Three different ways of implementing cycloidal computed tomography: a discussion of pros and cons

We present three implementation strategies for cycloidal computed tomography. The latter refers to an imaging concept that enables the acquisition of highresolution tomograms in a flexible manner (e.g. with x-ray sources with a relatively large focal spot and detectors with relatively large pixels). In cycloidal computed tomography, the sample is rotated and laterally translated simultaneously; with this scheme, each sample feature follows a cycloidal trajectory. This has been shown to reduce scanning time and delivered dose, while maintaining a high resolution. The different ways of implementing this method are: step-and-shoot, continuous unidirectional and continuous back-and-forth translation. While step-andshoot acquisitions yields the best results and are easiest to implement, they are also the most time-consuming. The continuous unidirectional method can be implemented with little effort and gives results comparable to step-and-shoot. Finally, back-and-forth scans can be implemented easily and provide similar results, although there appears to be a small loss in image quality. We present a comprehensive guide on using cycloidal sampling in practice

Technical Note: Practical implementation strategies of cycloidal computed tomography

Purpose: Cycloidal computed tomography is a novel imaging concept which combines a highly structured x-ray beam, offset lateral under-sampling, and mathematical data recovery to obtain high-resolution images efficiently and flexibly, even with relatively large source focal spots and detector pixels. The method reduces scanning time and, potentially, delivered dose compared to other sampling schemes. This study aims to present and discuss several implementation strategies for cycloidal computed tomography (CT) in order to increase its ease of use and facilitate uptake within the imaging community. Methods: The different implementation strategies presented are step-and-shoot, continuous unidirectional, continuous back-and-forth, and continuous pixel-wise scanning. In step-and-shoot scans the sample remains stationary while frames are acquired, whereas in all other cases the sample moves through the scanner continuously. The difference between the continuous approaches is the trajectory by which the sample moves within the field of view. Results: All four implementation strategies are compatible with a standard table-top x-ray setup. With the experimental setup applied here, step-and-shoot acquisitions yield the best spatial resolution (around 30 µm), but are the most time-consuming (1.4 h). Continuous unidirectional and back-and-forth images have resolution between 30 and 40 µm, and are faster (35 min). Continuous pixel-wise images are equally time-efficient, although technical challenges caused a small loss in image quality with a resolution of about 50 µm. Conclusion: The authors show that cycloidal CT can be implemented in a variety of ways with high quality results. They believe this posits cycloidal CT as a powerful imaging alternative to more time-consuming and less flexible methods in the field

Rapid and flexible high-resolution scanning enabled by cycloidal computed tomography and convolutional neural network (CNN) based data recovery

We have combined a recently developed imaging concept (“cycloidal computed tomography”) with convolutional neural network (CNN) based data recovery. The imaging concept is enabled by exploiting, in synergy, the benefits of probing the sample with a structured x-ray beam and applying a cycloidal acquisition scheme by which the sample is simultaneously rotated and laterally translated. The beam structuring provides a means of increasing the in-slice spatial resolution in tomographic images irrespective of the blur imposed by the x-ray source and detector, while the “roto-translation” sampling allows for rapid scanning. Data recovery based on the recently proposed Mixed-Scale Dense (MSD) CNN architecture enables an efficient reconstruction of high-quality, high-resolution images despite the fact that cycloidal computed tomography data are highly incomplete. In the following, we review the basic principles underpinning cycloidal computed tomography, introduce the CNN based data recovery method and discuss the benefit of combining both

Cycloidal CT with CNN-based sinogram completion and in-scan generation of training data

In x-ray computed tomography (CT), the achievable image resolution is typically limited by several pre-fixed characteristics of the x-ray source and detector. Structuring the x-ray beam using a mask with alternating opaque and transmitting septa can overcome this limit. However, the use of a mask imposes an undersampling problem: to obtain complete datasets, significant lateral sample stepping is needed in addition to the sample rotation, resulting in high x-ray doses and long acquisition times. Cycloidal CT, an alternative scanning scheme by which the sample is rotated and translated simultaneously, can provide high aperture-driven resolution without sample stepping, resulting in a lower radiation dose and faster scans. However, cycloidal sinograms are incomplete and must be restored before tomographic images can be computed. In this work, we demonstrate that high-quality images can be reconstructed by applying the recently proposed Mixed Scale Dense (MS-D) convolutional neural network (CNN) to this task. We also propose a novel training approach by which training data are acquired as part of each scan, thus removing the need for large sets of pre-existing reference data, the acquisition of which is often not practicable or possible. We present results for both simulated datasets and real-world data, showing that the combination of cycloidal CT and machine learning-based data recovery can lead to accurate high-resolution images at a limited dose

X-ray phase contrast tomography; proof of principle for post-mortem imaging

Objectives: To demonstrate the feasibility of using X-ray phase contrast tomography to assess internal organs in a post-mortem piglet model, as a possible non-invasive imaging autopsy technique. Methods: Tomographic images of a new-born piglet were obtained using a Free Space Propagation (FSP) X-ray phase contrast imaging setup at a synchrotron (European Synchrotron Radiation Facility, Grenoble, France). A monochromatic X-ray beam (52 keV) was used in combination with a detector pixel size of 46x46 μm2. A phase-retrieval algorithm was applied to all projections, which were then reconstructed into tomograms using the filtered-back projection algorithm. Images were assessed for diagnostic quality. Results: Images obtained with the FSP setup presented high soft tissue contrast and sufficient resolution for resolving organ structure. All of the main body organs (heart, lungs, kidneys, liver and intestines) were easily identified and adequately visualised. In addition, grey/white matter differentiation in the cerebellum while still contained within the skull was shown. Conclusions: The feasibility of using X-ray phase contrast tomography as a post-mortem imaging technique in an animal model has been demonstrated. Future studies will focus on translating this experiment to a laboratory-based setup

Cycloidal-spiral sampling for three-modal x-ray CT flyscans with two-dimensional phase sensitivity

We present a flyscan compatible acquisition scheme for three-modal X-Ray Computed Tomography (CT) with two-dimensional phase sensitivity. Our approach is demonstrated using a "beam tracking" setup, through which a sample's attenuation, phase (refraction) and scattering properties can be measured from a single frame, providing three complementary contrast channels. Up to now, such setups required the sample to be stepped at each rotation angle to sample signals at an adequate rate, to prevent resolution losses, anisotropic resolution, and under-sampling artefacts. However, the need for stepping necessitated a step-and-shoot implementation, which is affected by motors' overheads and increases the total scan time. By contrast, our proposed scheme, by which continuous horizontal and vertical translations of the sample are integrated with its rotation (leading to a "cycloidal-spiral" trajectory), is fully compatible with continuous scanning (flyscans). This leads to greatly reduced scan times while largely preserving image quality and isotropic resolution

Insight into the chemoselective aromatic vs. side-chain hydroxylation of alkylaromatics with H2O2 catalyzed by a non-heme imine-based iron complex

The oxidation of a series of alkylaromatic compounds with H2O2 catalyzed by an imine-based non-heme iron complex prepared in situ by reaction of 2-picolylaldehyde, 2-picolylamine, and Fe(OTf)2 in a 2 : 2 : 1 ratio leads to a marked chemoselectivity for aromatic ring hydroxylation over side-chain oxidation. This selectivity is herein investigated in detail. Side-chain/ring oxygenated product ratio was found to increase upon decreasing the bond dissociation energy (BDE) of the benzylic C-H bond in line with expectation. Evidence for competitive reactions leading either to aromatic hydroxylation via electrophilic aromatic substitution or side-chain oxidation via benzylic hydrogen atom abstraction, promoted by a metal-based oxidant, has been provided by kinetic isotope effect analysis. This journal i

Multilevel HfO2-based RRAM devices for low-power neuromorphic networks

Training and recognition with neural networks generally require high throughput, high energy efficiency, and scalable circuits to enable artificial intelligence tasks to be operated at the edge, i.e., in battery-powered portable devices and other limited-energy environments. In this scenario, scalable resistive memories have been proposed as artificial synapses thanks to their scalability, reconfigurability, and high-energy efficiency, and thanks to the ability to perform analog computation by physical laws in hardware. In this work, we study the material, device, and architecture aspects of resistive switching memory (RRAM) devices for implementing a 2-layer neural network for pattern recognition. First, various RRAM processes are screened in view of the device window, analog storage, and reliability. Then, synaptic weights are stored with 5-level precision in a 4 kbit array of RRAM devices to classify the Modified National Institute of Standards and Technology (MNIST) dataset. Finally, classification performance of a 2-layer neural network is tested before and after an annealing experiment by using experimental values of conductance stored into the array, and a simulation-based analysis of inference accuracy for arrays of increasing size is presented. Our work supports material-based development of RRAM synapses for novel neural networks with high accuracy and low-power consumption. (C) 2019 Author(s)

Cycloidal CT with CNN-based sinogram completion and in-scan generation of training data

In x-ray computed tomography (CT), the achievable image resolution is typically limited by several pre-fixed characteristics of the x-ray source and detector. Structuring the x-ray beam using a mask with alternating opaque and transmitting septa can overcome this limit. However, the use of a mask imposes an undersampling problem: to obtain complete datasets, significant lateral sample stepping is needed in addition to the sample rotation, resulting in high x-ray doses and long acquisition times. Cycloidal CT, an alternative scanning scheme by which the sample is rotated and translated simultaneously, can provide high aperture-driven resolution without sample stepping, resulting in a lower radiation dose and faster scans. However, cycloidal sinograms are incomplete and must be restored before tomographic images can be computed. In this work, we demonstrate that high-quality images can be reconstructed by applying the recently proposed Mixed Scale Dense (MS-D) convolutional neural network (CNN) to this task. We also propose a novel training approach by which training data are acquired as part of each scan, thus removing the need for large sets of pre-existing reference data, the acquisition of which is often not practicable or possible. We present results for both simulated datasets and real-world data, showing that the combination of cycloidal CT and machine learning-based data recovery can lead to accurate high-resolution images at a limited dose.Algorithms and the Foundations of Software technolog