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

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