Chronic cranial window (CCW) has been widely used to provide optical access to the brain cortex for longitudinal imaging, while preserving the physiological environments of the brain. Various optical imaging modalities have been demonstrated with CCW for longitudinal brain imaging, including single-photon and multi-photon fluorescence microscopy, optical coherence tomography, and photoacoustic microscopy (PAM). However, sophisticated surgeries and windows designs are still required for chronic imaging and direct cellular recording or manipulation because the surgical implantation of CCW precludes direct physical access to the brain other than optical access. Here, we report an active CCW with integrated ultrasound sensor based on a transparent microring resonator (MRR). The MRR is fabricated on a quartz substrate by using low-cost soft nanoimprinting lithography (sNIL) and then integrated on a glass window with optical fibers for disposable ultrasound-sensing CCW (usCCW). Encapsulating MRR inside an acoustic impedance matched protection layer further improves its reliability for in vivo applications over the observed period of a month. The functions of the active usCCW are experimentally validated through longitudinal (PAM) imaging of cortical vasculature in live mice over a 28-days period.
Infants born at an extremely low gestational age (ELGA, < 29 weeks) are at an increased risk of intraventricular hemorrhage (IVH), and there is a need for standalone, safe, easy -to -use tools for monitoring cerebral hemodynamics. We have built a multi -wavelength multi -distance diffuse correlation spectroscopy device (MW-MD-DCS), which utilizes timemultiplexed, long -coherence lasers at 785, 808, and 853 nm, to simultaneously quantify the index of cerebral blood flow (CBFi) and the hemoglobin oxygen saturation (SO2). We show characterization data on liquid phantoms and demonstrate the system performance on the forearm of healthy adults, as well as clinical data obtained on two preterm infants.
Limited throughput is a key challenge in in-vivo deep-tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the wide-field imaging modalities used for optically cleared or thin specimens. We recently introduced 'De-scattering with Excitation Patterning or DEEP', as a widefield alternative to point-scanning geometries. Using patterned multiphoton excitation, DEEP encodes spatial information inside tissue before scattering. However, to de-scatter at typical depths, hundreds of such patterned excitations are needed. In this work, we present DEEP$^2$, a deep learning based model, that can de-scatter images from just tens of patterned excitations instead of hundreds. Consequently, we improve DEEP's throughput by almost an order of magnitude. We demonstrate our method in multiple numerical and physical experiments including in-vivo cortical vasculature imaging up to four scattering lengths deep, in alive mice.
