Serial time-encoded amplified microscopy

Based on the photonic time stretch technique, STEAM holds world records for shutter speed and frame rate in continuous real-time imaging.

[14] The system measured 16 biophysical parameters of cells simultaneously in a single shot and performed hyper-dimensional classification using a Deep Neural Network (DNN).

This direct classification of raw time-stretched data reduced the inference time by orders of magnitude to 700 micro-seconds on a GPU accelerated processor.

The usual techniques of conventional CCD and CMOS cameras are inadequate for capturing fast dynamical processes with high sensitivity and speed; there are technological limitations—it takes time to read out the data from the sensor array and there's a fundamental trade-off between sensitivity and speed: at high frame rates, fewer photons are collected during each frame, a problem that affects nearly all optical imaging systems.

On the other hand, Stroboscopes have a complementary role: they can capture the dynamics of fast events—but only if the event is repetitive, such as rotations, vibrations, and oscillations.

In the second step, the spectrum is mapped into a serial temporal signal that is stretched in time using dispersive Fourier transform to slow it down such that it can be digitized in real-time.

Amplified dispersive Fourier transformation was originally developed to enable ultra wideband analog to digital converters and has also been used for high throughput real-time spectroscopy.

The resolution of STEAM imager is mainly determined by diffraction limit, the sampling rate of the back-end digitizer, and spatial dispersers.

Each pulse representing one frame of the camera is then stretched in time so that it can be digitized in real-time by an electronic analog-to-digital converter (ADC).

The ultra-fast pulse illumination freezes the motion of high-speed cells or particles in flow to achieve blur-free imaging.

Integrated with a microfluidic channel, coherent time stretch imaging system measures both quantitative optical phase shift and loss of individual cells as a high-speed imaging flow cytometer, capturing millions of line-images per second in flow rates as high as a few meters per second, reaching up to hundred-thousand cells per second throughput.

The time stretch quantitative phase imaging can be combined with machine learning to achieve very accurate label-free classification of the cells.

This method is useful for a broad range of scientific, industrial, and biomedical applications that require high shutter speeds and frame rates.