No abstract

Four-dimensional fluorescence microscopy-which records 3D image information as a function of time-provides an unbiased way of tracking dynamic behavior of subcellular components in living samples and capturing key events in complex macromolecular processes. Unfortunately, the combination of phototoxicity and photobleaching can severely limit the density or duration of sampling, thereby limiting the biological information that can be obtained. Although widefield microscopy provides a very light-efficient way of imaging, obtaining high-quality reconstructions requires deconvolution to remove optical aberrations. Unfortunately, most deconvolution methods perform very poorly at low signal-to-noise ratios, thereby requiring moderate photon doses to obtain acceptable resolution. We present a unique deconvolution method that combines an entropy-based regularization function with kernels that can exploit general spatial characteristics of the fluorescence image to push the required dose to extreme low levels, resulting in an enabling technology for high-resolution in vivo biological imaging.4D microscopy | low dose microscopy | noise-suppressing regularization T he study of dynamic processes is an important facet of cell biology research. Fluorescently tagged proteins combined with four-dimensional fluorescence microscopy, which records 3D image information as a function of time, provide a powerful framework for studying the dynamics of molecular processes in vivo. One of the most crucial challenges in 4D fluorescence microscopy is to ensure that normal biological function is not significantly perturbed as a result of the high doses of illumination (phototoxicity) incurred during 4D imaging. Recent work indicates that the maximal photon dose that avoids biological perturbation is 100-to 1,000-fold lower than that typically used for in vivo imaging (1). Dose limitations are even more challenging, given the desire to densely sample in time or to record over extended periods, especially in the context of analyzing multiple subcellular components via multiwavelength imaging.Under normal imaging conditions, widefield microscopy combined with image restoration using deconvolution methods provides an excellent modality for multiwavelength 4D imaging as it makes very efficient use of the illuminating photons. However, its effectiveness, in particular its ability to resolve subcellular detail sufficiently in the presence of noise, is limited by the performance of the deconvolution method. Such limitations can seriously degrade image quality at the low signal levels required for unperturbed in vivo imaging. The noise behavior of the deconvolution algorithm is determined by the efficiency of the noise stabilization term, known as the regularization functional. In particular, the functional's ability to discriminate the noise-related high frequencies from weak high frequencies in the signal ultimately determines the final resolution of the deconvolution. Currently used noise-stabilization techniques are largely based on ad hoc form...

The complex environment of biological cells and tissues has motivated development of three-dimensional (3D) imaging in both light and electron microscopies. To this end, one of the primary tools in fluorescence microscopy is that of computational deconvolution. Wide-field fluorescence images are often corrupted by haze due to out-of-focus light, i.e., to cross-talk between different object planes as represented in the 3D image. Using prior understanding of the image formation mechanism, it is possible to suppress the cross-talk and reassign the unfocused light to its proper source post facto. Electron tomography based on tilted projections also exhibits a cross-talk between distant planes due to the discrete angular sampling and limited tilt range. By use of a suitably synthesized 3D point spread function, we show here that deconvolution leads to similar improvements in volume data reconstructed from cryoscanning transmission electron tomography (CSTET), namely a dramatic in-plane noise reduction and improved representation of features in the axial dimension. Contrast enhancement is demonstrated first with colloidal gold particles and then in representative cryotomograms of intact cells. Deconvolution of CSTET data collected from the periphery of an intact nucleus revealed partially condensed, extended structures in interphase chromatin.

Full resolution electron microscopic tomographic (EMT) reconstruction of large-scale tilt series requires significant computing power. The desire to perform multiple cycles of iterative reconstruction and realignment dramatically increases the pressing need to improve reconstruction performance. This has motivated us to develop a distributed multi-GPU (graphics processing unit) system to provide the required computing power for rapid constrained, iterative reconstructions of very large three-dimensional (3D) volumes. The participating GPUs reconstruct segments of the volume in parallel, and subsequently, the segments are assembled to form the complete 3D volume. Owing to its power and versatility, the CUDA (NVIDIA, USA) platform was selected for GPU implementation of the EMT reconstruction. For a system containing 10 GPUs provided by 5 GTX295 cards, 10 cycles of SIRT reconstruction for a tomogram of 40962 × 512 voxels from an input tilt series containing 122 projection images of 40962 pixels (single precision float) takes a total of 1845 seconds of which 1032 seconds are for computation with the remainder being the system overhead. The same system takes only 39 seconds total to reconstruct 10242 × 256 voxels from 122 10242 pixel projections. While the system overhead is non-trivial, performance analysis indicates that adding extra GPUs to the system would lead to steadily enhanced overall performance. Therefore, this system can be easily expanded to generate superior computing power for very large tomographic reconstructions and especially to empower iterative cycles of reconstruction and realignment.