Accurate Resolution Measurement for X‐Ray Micro‐CT Systems

Sharma, Kriti Sen; Seshadri, Srivatsan; Feser, Michael; Wang, G.

doi:10.1063/1.3625373

Cited by 5 publications

(8 citation statements)

References 5 publications

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“…Next, ~ 300 μ m wide strips were diced off to create structures that fit within FOV of a high resolution micro-CT (Sen Sharma et al 2013). These structures are marked by white arrows in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…In high-resolution micro-CT, the most commonly used phantoms are thin test patterns fabricated by semiconductor fabrication techniques, but they allow system characterization in the projection domain only (Sen Sharma et al 2011). 3D micro-CT phantoms allowing quantification in reconstructed slices (Mizutani et al 2010) have been difficult to fabricate, or are not yet readily available (Sen Sharma et al 2013). Most importantly, previously reported micro-CT phantoms were not suitable for characterizing interior reconstruction, and thus, new phantoms were fabricated for this study.…”

Section: Introductionmentioning

confidence: 99%

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Scout-view assisted interior micro-CT

Sharma

Holzner²,

Vasilescu

et al. 2013

Phys. Med. Biol.

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Micro computed tomography (micro-CT) is a widely-used imaging technique. A challenge of micro-CT is to quantitatively reconstruct a sample larger than the field-of-view (FOV) of the detector. This scenario is characterized by truncated projections and associated image artifacts. However, for such truncated scans, a low resolution scout scan with an increased FOV is frequently acquired so as to position the sample properly. This study shows that the otherwise discarded scout scans can provide sufficient additional information to uniquely and stably reconstruct the interior region of interest. Two interior reconstruction methods are designed to utilize the multi-resolution data without a significant computational overhead. While most previous studies used numerically truncated global projections as interior data, this study uses truly hybrid scans where global and interior scans were carried out at different resolutions. Additionally, owing to the lack of standard interior micro-CT phantoms, we designed and fabricated novel interior micro-CT phantoms for this study to provide means of validation for our algorithms. Finally, two characteristic samples from separate studies were scanned to show the effect of our reconstructions. The presented methods show significant improvements over existing reconstruction algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scout-view assisted interior micro-CT

Sharma

Holzner²,

Vasilescu

et al. 2013

Phys. Med. Biol.

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show abstract

“…Since W sx and W sy are approximately the same, we can conclude that the source PSF is approximately circular in shape. Using the method in [21], the manufacturer of the X-ray system estimated the source PSF to have a FWHM of 3.63µm using calibration data at a SOD of 10mm and ODD of 30mm. For this case, the ratio ODD/SOD lies in between the data acquisition parameters of the second and third rows in Table I.…”

Section: A Blur Psf Parameter Estimationmentioning

confidence: 99%

“…Data-driven approaches to blur estimation relies on calculation of the PSF from radiographs of an object with known composition and shape such as a rollbar, slit, pinhole, or other test objects [17]- [21]. However, these methods only estimate one PSF that either models the X-ray source blur, the detector blur, or the total effective blur.…”

Section: Introductionmentioning

confidence: 99%

SABER: A Systems Approach to Blur Estimation and Reduction in X-Ray Imaging

Mohan

Panas

Cuadra

2020

IEEE Trans. on Image Process.

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Blur in X-ray radiographs not only reduces the sharpness of image edges but also reduces the overall contrast. The effective blur in a radiograph is the combined effect of blur from multiple sources such as the detector panel, X-ray source spot, and system motion. In this paper, we use a systems approach to model the point spread function (PSF) of the effective radiographic blur as the convolution of multiple PSFs, where each PSF models one of the various sources of blur. In particular, we model the combined contribution of X-ray source and detector blurs while assuming negligible contribution from other forms of blur. Then, we present a numerical optimization algorithm for estimating the source and detector PSFs from multiple radiographs acquired at different X-ray source to object (SOD) and object to detector distances (ODD). Finally, we computationally reduce blur in radiographs using deblurring algorithms that use the estimated PSFs from the previous step. Our approach to estimate and reduce blur is called SABER, which is an acronym for systems approach to blur estimation and reduction.

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“…The regularization function in (21) enforces smoothness in T k (i, j) by penalizing the difference in values between neighboring pixels with a 1.2-norm penalty function [39], [40]. The optimization problem in ( 21) is solved using the L-BFGS-B algorithm [35].…”

Section: B Regularized Least Squares Deconvolution (Rlsd)mentioning

confidence: 99%

SABER: A Systems Approach to Blur Estimation and Reduction in X-ray Imaging

Mohan,

Panas,

Cuadra

2019

Preprint

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Blur in X-ray radiographs not only reduces the sharpness of image edges but also reduces the overall contrast. The effective blur in a radiograph is the combined effect of blur from multiple sources such as the detector panel, X-ray source spot, and system motion. In this paper, we use a systems approach to model the point spread function (PSF) of the effective radiographic blur as the convolution of multiple PSFs, where each PSF models one of the various sources of blur. Then, we present a numerical optimization algorithm for estimating each PSF from multiple radiographs acquired at different X-ray source to object (SOD) and object to detector distances (ODD). Finally, we computationally reduce blur in radiographs using deblurring algorithms that use the estimated PSFs from the previous step. Our approach to estimate and reduce blur is called SABER, which is an acronym for systems approach to blur estimation and reduction.

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Accurate Resolution Measurement for X‐Ray Micro‐CT Systems

Cited by 5 publications

References 5 publications

Scout-view assisted interior micro-CT

Scout-view assisted interior micro-CT

SABER: A Systems Approach to Blur Estimation and Reduction in X-Ray Imaging

SABER: A Systems Approach to Blur Estimation and Reduction in X-ray Imaging

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