Showing papers on "Contrast transfer function published in 2021"

Flexible STEM with Simultaneous Phase and Depth Contrast.

Abstract: Recent advances in scanning transmission electron microscopy (STEM) have rekindled interest in multi-channel detectors and prompted the exploration of unconventional scan patterns. These emerging needs are not yet addressed by standard commercial hardware. The system described here incorporates a flexible scan generator that enables exploration of low-acceleration scan patterns, while data are recorded by a scalable eight-channel array of nonmultiplexed analog-to-digital converters. System integration with SerialEM provides a flexible route for automated acquisition protocols including tomography. Using a solid-state quadrant detector with additional annular rings, we explore the generation and detection of various STEM contrast modes. Through-focus bright-field scans relate to phase contrast, similarly to wide-field TEM. More strikingly, comparing images acquired from different off-axis detector elements reveals lateral shifts dependent on defocus. Compensation of this parallax effect leads to decomposition of integrated differential phase contrast (iDPC) to separable contributions relating to projected electric potential and to defocus. Thus, a single scan provides both a computationally refocused phase contrast image and a second image in which the signed intensity, bright or dark, represents the degree of defocus.

Differential programming enabled functional imaging with Lorentz transmission electron microscopy

Abstract: Lorentz transmission electron microscopy is an advanced characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials. Information such as magnetization and electric potentials is carried by the phase of the electron wave, and is lost during image acquisition. Various methods have been proposed to retrieve the phase of the electron wavefunction using intensities of the acquired images, most of which work only in the small defocus limit. Imaging at strong defoci not only carries more quantitative phase information, but is essential to the study of weak magnetic and electrostatic fields at the nanoscale. In this work we develop a method based on differentiable programming to solve the inverse problem of phase retrieval. We show that our method maintains a high spatial resolution and robustness against noise even at the upper defocus limit of the microscope. More importantly, our proposed method can go beyond recovering just the phase information. We demonstrate this by retrieving the electron-optical parameters of the contrast transfer function alongside the electron exit wavefunction.

Neural network model assisted Fourier ptychography with Zernike aberration recovery and total variation constraint.

Abstract: Significance: Fourier ptychography (FP) is a computational imaging approach that achieves high-resolution reconstruction. Inspired by neural networks, many deep-learning-based methods are proposed to solve FP problems. However, the performance of FP still suffers from optical aberration, which needs to be considered. Aim: We present a neural network model for FP reconstructions that can make proper estimation toward aberration and achieve artifact-free reconstruction. Approach: Inspired by the iterative reconstruction of FP, we design a neural network model that mimics the forward imaging process of FP via TensorFlow. The sample and aberration are considered as learnable weights and optimized through back-propagation. Especially, we employ the Zernike terms instead of aberration to decrease the optimization freedom of pupil recovery and perform a high-accuracy estimation. Owing to the auto-differentiation capabilities of the neural network, we additionally utilize total variation regularization to improve the visual quality. Results: We validate the performance of the reported method via both simulation and experiment. Our method exhibits higher robustness against sophisticated optical aberrations and achieves better image quality by reducing artifacts. Conclusions: The forward neural network model can jointly recover the high-resolution sample and optical aberration in iterative FP reconstruction. We hope our method that can provide a neural-network perspective to solve iterative-based coherent or incoherent imaging problems.

Model-based Reconstruction for Single Particle Cryo-Electron Microscopy

Abstract: Single particle cryo-electron microscopy is a vital tool for 3D characterization of protein structures. A typical workflow involves acquiring projection images of a collection of randomly oriented particles, picking and classifying individual particle projections by orientation, and finally using the individual particle projections to reconstruct a 3D map of the electron density profile. The reconstruction is challenging because of the low signal-to-noise ratio of the data, the unknown orientation of the particles, and the sparsity of data especially when dealing with flexible proteins where there may not be sufficient data corresponding to each class to obtain an accurate reconstruction using standard algorithms. In this paper we present a model-based image reconstruction technique that uses a regularized cost function to reconstruct the 3D density map by assuming known orientations for the particles. Our method casts the reconstruction as minimizing a cost function involving a novel forward model term that accounts for the contrast transfer function of the microscope, the orientation of the particles and the center of rotation offsets. We combine the forward model term with a regularizer that enforces desirable properties in the volume to be reconstructed. Using simulated data, we demonstrate how our method can significantly improve upon the typically used approach.

High resolution phase imaging with transport of intensity equation and sinusoidal illumination

Abstract: Transport of Intensity equation(TIE) is a non-interferometric method used for quantitative phase imaging. By reformulating the TIE using Contrast Transfer Function, it can be determined that the spatial resolution of the phase retrieved using TIE is limited by the product of imaging system transfer function and a sinc function. In this work, we apply the principles of structured illumination fluorescent microscopy to develop a TIE based super resolved phase imaging technique. The sinusoidal intensity pattern down modulates high frequency spectrum of the phase into the system pass band thereby providing a convenient approach to synthetically enlarge the numerical aperture of the system. Resolution enhancement by two folds is demonstrated using simulations.

Transport of intensity phase imaging with Wiener deconvolution for enhanced visibility

Abstract: Transport of Intensity Equation (TIE) is a powerful computational tool for quantitative phase imaging using intensity only measurement. However, one drawback of TIE is that it does not include any parameters of the imaging system in the equation. To account for the effect of the imaging system on the retrieved phase, TIE is reformulated using Contrast Transfer Function (CTF) to analytically derive the distortion functions present in TIE. The distortion function attenuates the frequency components in the pass band resulting in a blurry phase image. For image restoration, signal parameters are estimated by minimizing a cost function for power spectrum and an optimal wiener filter is employed to deconvolve the distortion function. The proposed method is experimentally demonstrated through a visible enhancement in the phase images of human cheek cells obtained using a bright field microscope.

Contrast transfer function with sinusoidal illumination for super resolved phase imaging

Abstract: Quantitative phase imaging (QPI) has emerged as a powerful computational tool that enables imaging unla- belled specimens with high contrast. It finds applications in microscopy, refractive index mapping , biomedical imaging and surface measurement. Several techniques including interferometry, holography, iterative methods and Transport of Intensity Equation have been developed over the years for QPI. However, the spatial resolution of the retrieved phase images are limited by the diffraction limit of the imaging system. Prior work on Super resolution phase imaging has been primarily focused on holography based techniques which require illumination sources with high coherence , phase unwrapping and high experimental stability. In this work, we propose a propagation based super resolution phase imaging technique using Contrast Transfer Function(CTF) and structured illumination. An enhancement in resolution by two folds is demonstrated using numerical results.