X-ray tomography of extended objects: a comparison of data acquisition approaches

The penetration power of x-rays allows one to image large objects. For example, centimeter-sized specimens can be imaged with micron-level resolution using synchrotron sources. In this case, however, the limited beam diameter and detector size preclude the acquisition of the full sample in a single take, necessitating strategies for combining data from multiple regions. Object stitching involves the combination of local tomography data from overlapping regions, while projection stitching involves the collection of projections at multiple offset positions from the rotation axis followed by data merging and reconstruction. We compare these two approaches in terms of radiation dose applied to the specimen, and reconstructed image quality. Object stitching involves an easier data alignment problem, and immediate viewing of subregions before the entire dataset has been acquired. Projection stitching is more dose-efficient, and avoids certain artifacts of local tomography; however, it also involves a more difficult data assembly and alignment procedure, in that it is more sensitive to accumulative registration error

Three dimensions, two microscopes, one code: automatic differentiation for x-ray nanotomography beyond the depth of focus limit

Conventional tomographic reconstruction algorithms assume that one has obtained pure projection images, involving no within-specimen diffraction effects nor multiple scattering. Advances in x-ray nanotomography are leading towards the violation of these assumptions, by combining the high penetration power of x-rays which enables thick specimens to be imaged, with improved spatial resolution which decreases the depth of focus of the imaging system. We describe a reconstruction method where multiple scattering and diffraction effects in thick samples are modeled by multislice propagation, and the 3D object function is retrieved through iterative optimization. We show that the same proposed method works for both full-field microscopy, and for coherent scanning techniques like ptychography. Our implementation utilizes the optimization toolbox and the automatic differentiation capability of the open-source deep learning package TensorFlow, which demonstrates a much straightforward way to solve optimization problems in computational imaging, and endows our program great flexibility and portability

Using Automatic Differentiation as a General Framework for Ptychographic Reconstruction

Coherent diffraction imaging methods enable imaging beyond lens-imposed resolution limits. In these methods, the object can be recovered by minimizing an error metric that quantifies the difference between diffraction patterns as observed, and those calculated from a present guess of the object. Efficient minimization methods require analytical calculation of the derivatives of the error metric, which is not always straightforward. This limits our ability to explore variations of basic imaging approaches. In this paper, we propose to substitute analytical derivative expressions with the automatic differentiation method, whereby we can achieve object reconstruction by specifying only the physics-based experimental forward model. We demonstrate the generality of the proposed method through straightforward object reconstruction for a variety of complex ptychographic experimental models

A matrix-free Levenberg-Marquardt algorithm for efficient ptychographic phase retrieval

The phase retrieval problem, where one aims to recover a complex-valued image from far-field intensity measurements, is a classic problem encountered in a range of imaging applications. Modern phase retrieval approaches usually rely on gradient descent methods in a nonlinear minimization framework. Calculating closed-form gradients for use in these methods is tedious work, and formulating second order derivatives is even more laborious. Additionally, second order techniques often require the storage and inversion of large matrices of partial derivatives, with memory requirements that can be prohibitive for data-rich imaging modalities. We use a reverse-mode automatic differentiation (AD) framework to implement an efficient matrix-free version of the Levenberg-Marquardt (LM) algorithm, a longstanding method that finds popular use in nonlinear least-square minimization problems but which has seen little use in phase retrieval. Furthermore, we extend the basic LM algorithm so that it can be applied for general constrained optimization problems beyond just the least-square applications. Since we use AD, we only need to specify the physics-based forward model for a specific imaging application; the derivative terms are calculated automatically through matrix-vector products, without explicitly forming any large Jacobian or Gauss-Newton matrices. We demonstrate that this algorithm can be used to solve both the unconstrained ptychographic object retrieval problem and the constrained "blind" ptychographic object and probe retrieval problems, under both the Gaussian and Poisson noise models, and that this method outperforms best-in-class first-order ptychographic reconstruction methods: it provides excellent convergence guarantees with (in many cases) a superlinear rate of convergence, all with a computational cost comparable to, or lower than, the tested first-order algorithms

The percentage of <i>Lissachatina fulica</i> that selected various test treatments and controls, or made no choice, in two-choice laboratory bioassays testing emulsions of commercial papaya-flavored oil, synthetic reconstructions of the papaya oil odor in canola oil or mineral oil, or water emulsion controls.

The percentage of Lissachatina fulica that selected various test treatments and controls, or made no choice, in two-choice laboratory bioassays testing emulsions of commercial papaya-flavored oil, synthetic reconstructions of the papaya oil odor in canola oil or mineral oil, or water emulsion controls.</p

The mean number (SE) of insects arthropods found on sticky traps (n = 6) baited with a dry cotton wick (dry wick), a saturated wick with synthetic papaya oil lure in mineral oil emulsion (synthetic papaya lure emulsion) or a wick saturated with mineral oil and water emulsion (water emulsion) collected over 24 h in Miami, Florida, USA.

Emulsions were 1:1:0.2 water/oil/ Tween®80.</p

A new synthetic lure for management of the invasive giant African snail, <i>Lissachatina fulica</i> - Fig 3

The mean number of giant African snails (Lissachatina fulica), semi-slugs (Parmarion martensi), Cuban slug (Veronicella cubensis) and black slugs (Laevicaulis alte) found in 50 cm2 plots (n = 50) 2 h (a) and 12 h (b) after application of liquid metaldehyde alone or liquid metaldehyde combined with 0.2% synthetic lure emulsion.</p

The pooled mean numbers (SE) and types of insects captured (indicated by ‘+’) on sticky cards (n = 8) baited with cotton wicks saturated with water, canola oil emulsion, synthetic papaya oil lure (0.2%) in water or synthetic papaya oil lure (0.2%) in a canola oil emulsion collected over 24 h in Hilo, HI, USA. Sticky card were suspended 1m (elevated) or placed on the ground (ground).

Emulsions were 1:1:0.2 water/oil/ Tween®80.</p

The mean number of giant African snails (<i>Lissachatina fulica</i>) and semi-slugs (<i>Parmarion martensi</i>) found in or near (15 cm) Petri dishes baited with wicks saturated with fresh or 48 h aged synthetic papaya odor lure (0.2%) combined with either mineral or canola oil and fresh liquid metaldehyde emulsion or liquid metaldehyde alone.

Bars represent one standard error of the mean. Emulsions were 1:1:0.2 water/oil/ Tween®80.</p

The total number and size range of giant African snails (<i>Lissachatina</i> fulic) found 25 cm from Petri dishes with a cotton wick saturated with liquid metaldehyde or a wick with 0.2% synthetic papaya lure in a 1:1:0.2 emulsion of water, canola oil, and Tween in a field bioassay at Mt. Lambert, Trinidad (n = 25), or 15 cm from 4 ml ground applications of the same blend and a water emulsion control 12 h after application of liquid metaldehyde in Miami, FL, U.S.A. (n = 110).

The total number and size range of giant African snails (Lissachatina fulic) found 25 cm from Petri dishes with a cotton wick saturated with liquid metaldehyde or a wick with 0.2% synthetic papaya lure in a 1:1:0.2 emulsion of water, canola oil, and Tween in a field bioassay at Mt. Lambert, Trinidad (n = 25), or 15 cm from 4 ml ground applications of the same blend and a water emulsion control 12 h after application of liquid metaldehyde in Miami, FL, U.S.A. (n = 110).</p