in b, c become clearly visible (also see Supplementary Movie 3). d, e Cross-sectional PA B-mode images of the planes marked by the white dashed lines d a–a′ and e b–b′. The PA signal from the skin is marked by the green dashed line, and the PA signal from the blood vessels is outlined by the blue dashed circle. The PA signal from the skin was completely removed in the B-mode image of the plane marked by the line b–b′. MAP, maximum amplitude projection; BV, blood vessel. Full size image

High-speed PA imaging of hemodynamics in a mouse ear

To fully take advantage of the high-speed imaging capability, we monitored the hemodynamics in the microvessels of the mouse ear (Fig. 4 and Supplementary Movie 4). We captured the blood flow and estimated the mean flow rate in the capillaries. A region of 2.4 mm × 0.5 mm (x- and y-axis, respectively) was imaged repeatedly for 20 s with a volumetric imaging speed of 5 Hz. Both step sizes along the x- and y-axis were 5 μm. After each PA MAP image was cropped to 890 μm × 420 μm, the image registration process was applied to all PA images to minimize motion artifacts caused by the target movement and the vibration of the scanners (Supplementary Fig. S7). Figure 4a is a representative PA MAP image after the acquisition of a series of images over the acquisition duration. To display the blood flow in the PA MAP image, we traced the PA signals in a capillary in which the amplitude changed. Figure 4b shows a series of PA images in the region outlined by the blue box in Fig. 4a. We estimated the blood flow rate by calculating the change in flow distance of the marked red blood cells (RBCs) (i.e., white and green dotted circles in Fig. 4b) over the image acquisition time. The mean flow rates of the RBCs were estimated to be 125 and 137 μm/s (RBCs highlighted by white and green arrows, respectively). The measured flow rate was similar to the previously reported blood flow rate (e.g., ~100 μm/s) in the vein41. Blood flow is also visualized in Supplementary Movie 4.

Fig. 4: Photoacoustic (PA) monitoring of hemodynamics in a mouse ear. a Representative single-shot PA MAP image of the mouse ear. b Series of PA images over a time period showing the flow of RBCs (also see Supplementary Movie 4). Close-up views of the region are outlined by the blue dashed boxes in a. Flowing red blood cells are highlighted by the white and green dashed circles. The flow rates of the RBCs during the captured period are quantified as shown below. MAP, maximum amplitude projection. Full size image

Agent-free localization photoacoustic microscopy

To confirm the imaging capability of our L-PAM-GS system using the agent-free localization method, we imaged black polystyrene particles in vitro and a mouse ear in vivo (Supplementary Fig. S8 and Fig. 5). In the in vitro experiment (Supplementary Text), the standard deviation of the localized positions of a single particle, determining the enhanced spatial resolution, were 0.4, 0.7, and 2.5 μm (x, y, and z directions, respectively)33. We applied our localization method to the label-free PAM of small animals in vivo by localizing the PA signals induced by RBCs. L-PAM-GS is able to capture the RBCs instantaneously based on its fast temporal resolution and high SNR (Fig. 5a). In addition, the PA signals in each frame, generated from the same RBC, can be localized at different positions under the flow condition. The L-PAM-GS image is finally rendered by superimposing all points localized in each frame (Fig. 5b). In this experiment, a sequence of 60 images, each with a size of 1.5 mm × 1 mm (x- and y-axis, respectively), was obtained, with a volumetric imaging speed of 2.5 Hz (Fig. 5c). Our localization algorithm was applied to each volumetric frame to determine the local maximum points, and the maximum points were superimposed to render localization 2D PA MAP and 3D PA volume images (Fig. 5d, e). Supplementary Movies 5 and 6 illustrate the actual 2D and 3D formations of the improved L-PAM-GS image from the sequence of 60 frames. The rotation movie of the localization volume image also shows the enhanced microvascular structures (Supplementary Movie 7). Supplementary Figure S9 shows the conventional and localization PA MAP images of the mouse ear along the y-axis. The imaging depths of both approaches are at least 320 μm, but these are not the maximum imaging depth because the thickness of the mouse ear is too thin to show the maximum imaging depth. To emphasize the enhanced resolution, we enlarged two regions in Fig. 5c (Fig. 5f, i) and corresponding regions in the localization MAP image in Fig. 5d (Fig. 5g, j). The line profiles marked in the magnified images are displayed in Fig. 5h, k. The profiles of the green dashed line a–a′ in Fig. 5f and the blue dashed line b–b′ in Fig. 5g are compared in Fig. 5h. Likewise, the profiles of the green dashed line c–c′ in Fig. 5i and the blue dashed line d–d′ in Fig. 5j are compared in Fig. 5k. As shown in Fig. 5h, the microvessels were clearly resolved into two separate microvessels by L-PAM-GS but were not resolved in the regular PA MAP image. In Fig. 5k, the regular PA profile shows only two microvessels, but the localization profile shows three detached microvessels. We also compared the B-mode images of the region highlighted by the green dashed line in Fig. 5c (Fig. 5l) with its corresponding localization B-mode image (Fig. 5m) to prove the improvement in the axial direction. We compared the profiles of the green dashed line e–e′ in Fig. 5l and the blue dashed line f–f′ in Fig. 5m (Fig. 5n). The blood vessels that could not be resolved in the regular PA B-mode image are well separated in the localization B-mode image, and the profiles confirm this enhancement (Fig. 5n). We compared the B-mode images of the conventional and localization OR-PAM, where a microvessel begins to bifurcate into two, to quantify the improvement in the spatial resolution (Supplementary Fig. S10 and Supplementary Text). These results demonstrate that the improvement in the spatial resolution by a factor of 2.5 was achieved in vivo by an agent-free localization approach. To render a localization PA microvascular image, multiple volumetric data had to be acquired. Thanks to the fast imaging speed (e.g., B-scan rate of 500 Hz) and high SNRs, our system could quickly track the localized PA signals from RBCs by obtaining multiple volumetric data within seconds.