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

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

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

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

Predicting the noise in hybrid (phase and attenuation) x-ray images acquired with the edge illumination technique

PURPOSE: To analyse the noise performance of the edge illumination phase-based x-ray imaging technique when applying "single-shot" phase retrieval. The latter consists in applying a sample-specific low-pass filter to the raw data, leading to "hybrid" images in which phase and attenuation contrast are merged with each other. A second objective is to compare the hybrid images with attenuation-only images based on their respective signal-to-noise ratio (SNR). METHODS: Noise is propagated from the raw images into the retrieved hybrid images, yielding analytic expressions for the variances and noise power spectra of the latter. An expression for the relative SNR between hybrid and attenuation images is derived. A comparison with simulated data is performed. Experimental data are also shown and discussed in the context of the theory. RESULTS: The noise transfer into the retrieved hybrid images is strongly related to the setup and acquisition parameters, as well as the imaged sample itself. Consequently, the relative merit between hybrid and attenuation images also depends on these criteria. Generally, the hybrid approach tends to perform worse for highly attenuating samples, as the availability of phase contrast is outweighed by the loss of photons that is necessarily encountered in hybrid acquisitions. On the contrary, the hybrid approach can lead to a much better SNR for weakly attenuating samples, as here phase effects lead to much stronger contrast, outweighing the reduction in photon numbers. CONCLUSIONS: The analytic expressions inform the design of edge illumination setups that lead to minimum noise transfer into the retrieved hybrid images. We also anticipate our theory to guide the decision as to which imaging mode (hybrid or attenuation) to use in order to to maximise SNR for a specific sample