𝜕𝜇 : Differentiable Microscopy | With applications ranging from, rare cellular event detection to drug screening, high content imaging provides biomedically important morphological features of cells or tissues via high speed acquisition hardware and fast image processing algorithms. Despite significant advances in faster and more multiplexed imaging sensors, the imaging throughput is currently limited by the speed of electronics hardware. In Wadduwage Lab we use an orthogonal approach, termed differential microscopy (𝜕𝜇), to improve the imaging throughput beyond existing electronic hardware bottleneck. The rationale for 𝜕𝜇 is that low-dimensional representations of image signals exists, and can be found through learning-based techniques; instruments can thus be designed to perform measurements on the lower dimensional compressed representation, improving the throughput by the factor of compression. In order to achieve this, 𝜕𝜇 models the front-end optics and the back-end image-processing algorithms together as a differentiable autoencoder of learnable parameters.
DEEP : De-scattering with Excitation Patterning | Nonlinear optical microscopy has enabled in vivo deep tissue imaging on the millimeter scale. A key unmet challenge is its limited throughput especially compared to rapid wide-field modalities that are used ubiquitously in thin specimens. Any wide-field approach would suffer from emission photons scattering inside the specimen, resulting degraded image contrast and image resolution. To address this challenge, we introduce a novel technique called De-scattering with Excitation Patterning, or ‘DEEP’, which uses patterned nonlinear excitation followed by computational imaging assisted wide-field detection. Multiphoton temporal focusing allows high resolution excitation patterns to be projected deep inside specimen at multiple scattering lengths due to the use of long wavelength light. Computational reconstruction allows high resolution structural features to be reconstructed from tens to hundreds of DEEP images, instead of millions of point-scanning measurements.
Wide-field Hyperspectral Imaging | Hyperspectral imaging has a number of applications in bio-imaging. Many biological molecules have distinct spectral signatures that may be sensitive to local biochemical microenvironments. Hyperspectral imaging can also be used to resolve spectrally overlapping fluorescent labels enabling highly multiplexed fluorescence imaging. However, in order to capture both spatial and spectral information the number of measurements needed can be overwhelmingly large. In our lab we work on hyperspectral imaging technologies that can inherently measure information in compressed forms. This compressed acquisition helps us cutdown measurement time by almost an order of magnitude.
High-throughput Raman Imaging | Raman imaging can be used to extract chemical information of cell and tissue specimen, label-free. Label-free Raman based non-destructive pathology approaches have been proposed with the bottleneck being the imaging time. Imaging throughput can be improved by substituting point-scanning spectral imagers with wide-field geometries. However, wide-field techniques require engineering power efficient excitations to maintain the excitation power per point. With the help of our collaborators, we are working on engineering photonics chips to enable a power efficient excitation geometry for Raman imaging.