Search for: All records

Mid-infrared microscopy is an important tool for biological analyses, allowing a direct probe of molecular bonds in their low energy landscape. In addition to the label-free extraction of spectroscopic information, the application of broadband sources can provide a third dimension of chemical specificity. However, to enable widespread deployment, mid-infrared microscopy platforms need to be compact and robust while offering high speed, broad bandwidth, and high signal-to-noise ratio. In this study, we experimentally showcase the integration of a broadband, high-repetition-rate dual-comb spectrometer (DCS) in the mid-infrared range with a scanning microscope. We employ a set of 1-GHz mid-infrared frequency combs, demonstrating their capability for high-speed and broadband hyperspectral imaging of polymers and ovarian tissue. The system covers 1000 cm−1 at νc = 2941 cm−1 with 12.86 kHz spectra acquisition rate and 5 µm spatial resolution. Taken together, our experiments and analysis elucidate the trade-off between bandwidth and speed in DCS as it relates to microscopy. This provides a roadmap for the future advancement and application of high-repetition-rate DCS hyperspectral imaging. 

This study introduces label-free, automated ovarian tissue cell recognition using O-PTIR imaging, offering 10× better resolution than FTIR. It outperforms FTIR, achieving 0.98 classification accuracy. This work aids early ovarian cancer diagnosis. 

Mid-infrared Spectroscopic Imaging (MIRSI) provides spatially-resolved molecular specificity by measuring wavelength-dependent mid-infrared absorbance. Infrared microscopes use large numerical aperture objectives to obtain high-resolution images of heterogeneous samples. However, the optical resolution is fundamentally diffraction-limited, and therefore wavelength-dependent. This significantly limits resolution in infrared microscopy, which relies on long wavelengths (2.5 μm to 12.5 μm) for molecular specificity. The resolution is particularly restrictive in biomedical and materials applications, where molecular information is encoded in the fingerprint region (6 μm to 12 μm), limiting the maximum resolving power to between 3 μm and 6 μm. We present an unsupervised curvelet-based image fusion method that overcomes limitations in spatial resolution by augmenting infrared images with label-free visible microscopy. We demonstrate the effectiveness of this approach by fusing images of breast and ovarian tumor biopsies acquired using both infrared and dark-field microscopy. The proposed fusion algorithm generates a hyperspectral dataset that has both high spatial resolution and good molecular contrast. We validate this technique using multiple standard approaches and through comparisons to super-resolved experimentally measured photothermal spectroscopic images. We also propose a novel comparison method based on tissue classification accuracy. 

Older citizens experience a large number of falls and hospitalizations per year throughout the world. These intervening health events (IHEs) such as falls/injuries, illnesses, hospitalizations, are strong precipitants of disability in older adults. They are episodic in nature, extremely difficult to study, and require continuous and long-term monitoring. Wearable technologies with remote capabilities are an ideal solution for capturing information before and after such events. This work presents the ROAMM campaign platform for harnessing sensor and interface capabilities on smart wearables to provide customizable, affordable, and versatile health monitoring that leads to practical remote-based interventions. The platform is flexible, efficient, and scalable for concurrently running multiple studies, each of which consists of patient-reported outcomes, ecological momentary assessments and mental health-related patient responses. Additionally, the system is able to capture and derive ecological, momentary assessments of pain with concurrent mobility tracking that allows life-space mobility ascertainment. The platform supports multiple watches, and we show implementations on both the Samsung Galaxy and Apple series of smartwatches.