Bhanu Pratap Singh | Searcharxiv

arXiv2026

Compact Optical-Resolution Photoacoustic Microscopy System with Reflective Objective-Based Transducer Integration

We present an optical-resolution photoacoustic microscopy (OR-PAM) system designed to overcome key limitations in conventional transducer integration within a compact microscopy configuration, while preserving high optical performance and improving acoustic detection efficiency. The system uses a reflective objective that reduces spatial constraints within the optical pathway, enabling the integration of a large-area PVDF transducer within the optical obscuration zone. The system performance was characterized through spatial resolution analysis, laser pulse energy measurement at the sample plane, and evaluation of photoacoustic signal dependence on laser pulse energy. For biological validation, OR-PAM imaging of sections from B16F10 tumors implanted in mice was performed and compared with optical microscopy and H&E stained histological sections. The results demonstrate strong spatial correlation between photoacoustic signal intensity and melanin rich regions, confirming label-free sensitivity to endogenous optical absorbers at 532 nm. This work establishes a compact OR-PAM imaging setup with improved optical-acoustic integration for high resolution biomedical imaging applications, with potential for future extension to multi-wavelength laser excitation.

physics.optics↗

arXiv2019

Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)

Missing values, irregularly collected samples, and multi-resolution signals commonly occur in multivariate time series data, making predictive tasks difficult. These challenges are especially prevalent in the healthcare domain, where patients' vital signs and electronic records are collected at different frequencies and have occasionally missing information due to the imperfections in equipment or patient circumstances. Researchers have handled each of these issues differently, often handling missing data through mean value imputation and then using sequence models over the multivariate signals while ignoring the different resolution of signals. We propose a unified model named Multi-resolution Flexible Irregular Time series Network (Multi-FIT). The building block for Multi-FIT is the FIT network. The FIT network creates an informative dense representation at each time step using signal information such as last observed value, time difference since the last observed time stamp and overall mean for the signal. Vertical FIT (FIT-V) is a variant of FIT which also models the relationship between different temporal signals while creating the informative dense representations for the signal. The multi-FIT model uses multiple FIT networks for sets of signals with different resolutions, further facilitating the construction of flexible representations. Our model has three main contributions: a.) it does not impute values but rather creates informative representations to provide flexibility to the model for creating task-specific representations b.) it models the relationship between different signals in the form of support signals c.) it models different resolutions in parallel before merging them for the final prediction task. The FIT, FIT-V and Multi-FIT networks improve upon the state-of-the-art models for three predictive tasks, including the forecasting of patient survival.

cs.LG↗