Theoretical minimum uncertainty of single-molecule localizations using a single-photon avalanche diode array

Quantifying the minimum localization uncertainty of image scanning localization microscopy

Modulation enhanced single-molecule localization microscopy (meSMLM), where emitters are sparsely activated with sequentially applied patterned illumination, increases the localization precision over single-molecule localization microscopy (SMLM). The precision improvement of modulation enhanced SMLM is derived from retrieving the position of an emitter relative to individual illumination patterns, which adds to existing point spread function information from SMLM. Here, we introduce SpinFlux: modulation enhanced localization for spinning disk confocal microscopy. SpinFlux uses a spinning disk with pinholes in its illumination and emission paths, to sequentially illuminate regions in the sample during each measurement. The resulting intensity-modulated emission signal is analyzed for each individual pattern to localize emitters with improved precision. We derive a statistical image formation model for SpinFlux and we quantify the theoretical minimum localization uncertainty in terms of the Cramér-Rao lower bound. Using the theoretical minimum uncertainty, we compare SpinFlux to localization on Fourier reweighted image scanning microscopy reconstructions. We find that localization on image scanning microscopy reconstructions with Fourier reweighting ideally results in a global precision improvement of 2.1 over SMLM. When SpinFlux is used for sequential illumination with three patterns around the emitter position, the localization precision improvement over SMLM is twofold when patterns are focused around the emitter position. If four donut-shaped illumination patterns are used for SpinFlux, the maximum local precision improvement over SMLM is increased to 3.5. Localization of image scanning microscopy reconstructions thus has the largest potential for global improvements of the localization precision, where SpinFlux is the method of choice for local refinements.

PE-RASP: range image stitching of photon-efficient imaging through reconstruction, alignment, stitching integration network based on intensity image priors

Single photon imaging integrates advanced single photon detection technology with Laser Radar (LiDAR) technology, offering heightened sensitivity and precise time measurement. This approach finds extensive applications in biological imaging, remote sensing, and non-visual field imaging. Nevertheless, current single photon LiDAR systems encounter challenges such as low spatial resolution and a limited field of view in their intensity and range images due to constraints in the imaging detector hardware. To overcome these challenges, this study introduces a novel deep learning image stitching algorithm tailored for single photon imaging. Leveraging the robust feature extraction capabilities of neural networks and the richer feature information present in intensity images, the algorithm stitches range images based on intensity image priors. This innovative approach significantly enhances the spatial resolution and imaging range of single photon LiDAR systems. Simulation and experimental results demonstrate the effectiveness of the proposed method in generating high-quality stitched single-photon intensity images, and the range images exhibit comparable high quality when stitched with prior information from the intensity images.

Fundamental limits to depth imaging with single-photon detector array sensors

Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging technology. These multi-pixel sensors have single-photon sensitivities and pico-second temporal resolutions thus they can rapidly generate depth images with millimeter precision. Such sensors are a key enabling technology for future autonomous systems as they provide guidance and situational awareness. However, to fully exploit the capabilities of SPAD array sensors, it is crucial to establish the quality of depth images they are able to generate in a wide range of scenarios. Given a particular optical system and a finite image acquisition time, what is the best-case depth resolution and what are realistic images generated by SPAD arrays? In this work, we establish a robust yet simple numerical procedure that rapidly establishes the fundamental limits to depth imaging with SPAD arrays under real world conditions. Our approach accurately generates realistic depth images in a wide range of scenarios, allowing the performance of an optical depth imaging system to be established without the need for costly and laborious field testing. This procedure has applications in object detection and tracking for autonomous systems and could be easily extended to systems for underwater imaging or for imaging around corners.

Thermal drift correction method for laboratory nanocomputed tomography based on global mixed evaluation

Nanocomputed tomography (nanoCT) is an effective tool for the nondestructive observation of 3D structures of nanomaterials; however, it requires additional correction phantom to reduce artifacts induced by the focal drift of the X-ray source and mechanical thermal expansion. Drift correction without a correction phantom typically uses rapidly acquired sparse projections to align the original projections. The noise and brightness difference in the projections limit the accuracy of existing feature-based methods such as locality preserving matching (LPM) and random sample consensus (RANSAC). Herein, a rough-to-refined correction framework based on global mixed evaluation (GME) is proposed for precise drift estimation. First, a new evaluation criterion for projection alignment, named GME, which comprises the structural similarity (SSIM) index and average phase difference (APD), is designed. Subsequently, an accurate projection alignment is achieved to estimate the drift by optimizing the GME within the proposed correction framework based on the rough-to-refined outlier elimination strategy. The simulated 2D projection alignment experiments show that the accuracy of the GME is improved by 14× and 12× than that of the mainstream feature-based methods LPM and RANSAC, respectively. The proposed method is validated through actual 3D imaging experiments.

Theoretical minimum uncertainty of single-molecule localizations using a single-photon avalanche diode array

Single-photon avalanche diode (SPAD) arrays can be used for single-molecule localization microscopy (SMLM) because of their high frame rate and lack of readout noise. SPAD arrays have a binary frame output, which means photon arrivals should be described as a binomial process rather than a Poissonian process. Consequentially, the theoretical minimum uncertainty of the localizations is not accurately predicted by the Poissonian Cramér-Rao lower bound (CRLB). Here, we derive a binomial CRLB and benchmark it using simulated and experimental data. We show that if the expected photon count is larger than one for all pixels within one standard deviation of a Gaussian point spread function, the binomial CRLB gives a 46% higher theoretical uncertainty than the Poissonian CRLB. For typical SMLM photon fluxes, where no saturation occurs, the binomial CRLB predicts the same uncertainty as the Poissonian CRLB. Therefore, the binomial CRLB can be used to predict and benchmark localization uncertainty for SMLM with SPAD arrays for all practical emitter intensities.