image: (a) Light field microscopy (LFM) imaging scheme. 3D distributed samples will be collected by a standard microscopy (i.e. an objective with a tube lens), then coded by a microlens array (MLA) and captured by a camera. Zoom-in panel ① shows the relationship of a sample space point with the pattern in the native objective plane (NIP). Zoom-in panel ② shows an exemplary LFM PSF captured by the camera. (b) Principle of DiLFM. After ringing-reduced RL reconstruction, DiLFM replaces low-quality image elements with high-quality ones to suppress reconstruction artifacts. view more
Credit: by Yuanlong Zhang, Bo Xiong, Yi Zhang, Zhi Lu, Jiamin Wu and Qionghai Dai
Light field microscopy (LFM) has been widely used for observing multiple biological dynamics such as embryo membrane dynamics, neuronal calcium transients, and blood cell flow in beating heart, to name a few. The scanning-free manner of LFM pave the way for simultaneously 3D recording of complex cellular and organa structures in high speed, and the photon efficiency by fully utilizing emitted photons volumetrically make LFM to be suitable for long-term observing of phototoxicity sensitive samples. However, However, LFM suffers from artifact contamination due to the illness of the reconstruction problem via naïve Richardson-Lucy (RL) deconvolution and the performance of LFM significantly dropped in low-light conditions since noise due to absence of sample prior.
In a new paper published in Light Science & Application, a team of scientists, led by Professor Qionghai Dai from Institute for Brain and Cognitive Sciences, Department of Automation and Tsinghua Shenzhen International Graduate School, Tsinghua University, China and co-workers have proposed a new LFM method based on dictionary patching, termed DiLFM, to enable robust artifact-free fast volumetric imaging under different noisy conditions without hardware modification. The approach is motivated by recent results in sparse signal representation, which suggests that artifact-free signals can be well represented using a linear combination of few elements from a redundant dictionary even under heavily noisy conditions. The systematic artifacts due to the low sampling rate in LFM can be compensated by dictionary priors learned from general biological samples.
“Our DiLFM reconstruction is a combination of a few RL iterations to provide basic but ringing-reduced 3D volumes with a dictionary patching process to fix the reconstruction artifacts and improve the resolution and contrast. With the robustness of both few-run RL and dictionary patching in low SNR conditions, our DiLFM provides superior performances over other methods under noise contaminations.” they added.
###
Journal
Light Science & Applications