Tae Joong Eom

Biophotonics is an interdisciplinary field at the intersection of photonics, biology, and medicine, driving advances in biomedical imaging, sensing, diagnosis, and therapy. With rapid developments in optical imaging, computational optics, artificial intelligence, nanophotonics, and advanced optical instrumentation, this feature issue highlights recent advances that span fundamental research, enabling technologies, and emerging biomedical applications.

Lipid accumulation is a key pathological feature of metabolic and cardiovascular diseases, but single-wavelength photoacoustic (PA) imaging at 1726 nm can be affected by non-lipid tissue backgrounds, limiting reliable interpretation in heterogeneous tissues. Here, we present a dual-wavelength reflection-mode photoacoustic microscopy (PAM) system using paired 1726- and 1558-nm excitation to classify lipid-dominant spectral responses. A gain-switched thulium-doped fiber laser generated 1726-nm lipid-sensitive pulses and residual 1558-nm low-lipid reference pulses in a temporally multiplexed format. From the dual-wavelength PA images, a lipid-sensitive normalized difference index (LNDI) was calculated and partitioned using three-class multi-Otsu thresholding into non-lipid-dominant, intermediate, and lipid-dominant response ranges. Phantom, ex vivo meat, and MCD diet-induced fatty-liver experiments demonstrated improved spectral separation and image-adaptive visualization of heterogeneous lipid-associated responses beyond single-wavelength PA amplitude.

Photoacoustic microscopy (PAM) enables high‐resolution, label‐free imaging by detecting ultrasound generated from pulsed‐laser absorption. For high‐fidelity spatiospectral imaging with single‐raster scanning, two spectrally distinct pulses should arrive nearly simultaneously to preserve spatial–temporal correlation, and at least one wavelength should be tunable for exogenous agents. We present spatiospectral PAM (SS‐PAM) driven by a gain‐switched Ti:sapphire laser engineered for this purpose. The source provides 735–855 nm tunability spanning hemoglobin and NIR‐I agents (e.g., indocyanine green, ICG) and is intrinsically synchronized to a 532 nm pump with a ≈150 ns gain‐switch delay, producing dual pulses for spatiospectral excitation. Operating at 20–30 kHz with pulse energies up to 63 µJ, SS‐PAM yields temporally correlated, high‐contrast excitation beyond sequential or dual‐laser schemes. In vivo, we quantified hemoglobin oxygen saturation (sO ₂ ) under controlled hypoxia, tracked vascular transport after intravenous ICG, visualized peritumoral lymphatic drainage, and analyzed oxygen extraction in tumor‐associated microvasculature, establishing SS‐PAM as a robust platform for synchronized, vessel‐level functional and molecular imaging.

Scanning acoustic microscopy is a non-destructive imaging technique that visualizes internal features of microstructures by raster-scanning a focused ultrasound beam. While high-quality imaging typically requires densely sampled data, this leads to prolonged data acquisition times. In this study, we propose a deep learning model that enables the generation of high-quality images from sparsely sampled data, thereby significantly reducing scanning time. The proposed model successfully reconstructed high-quality images from sparse data acquired with up to 8-fold sparsity, achieving an 8-fold acceleration in data acquisition speed without compromising image quality. Compared to conventional mathematical interpolation methods, the proposed model provides substantially sharper and more detailed reconstructions, particularly suitable for identifying fine internal features. Because of its adaptability to other raster-scanning-based imaging techniques, this approach holds broad potential for accelerating non-destructive evaluation workflows in fields such as semiconductor inspection, microelectronics packaging, and structural monitoring of advanced materials.

Liver diseases typically exhibit a progressive course, beginning with fat accumulation and advancing through inflammation, fibrosis, and cirrhosis. Cumulative changes in tissue structure and function can lead to various complications during this process. To monitor the stages of liver disease, it is essential to image or map lipid droplet accumulation and liver fibrosis. The 1700 nm wavelength band is suited for detecting lipid accumulation due to its strong light absorption by lipid. In this study, we developed a spectroscopic polarization-sensitive OCT(SPS-OCT) imaging system that enables simultaneous spectroscopic imaging and birefringence analysis of tissue using 1700 nm swept-source. To demonstrate its utility for liver disease evaluation, we applied the systems to mouse models of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH).

Polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of backscattered light from tissues and provides valuable insights into the birefringence properties of biological tissues. Contrastive unpaired translation (CUT) was used in this study to generate a synthetic PS-OCT image from a single OCT image. The challenges related to extensive data requirements relying on labeled datasets using only pixel-wise correlations that make it difficult to efficiently regenerate the periodic patterns observed in PS-OCT images were addressed. The CUT model captures birefringence patterns by leveraging patch-wise correlations from unpaired data, which allows learning of the underlying structural features of biological tissues responsible for birefringence. To demonstrate the performance of the proposed approach, three generative models (Pix2pix, CycleGAN, and CUT) were compared on an in vivo dataset of injured mouse tendons over a six-week healing period. CUT outperformed Pix2pix and CycleGAN by producing high-fidelity synthetic PS-OCT images that closely matched the original PS-OCT images. Pearson correlation and two-way ANOVA tests confirmed the superior performance of CUT (p-value < 0.0001) over the comparison models. Additionally, a ResNet-152 classification model was used to assess tissue damage, which achieved an accuracy of up to 90.13% compared to the original PS-OCT images. This research demonstrates that CUT is superior to conventional methods for generating high-quality synthetic PS-OCT images and offers better improvements in most scenarios, in terms of efficiency and image fidelity.

The extent of surgical resection is an important prognostic factor in the treatment of patients with glioblastoma. Optical coherence tomography (OCT) imaging is one of the adjunctive methods available to achieve the maximal surgical resection. In this study, the tumor margins were visualized with the OCT image obtained from a murine glioma model. A commercialized human glioblastoma cell line (U-87) was employed to develop the orthotopic murine glioma model. A swept-source OCT (SS-OCT) system of 1300 nm was used for three-dimensional imaging. Based on the OCT intensity signal, which was obtained via accumulation of each A-scan data, an en-face optical attenuation coefficient (OAC) map was drawn. Due to the limited working distance of the focused beam, OAC values decrease with depth, and using the OAC difference in the superficial area was chosen to outline the tumor boundary, presenting a challenge in analyzing the tumor margin along the depth direction. To overcome this and enable three-dimensional tumor margin detection, we converted the en-face OAC map into an en-face difference map with x- and y-directions and computed the normalized absolute difference (NAD) at each depth to construct a volumetric NAD map, which was compared with the corresponding H&E-stained image. The proposed method successfully revealed the tumor margin along the peripheral boundaries as well as the margin depth. We believe this method can serve as a useful adjunct in glioma surgery, with further studies necessary for real-world practical applications.

This study presents a novel nondestructive analysis method for precise characterization of corroded copper oxidation using optical coherence microscopy (OCM). By exploiting the partial light transmission through metallic oxide layers, we employed a specialized OCM system with a wavelength of 1700nm and enhanced the analysis accuracy compared to conventional optical coherence tomography (OCT). The developed OCM system featured a numerical aperture (NA) of 0.15, providing improved surface profiling and higher lateral resolution than OCT. we developed a peak-finding algorithm to accurately determine the thickness of the copper oxide layer from the acquired interference data with zero padding. Our method was validated by comparing the measured thickness profiles with those obtained from scanning electron microscope (SEM) images of corroded metals. The copper oxidation specimens were prepared after heat treatment for 1, 2, 4, and 8 h in an alumina tube furnace at a temperature of 900 °C to find the correlation between the OCM thickness measurement. Additionally, the acquired enface 3D images enabled the identification of local corrosion distribution within a 4 mm × 4 mm area. The en-face mapping images are utilized to analyze the uniformity of the metal oxidation process across the imaging area of the copper oxidation specimens. With an increase in heat treatment time, the median value of the thickness histogram for the copper oxide within the area consistently remained around 10 µm. However, the thickness variation ranged from -2 µm to 5 µm. This indicates that as the heat treatment time progresses, the thickness of the copper oxide becomes more non-uniform. Our technique holds great potential for nondestructive and noncontact detection of metal corrosion and assessment of corrosion rates in various industrial applications. Future research efforts could focus on expanding the application of OCM to different metals and exploring its commercialization prospects for practical implementation in diverse industries.