Optimal compressive multiphoton imaging at depth using single-pixel detection

Abstract

Compressive sensing can overcome the Nyquist criterion and record images with a fraction of the usual number of measurements required. However, conventional measurement bases are susceptible to diffraction and scattering, prevalent in high-resolution microscopy. Here, we explore the random Morlet basis as an optimal set for compressive multiphoton imaging, based on its ability to minimise the space-frequency uncertainty. We implement this approach for the newly developed method of wide-field multiphoton microscopy with single-pixel detection (TRAFIX), which allows imaging through turbid media without correction. The Morlet basis is well-suited to TRAFIX at depth, and promises a route for rapid acquisition with low photodamage.

Scanless two-photon excitation with temporal focusing

“…These benefits are achieved at the cost of higher excitation power, compared to 2P point-scanning. For now, the acquisition time for TRAFIX is limited by the number of orthogonal light patterns (4092 or 1024) required for image reconstruction, but higher acquisition speed can be reached by incorporating a compressive sensing approach 89 .…”

Ultrafast Light Targeting for High-Throughput Precise Control of Neuronal Networks

“…The FliT approach will enable the generation of single or multiple shaped temporally focused spots for fast multi-target imaging using e.g. voltage indicators, or for fast compressive multiphoton imaging 43 . Also, the possibility to rapidly switch between different holograms each introducing different defocusing effects can be exploited for fast repositioning of the imaging focus and ultrafast fast volumetric imaging 5,44 .…”

Single-pixel imaging of dynamic objects using multi-frame motion estimation

“…Our local-motion algorithm may be used for imaging dynamic objects moving over a relatively static background. Specific application include fluorescence microscopy 11 and two photon microscopy [12][13][14] . In such applications, merely distinguishing between the background and foreground can significantly reduce the size of the the region that needs to be updated between subsequent frames, enabling significant acceleration in image acquisition.…”

“…To recover the image from the measured data, a computational technique is used. SPI is an attractive approach in fields where camera technology is limited and it has found numerous applications in the last decade: Terahertz imaging 1 , LIDAR 2 , compressed sensing 3 , single pixel telescope 4 , optoacoustic imaging 5 , multispectral imaging 6 , 3D imaging 7,8 , imaging through scattering medium 9,10 , fluorescence microscopy 11 , multiphoton imaging [12][13][14] and image encryption 15,16 .…”