Photoacoustic (PA) imaging can map the physiological conditions of tissue and track the biodistribution of contrast agents; ultrasound localization microscopy (ULM) with microbubbles provides deep-tissue super-resolution blood vessel images and blood velocity maps. When combined, the dual imaging technique will become a powerful tool with diverse applications. However, longer acquisition time of ULM poses a challenge. To address this, we propose sparsity constraint optimization to accelerate ULM and develop an interleaved PA/fast ULM imaging sequences for the dual imaging. We demonstrate 3D and time-lapse dual imaging with a commonly used linear array imaging system in a mouse model.

To achieve a comprehensive optical characterization of collagen within its native environment, we present a novel label-free multimodal polarimetric imaging system that integrates polarization-sensitive second-harmonic generation microscopy, polarization-sensitive optical coherence microscopy, and two-photon autofluorescence lifetime imaging. Through simultaneous and co-registered measurements, this multimodal approach captures different collagen signatures by exploring diverse light-collagen interactions across multiple scales. Quantitative information was retrieved at the tissue level, the collagen fibril level, and the molecular level. We believe that this innovative multimodal and quantitative approach has the potential to significantly enhance our fundamental understanding of collagen, especially its intricate roles in human health and disease.

A combined 1300 nm Optical Coherence Microscopy (OCM) and 488 nm confocal reflectance/ fluorescence microscopy system was designed to perform high-resolution, high-specificity imaging of collagen-embedded spheroids. Spheroids of Hyaluronic Acid (HA) synthase-overexpressing breast epithelial cells alone, or co-cultured with adipose stromal cells were imaged. The volumes, acquired either after fixing and staining or longitudinally with labeling, enabled the visualization of the spheroid morphology, luminal structures, cellular organization, and collagen matrix remodeling. The morphology and internal lumen structures of spheroids, as large as 500 μm in diameter, could be obtained from the OCM volumes, even in the presence of dense collagen matrix surrounding the spheroids. The confocal stacks provided superior specificity to discriminate cells from the compacted collagen along the spheroid’s periphery, up to a depth of ~120 μm. The combined use of OCM and confocal imaging on these spheroid models has added to our understanding of how HA may contribute to tumor initiation and invasion.

Kidney transplantation faces a worldwide shortage due to the lack of reliable assessment for screening qualified donor kidneys for transplantation. We evaluated the feasibility of using polarization-sensitive optical coherence tomography (PS-OCT) to provide a score map covering the entire surface of a kidney to evaluate the pre-transplantation kidney quality. Multiple histology staining and two-photon microscopy (TPM) were used to provide verification standards for microstructures, tissue distributions, and fibrosis in PS-OCT imaging. Our results indicated that PS-OCT was a reliable method for noninvasively imaging kidney microstructure and fibrosis matching the pretransplant scoring system for assessing the quality of pre-transplantation kidneys.

Oral cancer management is challenging as many benign lesions present similarly to precancerous lesions; thus, non-invasive optical tools that can assess tissue status would provide utility in lesion monitoring and biopsy site selection. We hypothesize that there may be oral cancer-sensitive image biomarkers present in a novel image processing technique that interrogates angular scattering behaviour (multipath contrast imaging, MCI). This work retrospectively examines MCI of oral lesions imaged with a widefield endoscopic optical coherence tomography and autofluorescence imaging (OCT-AFI) device. Preliminary analysis shows subtle MCI intensity changes dysplasia and distinct visual changes in carcinoma when compared to contralateral.

We have previously demonstrated multimodal optical coherence tomography and autofluorescence imaging (OCT-AFI) in the distal airways of the lung. To combine the two modalities into a single-fiber endoscope, we use double-clad fibers, which causes additional blurred OCT images from the fibers' higher-order modes. Recently, we established multipath contrast imaging (MCI) which leverages these higher-order images to elucidate angular backscattering of tissue. MCI can be generated retroactively; we seek to re-evaluate images from our in vivo OCT-AFI lung cancer study. Early MCI results demonstrate high contrast in healthy tissue compared to blood, and for a histologically confirmed adenocarcinoma.

3D Fluorescence Molecular Tomography (FMT) is an important pre-clinical tool for quantifying and characterizing molecular markers for different diseases. Herein, we demonstrate, for the first time, 3D K-Space FMT reconstructions using a large time-gated SPAD camera, SwissSPAD2 (SS2). The 3D reconstruction results are obtained using traditional and Deep Neural Network-based inverse solvers. Moreover, the 3D reconstruction performance with SS2 is benchmarked against that obtained with a gated-ICCD camera. The reconstruction results obtained using SS2 are in good agreement with those obtained from the gated-ICCD.

In this work, murine xenografts of pancreatic cancer cell lines AsPC-1 and Mia PaCa-2 were used to investigate vascularization, oxygenation, and the effect of sunitinib treatment on pancreatic cancer. Ultrasound-guided photoacoustic imaging (US-PAI) at multiple wavelengths was utilized to study the tumor vascular networks, throughout the receptor tyrosine kinase inhibitor sunitinib treatment. The 3D photoacoustic data was fluence compensated and spectrally unmixed to acquire the parameters of blood oxygen saturation (StO2) and total hemoglobin concentration (HbT). A custom regional segmentation algorithm was applied to the volumetric HbT images to segment the tumor volume into areas of high vascular density and low vascular density. Regionally evaluating the changes in StO2 and HbT reveals that sunitinib is preferentially targeting areas of low vascular density.

Image classification using Deep Ensemble Learning and Transfer Learning methods is performed on a small, labeled dataset of multimodal nonlinear optical microscopy images coming from Stimulated Raman Scattering, Two Photon Excited Fluorescence and Optical Transmission, to differentiate proliferating cancer cells from senescent ones, a peculiar phenotype following an anti-cancer treatment responsible for tumour relapse. The superior performances of the Deep Ensemble Transfer Learning approach are compared with other less complex neural network architectures. Ultimately, the predictions of the neural network are evaluated using the Grad-CAM visualization approach, which allows highlighting the most important features in the input images responsible for the labels assigned by the network.

Fluorescence lifetime (FLI) parameter estimation of a fluorescence inclusion inside a tissue remains challenging without due correction from Instrument Response function (IRF). Mathematical models, non-linear least-square-fit (‘reconvolution’), center-of-mass (CMM), and Phasor plot methods use IRF correction, however, recent machine learning (ML) models omit correction learning from IRF and often fails in in-vivo samples. Here, we use a transformer-ML model (MFLI-NET) which also takes temporal-point spread function (TPSF) and pixelwise IRF inputs to provide the offset correction due to depth. The MFLI-NET model showed high accuracy and robustness when tested with 1- and 2- exponential in vitro and in-vivo fluorescence samples.

Terahertz and autofluorescence imaging technologies are combined for accurate breast and oral cancer margin detection. More than thirty fresh tissue samples are imaged in this study. Cancer progression causes structural, and metabolic changes which can be probed effectively by combining Terahertz and Autofluorescence technologies and using advanced machine learning algorithms. To train the Machine Learning algorithm, the cancer and noncancer regions in Terahertz and fluorescence images are identified by overlapping with histopathology images. This study confirms that the combination of multiple spectroscopy techniques and Machine Learning algorithms has the potential to achieve better diagnostic accuracy in fresh cancer tissue.

To enhance the functional image-guided capabilities in preclinical radiotherapy research, we developed fluorescence tomography (FT) and integrated it with a commercial bioluminescence tomography (BLT) system for small animal irradiators. We devised a novel method to achieve submillimeter accuracy in mapping the excitation sources and fluorescence data to the imaged animal surface. We expect to achieve approximately 1 mm accuracy in 3D target localization for in vivo tumor model. The multi-model FT/BLT-guided system will provide advanced image-guided capabilities to enhance preclinical radiotherapy research, particularly in cases where radiation is combined with other systemic treatments, thus expanding its applications beyond local therapy.

Triple-negative breast cancer (TNBC) is a challenging disease to treat and there is an unmet need to introduce new tools that offers comprehensive molecular imaging of tissues. Here we introduce an untargeted correlative molecular imaging approach to shed light on TNBC responders and non-responders to chemotherapy. Desorption electrospray ionization (DESI) -mass spectrometry (MS) is an ambient technique for lipidomic/metabolomic imaging. We have developed a comprehensive pipeline to correlate Raman spectroscopy and Fourier-transform infrared spectroscopy imaging with DESI-MSI. This highly complementary transformative imaging tool can enable disruptive research by providing insights in monitoring TNBC progression and identification of new therapeutic targets.

We present label-free morpho-chemical cancer cell phenotyping by combining confocal Raman micro-spectroscopy and three-dimensional holotomography. Observing colon cancer cell types with different progression stages from adenoma to metastasis, we demonstrate the advantage of a multimodal approach for rapid and accurate cancer cell phenotyping. We introduce data processing pipelines to decode and comprehensively interpret molecular and structural information from hyperspectral Raman images and co-registered refractive index tomograms. Finally, we investigate and discuss any unique or shared information that combined Raman spectroscopy and holotomography can provide when characterizing the same sample, and how this synergy advances cell type differentiation.

A multimodal imaging system was developed, integrating swept-source optical coherence microscopy (SS-OCM) with a confocal fluorescence microscope for studying dry eye disease (DED). SS-OCM utilized a 1060 nm central wavelength source, resulting in 7.5 μm axial and 3.4 μm lateral resolution. The fluorescence subsystem employed a 488 nm laser with 2.4 μm lateral resolution. Simultaneous data acquisition at 100 kHz A-scan/pixel was achieved. The system allowed the characterization of the ocular surface's biochemical composition in vivo using fluorescently labeled ligands, facilitating visualization of adhering fluorescent molecules in animal models. Proof-of-concept experiments with a quantum dot-tagged ligand showed potential for improved DED diagnosis and therapeutic response evaluation.

Oral cancer is a global public health challenge, particularly affecting low-resource regions. To address the early detection requirement, we introduce a novel intraoral probe combining conventional oral examination (COE), autofluorescence visualization (AFV), and optical coherence tomography (OCT) for multidimensional oral cancer screening. Real-time COE and AFV offer a broad field of view, while OCT provides depth-resolved imaging. Our handheld probe demonstrates widefield, autofluorescence, and depth-resolved imaging capabilities in clinical settings, holding promise for enhanced early detection and management of oral cancer.