Dual modality intravascular catheter system combining pulse-sampling fluorescence lifetime imaging and polarization-sensitive optical coherence tomography.

The clinical management of coronary artery disease and the prevention of acute coronary syndromes require knowledge of the underlying atherosclerotic plaque pathobiology. Hybrid imaging modalities capable of comprehensive assessment of biochemical and morphological plaques features can address this need. Here we report the first implementation of an intravascular catheter system combining fluorescence lifetime imaging (FLIm) with polarization-sensitive optical coherence tomography (PSOCT).

Finite-Time Lyapunov Exponents of Deep Neural Networks.

We compute how small input perturbations affect the output of deep neural networks, exploring an analogy between deep feed-forward networks and dynamical systems, where the growth or decay of local perturbations is characterized by finite-time Lyapunov exponents. We show that the maximal exponent forms geometrical structures in input space, akin to coherent structures in dynamical systems. Ridges of large positive exponents divide input space into different regions that the network associates with different classes.

Pulse-sampling fluorescence lifetime imaging: evaluation of photon economy.

This Letter presents an experimental study comparing the photon rate and photon economy of pulse sampling fluorescence lifetime imaging (PS-FLIm) with the conventional time-correlated single photon counting (TCSPC) technique. We found that PS-FLIm has a significantly higher photon detection rate (200 MHz) compared with TCSPC (2-8 MHz) but lower photon economy (4-5 versus 1-1.3).

Steering undulatory micro-swimmers in a fluid flow through reinforcement learning.

This work aims at finding optimal navigation policies for thin, deformable microswimmers that progress in a viscous fluid by propagating a sinusoidal undulation along their slender body. These active filaments are embedded in a prescribed, non-homogeneous flow, in which their swimming undulations have to compete with the drifts, strains, and deformations inflicted by the outer velocity field. Such an intricate situation, where swimming and navigation are tightly bonded, is addressed using various methods of reinforcement learning.

In vivo characterization of the human glioblastoma infiltrative edge with label-free intraoperative fluorescence lifetime imaging.

Challenges in identifying a glioblastoma's infiltrative edge during neurosurgical procedures result in rapid recurrence. A label-free fluorescence lifetime imaging (FLIm) device was used to evaluate glioblastoma's infiltrative edge in 15 patients (89 samples). FLIm data were analyzed according to tumor cell density, infiltrating tissue type (gray and white matter), and diagnosis history (new or recurrent).