Browsing by Author "Raghuram, Ankit"
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Item Determining the Depth Limit of Bioluminescent Sources in Scattering Media(2020-04-23) Raghuram, Ankit; Veeraraghavan, AshokVisualizing neural activity can uncover new information on how the brain encodes function. In the past, fluorescence-based neural imaging has been the standard, but it results in phototoxicity and photobleaching, making it challenging to perform long term behavioral studies. In the case of deep brain imaging, autofluorescence and excitation light scattering reduce imaging contrast, restricting the depth limit of epifluorescence imaging to 3 mean free paths (MFPs). Techniques like confocal and 2-photon imaging have been developed to improve contrast, but require large, expensive optical hardware to implement. Bioluminescence relies on a chemical pathway for signal generation, avoiding many of the problems associated with the use of excitation light. Bioluminescence also does not need any extra hardware to implement, making it a viable option for in vivo experiments. However, the depth limit of bioluminescence in scattering tissue has yet to be characterized. This study aims to determine at what depth bioluminescent sources can be detected within the brain. We employed a Monte Carlo simulation to study the depth limit of bioluminescence because simulations allow easy tuning of parameters (source emission rate and source density) that affect the detection of bioluminescent sources. Simulation results were then corroborated with an experimental bioluminescence analog. From the study, the depth limit of bioluminescence ranges from 5-10 MFPs, translating to 350 microns deep within neural tissue. An analysis of the feasibility of the emission rates used in this study is also provided, showing that bioluminescence can be a valuable tool for in vivo imaging.Item High density time-of-flight diffuse optical tomography(2022-04-26) Raghuram, Ankit; Veeraraghavan, AshokDiffuse Optical Tomography (DOT) has recently emerged as a methodology for non-invasive brain imaging. The time-of-flight variant (ToF-DOT), with added dimensionality and depth selectivity, is capable of imaging deeper with centimeter-scale resolution. However, for decoding neural activity in the brain, a greater spatial resolution is needed (millimeter-scale) at a depth that has not been well studied (5-10 millimeters deep). We have demonstrated that imaging through the skull can be improved to millimeter-scale resolution using a higher density of sampled measurements than what has been traditionally employed. We have shown these results both with confocally captured and time-gated measurements to reduce experimental runtime, reconstruction runtime, and hardware needed for an integrated chip. We have also demonstrated this procedure with fluorescent markers in a mouse, achieving millimeter-scale spatial resolution and stimulus-triggered dynamic reconstruction through a human skull phantom. Our ToF-DOT system can distinguish between different stimuli applied to the mouse by localizing activity to different regions of the brain. We finally illustrate an extension to human use by demonstrating the potential of a high-density, fiber-based ToF-DOT system for hemodynamic monitoring. These studies highlight the potential of high-density ToF-DOT as a high-accuracy, non-invasive, optical brain-computer-interface.Item Unrolled-DOT: an interpretable deep network for diffuse optical tomography(SPIE, 2023) Zhao, Yongyi; Raghuram, Ankit; Wang, Fay; Kim, Stephen Hyunkeol; Hielscher, Andreas H.; Robinson, Jacob T.; Veeraraghavan, AshokSignificanceImaging through scattering media is critical in many biomedical imaging applications, such as breast tumor detection and functional neuroimaging. Time-of-flight diffuse optical tomography (ToF-DOT) is one of the most promising methods for high-resolution imaging through scattering media. ToF-DOT and many traditional DOT methods require an image reconstruction algorithm. Unfortunately, this algorithm often requires long computational runtimes and may produce lower quality reconstructions in the presence of model mismatch or improper hyperparameter tuning.AimWe used a data-driven unrolled network as our ToF-DOT inverse solver. The unrolled network is faster than traditional inverse solvers and achieves higher reconstruction quality by accounting for model mismatch.ApproachOur model “Unrolled-DOT” uses the learned iterative shrinkage thresholding algorithm. In addition, we incorporate a refinement U-Net and Visual Geometry Group (VGG) perceptual loss to further increase the reconstruction quality. We trained and tested our model on simulated and real-world data and benchmarked against physics-based and learning-based inverse solvers.ResultsIn experiments on real-world data, Unrolled-DOT outperformed learning-based algorithms and achieved over 10× reduction in runtime and mean-squared error, compared to traditional physics-based solvers.ConclusionWe demonstrated a learning-based ToF-DOT inverse solver that achieves state-of-the-art performance in speed and reconstruction quality, which can aid in future applications for noninvasive biomedical imaging.