Optical imaging technology serves as a powerful tool in neuroscience study for recording large populations of neurons in vivo. Here we present a compact lensless microscope that breaks the fundamental tradeoff in lens-based imaging systems to simultaneously achieve large field-of-view (FOV) and high-resolution imaging. Our prototype lensless microscope incorporates a “contour” phase mask with an integrated illumination system giving us improved performance for allowing us to demonstrate the first functional imaging with a lensless microscope in behaving non-human primates (NHPs). Specifically, we successfully imaged over a 16 mm2 FOV on primary visual cortex of NHPs, and measured how cortical activity changes as a function of the stimulus position. The extracted position tuning information from our lensless microscope has good correspondence to the ground truth captured by a tabletop widefield microscope system.
SignificanceImaging 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.
We present here a new fluorescence molecular tomographic model that can provide ultrahigh spatial and temporal resolution reconstruction through sparsity constrained dimensional reduction. The new method implements a novel sparsity function to asymptotically enforce the sparsest representation of fluorescent targets while reducing the problem dimension based correlation between sensing matrix and measurement. Parameterized temporal data (TD) 𝐿(𝑠), available from the Laplace transform, is used here as input to the inverse model for their computational efficiency and accuracy and robustness to noise. We use radiative transfer equation (RTE) as a light propagation model as it provides more accurate predictions of light propagation in small-volume tissue. The performance of this new method is evaluated through numerical phantoms, focusing on spatial resolution and computational speed. The results show that the sparsity constrained dimensional reduction inverse model can achieve near cellular resolution (~1mm spatial resolution) at depth of 70 mean free paths (MFPs) within ~25 milliseconds.
We present a method to passively edge couple multiple optical fibers with silicon nitride waveguides in the visible wavelengths. We efficiently convert the fiber mode to the waveguide mode using an inverse taper mode size converter and support passive alignment using a U-groove that centers the optical fiber to the inverse taper. In our prototypes, we measure a coupling efficiency of −4.2 dB per facet. To reduce light leakage to the silicon substrate, we use a 6-μm oxide layer, which also eliminates the additional processes of undercutting the silicon substrate underneath the waveguide. Furthermore, the U-groove structure has a polished edge surface for coupling, reducing the steps of edge polishing the die. Fabrication of this visible-range edge coupler is complementary metal–oxide–semiconductor-compatible, making it a highly scalable method for passively packaging multiple visible-range integrated photonics devices.
In most biological tissues, light scattering due to small differences in refractive index limits the depth of optical imaging systems. Two-photon microscopy (2PM), which significantly reduces the scattering of the excitation light, has emerged as the most common method to image deep within scattering biological tissue. This technique, however, requires high-power pulsed lasers that are both expensive and difficult to integrate into compact portable systems. Using a combination of theoretical and experimental techniques, we show that if the excitation path length can be minimized, selective plane illumination microscopy (SPIM) can image nearly as deep as 2PM without the need for a high-powered pulsed laser. Compared to other single-photon imaging techniques like epifluorescence and confocal microscopy, SPIM can image more than twice as deep in scattering media (∼10 times the mean scattering length). These results suggest that SPIM has the potential to provide deep imaging in scattering media in situations in which 2PM systems would be too large or costly.
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