In Vitro Validation of an Artefact Suppression Algorithm in X-Ray Phase-Contrast Computed Tomography

<div><p>X-ray phase-contrast tomography can significantly increase the contrast-resolution of conventional attenuation-contrast imaging, especially for soft-tissue structures that have very similar attenuation. Just as in attenuation-based tomography, phase contrast tomography requires a linear dependence of aggregate beam direction on the incremental direction alteration caused by individual voxels along the path of the X-ray beam. Dense objects such as calcifications in biological specimens violate this condition. There are extensive beam deflection artefacts in the vicinity of such structures because they result in large distortion of wave front due to the large difference of refractive index; for such large changes in beam direction, the transmittance of the silicon analyzer crystal saturates and is no longer linearly dependent on the angle of refraction. This paper describes a method by which these effects can be overcome and excellent soft-tissue contrast of phase tomography can be preserved in the vicinity of such artefact-producing structures.</p></div

Synchrotron Radiation Refraction-Contrast Computed Tomography Based on X-ray Dark-Field Imaging Optics of Pulmonary Malignancy: Comparison with Pathologic Examination

Refraction-contrast computed tomography based on X-ray dark-field imaging (XDFI) using synchrotron radiation (SR) has shown superior resolution compared to conventional absorption-based methods and is often comparable to pathologic examination under light microscopy. This study aimed to investigate the potential of the XDFI technique for clinical application in lung cancer diagnosis. Two types of lung specimens, primary and secondary malignancies, were investigated using an XDFI optic system at beamline BL14B of the High-Energy Accelerator Research Organization Photon Factory, Tsukuba, Japan. Three-dimensional reconstruction and segmentation were performed on each specimen. Refraction-contrast computed tomographic images were compared with those obtained from pathological examinations. Pulmonary microstructures including arterioles, venules, bronchioles, alveolar sacs, and interalveolar septa were identified in SR images. Malignant lesions could be distinguished from the borders of normal structures. The lepidic pattern was defined as the invasive component of the same primary lung adenocarcinoma. The SR images of secondary lung adenocarcinomas of colorectal origin were distinct from those of primary lung adenocarcinomas. Refraction-contrast images based on XDFI optics of lung tissues correlated well with those of pathological examinations under light microscopy. This imaging method may have the potential for use in lung cancer diagnosis without tissue damage. Considerable equipment modifications are crucial before implementing them from the lab to the hospital in the near future

Iliac artery specimen in cross-sectional (top) and longitudinal (bottom) cut planes.

<p>Absorption-contrast images (a), phase-contrast images with calcification artefact (b), and phase-contrast image using the proposed artefact removal algorithm (c) are shown.</p

The rocking curve for the forward-diffraction.

<p>The rocking curve for the forward-diffraction.</p

The main intuition behind the artefact reduction algorithm.

<p>Since the missing data is present only at the edges of dense objects, one can use the absorption image to localize the regions of non-linear effects responsible for the artefacts.</p

Improved soft-tissue contrast afforded by X-ray phase images: An iliac artery with extensive atherosclerotic disease demonstrating different plaque components in the phase but not attenuation image a: calcification; b: plastic container; c: fibrous cap; d: tri-laminar arterial wall; e: a plastic rod as fiducial marker; f: soft plaque or atheroma.

<p>Improved soft-tissue contrast afforded by X-ray phase images: An iliac artery with extensive atherosclerotic disease demonstrating different plaque components in the phase but not attenuation image a: calcification; b: plastic container; c: fibrous cap; d: tri-laminar arterial wall; e: a plastic rod as fiducial marker; f: soft plaque or atheroma.</p

Calculation of Stopping-Power Ratio from Multiple CT Numbers Using Photon-Counting CT System: Two- and Three-Parameter-Fitting Method

The two-parameter-fitting method (PFM) is commonly used to calculate the stopping-power ratio (SPR). This study proposes a new formalism: a three-PFM, which can be used in multiple spectral computed tomography (CT). Using a photon-counting CT system, seven rod-shaped samples of aluminium, graphite, and poly(methyl methacrylate) (PMMA), and four types of biological phantom materials were placed in a water-filled sample holder. The X-ray tube voltage and current were set at 150 kV and 40 μμA respectively, and four CT images were obtained at four threshold settings. A semi-empirical correction method that corrects the difference between the CT values from the photon-counting CT images and theoretical values in each spectral region was also introduced. Both the two- and three-PFMs were used to calculate the effective atomic number and electron density from multiple CT numbers. The mean excitation energy was calculated via parameterisation with the effective atomic number, and the SPR was then calculated from the calculated electron density and mean excitation energy. Then, the SPRs from both methods were compared with the theoretical values. To estimate the noise level of the CT numbers obtained from the photon-counting CT, CT numbers, including noise, were simulated to evaluate the robustness of the aforementioned PFMs. For the aluminium and graphite, the maximum relative errors for the SPRs calculated using the two-PFM and three-PFM were 17.1% and 7.1%, respectively. For the PMMA and biological phantom materials, the maximum relative errors for the SPRs calculated using the two-PFM and three-PFM were 5.5% and 2.0%, respectively. It was concluded that the three-PFM, compared with the two-PFM, can yield SPRs that are closer to the theoretical values and is less affected by noise

A flowchart of the main computational steps in the artefact reduction algorithm.

<p>A flowchart of the main computational steps in the artefact reduction algorithm.</p

Comparison between the algorithms proposed in reference [16] and that described in the current paper.

<p>Panel (a): Phase-contrast images reconstructed using the algorithm without any de-noising step, as proposed in reference [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135654#pone.0135654.ref016" target="_blank">16</a>]; Panel (b): reconstructed image using the de-noising procedure proposed in this research. Both panels are zoomed-in detail of the region indicated by a red dashed square in the top image in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135654#pone.0135654.g007" target="_blank">Fig 7(c)</a>.</p