Quantitative region-of-interest tomography using variable field of view

Abstract

In X-ray computed tomography, the task of imaging only a local region of interest (ROI) inside a larger sample is very important. However, without a priori information, this ROI cannot be exactly reconstructed using only the image data limited to the ROI. We propose here an approach of region-of-interest tomography, which reconstructs a ROI within an object from projections of different fields of view acquired on a specific angular sampling scheme in the same tomographic experiment. We present a stable procedure that not only yields high-quality images of the ROI but keeps as well the quantitative contrast on the reconstructed images. In addition, we analyze the minimum number of projections required for ROI tomography from the point of view of the band region of the Radon transform, which confirms this number must be estimated based on the size of the entire object and not only on the size of the ROI.

Machine learning denoising of high-resolution X-ray nanotomography data

“…The periodic structure of the sample gives rise to frequencies around 0.004 nm À1 . projections needed, and not only the FOV (Silva et al, 2018). Otherwise, aliasing artifacts can arise due to structures outside the FOV that are rotating in and out during the scan, disturbing the high frequencies and reducing the resolution.…”

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

“…Finally, each projection image collected inevitably contains information of the portion object lying outside of the ROI, which, at least to some minor extent, violates the Fourier slice theorem [20]. When the truncation ratio is not too low, one can use this excessive information to slightly expand the field-of-view by padding both sides of the sinogram with its edge values; however, streak artifacts will be heavily present in the area out of the scanned disk in the case of a small truncation ratio [13]. In addition, ideally, one would also seek to satisfy the Crowther criterion [21] on the required number of rotation angles based on the entire object size rather than the size of the local tomography region of interest.…”

“…For example, in LTA one can begin to reconstruct regions of the object immediately after collection of its local tomography data, whereas in SOA one must wait for the collection of all "ring in a cylinder" data before obtaining a full volume reconstruction. One study of LTA [12] indicated that the method contains inherent complicating factors that can affect image quality, while another study [13] has shown that the tomographic reconstruction of a local region can be improved by using a multiscale acquisition approach including lower resolution views of the entire specimen (this is not straightforward when the specimen is larger than the illuminating beam). However, we are not aware of detailed comparisons of LTA and SOA with regards to radiation dose efficiency as well as reconstruction quality.…”

Alignment methods for nanotomography with deep subpixel accuracy

“…The cross-correlation-based methods suffer from accumulation of subpixel errors resulting in poor sensitivity to misalignment between angularly distanced projections. The common-line methods such as the center-of-mass method [28,29] for estimation of the horizontal shift and the vertical-mass-fluctuation (VMF) method for estimation [28,29] of the vertical shift are limited to the parallel-beam tomography with conventional geometry and cannot be used for more general 3D imaging geometries such as cone-beam tomography, laminography or interior tomography [20,32,33]. Finally, the projection matching alignment (PMA) methods [18][19][20][21][22][23][24][25][26] provide a very general way of projection alignment.…”