Adaptive surface geometry determination in multi-material x-ray computed tomography using fringe projection

2021 ◽  
Author(s):  
Jonas Großeheide ◽  
Kilian Geiger ◽  
Ânderson Schmidt ◽  
Calvin Bütow ◽  
Benjamin Montavon ◽  
...  
Keyword(s):  
Measurement Data ◽  
Internal Surface ◽  
Detection Algorithm ◽  
Fringe Projection ◽  
X Rays ◽  
Surface Geometry ◽  
Data Set ◽  
X Ray ◽  
Starting Point ◽  
X Ray Computed

Abstract One of the main challenges during digital post-processing of x-ray computed tomographic (XCT) measurement data is the reconstruction of the surface geometry of the measured objects. Conventionally, the surface geometry is defined as an isosurface of identical greyscale values, i.e. the x-ray absorbance of the material, based on a linear interpolation between neighboring voxels. Due to the complex surface geometry and rough surface, XCT measurements of additively manufactured (AM) parts are particularly prone to measurement artefacts caused by various physical effects when the x-rays penetrate the material. The irregular greyscale values at the measured surface geometry render commonly used single threshold greyscale value based isosurfaces as insufficient for representing the external and internal surface of the measured objects. This issue becomes particularly apparent when measuring multi-material objects, such as additively manufactured objects with integrated RFID tags. To address this challenge, this study presents a methodology for reliable surface geometry determination of XCT data based on previously acquired fringe projection (FP) data. For this purpose, the conventionally acquired surfaces geometries from the XCT and FP measurements are extracted, pre-processed and registered to each other before being merged into a single mesh. This merged data set is subsequently used as a starting point or reference for a locally adaptive threshold surface detection algorithm, which is able to capture the surface geometry at a sub-voxel resolution. In order to validate the methodology and confirm the envisaged benefits, selected geometry elements of the resulting surface geometry from measurements samples manufactured by additive manufacturing with integrated RIFD tags are compared to coordinate measurement machine (CMM) reference measurements. The results indicate a more robust surface geometry detection against artifacts especially for multi-material applications.

scholarly journals Application of an edge detection algorithm for surface determination in industrial X-ray computed tomography

2022 ◽  
Author(s):  
Matthias Busch ◽  
Tino Hausotte
Keyword(s):  
Computed Tomography ◽  
Edge Detection ◽  
Detection Algorithm ◽  
Measurement Process ◽  
Initial Contour ◽  
Process Step ◽  
Edge Detection Algorithm ◽  
X Ray ◽  
Starting Point ◽  
X Ray Computed

AbstractSurface determination is an essential step of the measurement process in industrial X-ray computed tomography (XCT). The starting point of the surface determination process step is a single grey value threshold within a voxel volume in conventional surface determination methods. However, this value is not always found in the reconstructed volume in the local environment of the surface of the measurement object due to various artefacts, so that none or incorrect surfaces are determined. In order to find surfaces independently of a single grey value, a three-dimensional approach of the initial contour determination based on a Prewitt edge detection algorithm is presented in this work. This method is applied to different test specimens and specimen compositions which, due to their material or material constellation, their geometric properties with regard to surfaces and interfaces as well as their calibrated size and length dimensions, embody relevant properties in the examination of joining connections. It is shown that by using the surface determination method in the measurement process, both a higher metrological structure resolution and interface structure resolution can be achieved. Surface artefacts can be reduced by the application and it is also an approach to improved surface finding for the multi-material components that are challenging for XCT.

Visualizing Fluid Flows With X-Rays

2007 ◽  
Author(s):  
Theodore J. Heindel ◽  
Terrence C. Jensen ◽  
Joseph N. Gray
Keyword(s):  
Computed Tomography ◽  
Flow Visualization ◽  
Three Dimensional ◽  
Fluid Flows ◽  
X Rays ◽  
Radiographic Images ◽  
Optical Access ◽  
X Ray ◽  
X Ray Computed ◽  
Density Map

There are several methods available to visualize fluid flows when one has optical access. However, when optical access is limited to near the boundaries or not available at all, alternative visualization methods are required. This paper will describe flow visualization using an X-ray system that is capable of digital X-ray radiography, digital X-ray stereography, and digital X-ray computed tomography (CT). The unique X-ray flow visualization facility will be briefly described, and then flow visualization of various systems will be shown. Radiographs provide a two-dimensional density map of a three dimensional process or object. Radiographic images of various multiphase flows will be presented. When two X-ray sources and detectors simultaneously acquire images of the same process or object from different orientations, stereographic imaging can be completed; this type of imaging will be demonstrated by trickling water through packed columns and by absorbing water in a porous medium. Finally, local time-averaged phase distributions can be determined from X-ray computed tomography (CT) imaging, and this will be shown by comparing CT images from two different gas-liquid sparged columns.

scholarly journals Synchrotron X-ray induced acoustic imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seongwook Choi ◽  
Eun-Yeong Park ◽  
Sinyoung Park ◽  
Jong Hyun Kim ◽  
Chulhong Kim
Keyword(s):  
Acoustic Imaging ◽  
Signal Frequency ◽  
X Rays ◽  
Repetition Period ◽  
Linear Accelerators ◽  
X Ray ◽  
X Ray Computed ◽  
Medical Linear Accelerators ◽  
Ionizing Radiation Exposure ◽  
X Ray Absorption

AbstractX-ray induced acoustic imaging (XAI) is an emerging biomedical imaging technique that can visualize X-ray absorption contrast at ultrasound resolution with less ionizing radiation exposure than conventional X-ray computed tomography. So far, medical linear accelerators or industrial portable X-ray tubes have been explored as X-ray excitation sources for XAI. Here, we demonstrate the first feasible synchrotron XAI (sXAI). The synchrotron generates X-rays, with a dominant energy of 4 to 30 keV, a pulse-width of 30 ps, a pulse-repetition period of 2 ns, and a bunch-repetition period of 940 ns. The X-ray induced acoustic (XA) signals are processed in the Fourier domain by matching the signal frequency with the bunch-repetition frequency. We successfully obtained two-dimensional XA images of various lead targets. This novel sXAI tool could complement conventional synchrotron applications.

Automatic Knot Detection in Coarse-Resolution Cone-Beam Computed Tomography Images of Softwood Logs

2019 ◽  
Vol 69 (3) ◽  
pp. 185-187
Author(s):  
Magnus Fredriksson ◽  
Julie Cool ◽  
Stavros Avramidis
Keyword(s):  
Computed Tomography ◽  
Exploratory Study ◽  
Ct Scanning ◽  
Detection Algorithm ◽  
Cone Beam ◽  
X Ray ◽  
Coarse Resolution ◽  
Computed Tomography Images ◽  
X Ray Computed ◽  
Accurate Position

Abstract X-ray computed tomography (CT) scanning of sawmill logs is associated with costly and complex machines. An alternative scanning solution was developed, but its data have not been evaluated regarding detection of internal features. In this exploratory study, a knot detection algorithm was applied to images of four logs to evaluate its performance in terms of knot position and size. The results were a detection rate of 67 percent, accurate position, and inaccurate size. Although the sample size was small, it was concluded that automatic knot detection in coarse resolution CT images of softwoods is feasible, albeit for knots of sufficient size.

scholarly journals Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

2020 ◽  
Author(s):  
Lawrence Hall ◽  
Dmitry Goldgof ◽  
Rahul Paul ◽  
Gregory M. Goldgof
Keyword(s):  
False Negative ◽  
True Positive Rate ◽  
Small Data ◽  
Good Resolution ◽  
X Rays ◽  
True Positive ◽  
Data Set ◽  
X Ray ◽  
Negative Rate ◽  
Positive Rate

<p>Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 135 chest X-rays of COVID-19 and 320 chest X-rays of viral and bacterial pneumonia. </p><p> A pre-trained deep convolutional neural network, Resnet50 was tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were </p><p> an overall accuracy of 89.2% with a COVID-19 true positive rate of 0.8039 and an AUC of 0.95. Pre-trained Resnet50 and VGG16 plus our own small CNN were tuned or trained on a balanced set of COVID-19 and pneumonia chest X-rays. An ensemble of the three types of CNN classifiers was applied to a test set of 33 unseen COVID-19 and 218 pneumonia cases. The overall accuracy was 91.24% with the true positive rate for COVID-19 of 0.7879 with 6.88% false positives for a true negative rate of 0.9312 and AUC of 0.94. </p><p> This preliminary study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images at good resolution will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19.</p>

159 The Need Of Six Monthly Follow Up X-Rays Post Total Knee Replacement(TKR)

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
N Baig ◽  
M Ferrari ◽  
A Lukaszewicz
Keyword(s):  
Total Knee Replacement ◽  
Knee Replacement ◽  
Trauma Centre ◽  
X Rays ◽  
Data Set ◽  
X Ray ◽  
Major Trauma Centre ◽  
Plain Films ◽  
Total Knee

Abstract Background There is a longstanding culture of repeat x-rays after total knee replacement (TKR) as part of follow up, often combined with a clinic review. This is to check that the prosthesis is in a satisfactory position. There are inherently a number of issues with this historic approach including exposure of patients to further radiation who may be asymptomatic, time delays in busy clinics or x-ray departments and costs. Objectives The aim of this audit was to assess whether follow up plain films after TKR are methodically undertaken and of benefit to confirm satisfactory appearance if immediate post -operative x-rays were unremarkable. The findings of a six month follow up x-ray was specifically evaluated. The secondary aim was to establish the timing of further follow up x-rays within the department. Method 200 patients were included within the analysis, they all received a TKR at a major trauma centre, over a one-year period between December 2017 and December 2018. Results It was found that 100% of those patients having a post-operative film had a satisfactory appearance. 78% of patients had at least one further follow op x-ray of which 99.4% were satisfactory. Up to five follow up x-rays were taken with 53.5% of patients having a follow up x-ray at 6 months. Conclusions From the above results there is minimal, if any, evidence within the data set to support routine, additional follow up imaging if initial post-operative films are satisfactory, and the patient is asymptomatic.

scholarly journals Optimization of the energy for Breast monochromatic absorption X-ray Computed Tomography

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Pasquale Delogu ◽  
Vittorio Di Trapani ◽  
Luca Brombal ◽  
Giovanni Mettivier ◽  
Angelo Taibi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Imaging Techniques ◽  
Fixed Dose ◽  
X Rays ◽  
Reconstruction Algorithms ◽  
Contrast Resolution ◽  
X Ray ◽  
X Ray Imaging ◽  
X Ray Computed ◽  
Dose Levels

Abstract The limits of mammography have led to an increasing interest on possible alternatives such as the breast Computed Tomography (bCT). The common goal of all X-ray imaging techniques is to achieve the optimal contrast resolution, measured through the Contrast to Noise Ratio (CNR), while minimizing the radiological risks, quantified by the dose. Both dose and CNR depend on the energy and the intensity of the X-rays employed for the specific imaging technique. Some attempts to determine an optimal energy for bCT have suggested the range 22 keV–34 keV, some others instead suggested the range 50 keV–60 keV depending on the parameters considered in the study. Recent experimental works, based on the use of monochromatic radiation and breast specimens, show that energies around 32 keV give better image quality respect to setups based on higher energies. In this paper we report a systematic study aiming at defining the range of energies that maximizes the CNR at fixed dose in bCT. The study evaluates several compositions and diameters of the breast and includes various reconstruction algorithms as well as different dose levels. The results show that a good compromise between CNR and dose is obtained using energies around 28 keV.

scholarly journals K-edge Subtraction Computed Tomography with a Compact Synchrotron X-ray Source

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Stephanie Kulpe ◽  
Martin Dierolf ◽  
Benedikt Günther ◽  
Madleen Busse ◽  
Klaus Achterhold ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Contrast Agent ◽  
Imaging Techniques ◽  
X Rays ◽  
Synchrotron Source ◽  
X Ray ◽  
Inverse Compton Scattering ◽  
Inverse Compton ◽  
X Ray Computed ◽  
Compact Light Source

Abstract In clinical diagnosis, X-ray computed tomography (CT) is one of the most important imaging techniques. Yet, this method lacks the ability to differentiate similarly absorbing substances like commonly used iodine contrast agent and calcium which is typically seen in calcifications, kidney stones and bones. K-edge subtraction (KES) imaging can help distinguish these materials by subtracting two CT scans recorded at different X-ray energies. So far, this method mostly relies on monochromatic X-rays produced at large synchrotron facilities. Here, we present the first proof-of-principle experiment of a filter-based KES CT method performed at a compact synchrotron X-ray source based on inverse-Compton scattering, the Munich Compact Light Source (MuCLS). It is shown that iodine contrast agent and calcium can be clearly separated to provide CT volumes only showing one of the two materials. These results demonstrate that KES CT at a compact synchrotron source can become an important tool in pre-clinical research.

Dimensional Quantification of Embedded Voids or Objects in Three Dimensions Using X-Ray Tomography

2012 ◽  
Vol 18 (2) ◽  
pp. 390-398 ◽  
Author(s):  
Brian M. Patterson ◽  
Juan P. Escobedo-Diaz ◽  
Darcie Dennis-Koller ◽  
Ellen Cerreta
Keyword(s):  
Digital Imaging ◽  
Three Dimensional ◽  
Three Dimensions ◽  
Post Processing ◽  
X Ray ◽  
Individual Object ◽  
Starting Point ◽  
X Ray Computed ◽  
The Individual ◽  
Processing Steps

AbstractScientific digital imaging in three dimensions such as when using X-ray computed tomography offers a variety of ways to obtain, filter, and quantify data that can produce vastly different results. These opportunities, performed during image acquisition or during the data processing, can include filtering, cropping, and setting thresholds. Quantifying features in these images can be greatly affected by how the above operations are performed. For example, during binarization, setting the threshold too low or too high can change the number of objects as well as their measured diameter. Here, two facets of three-dimensional quantification are explored. The first will focus on investigating the question of how many voxels are needed within an object to have accurate geometric statistics that are due to the properties of the object and not an artifact of too few voxels. These statistics include but are not limited to percent of total volume, volume of the individual object, Feret shape, and surface area. Using simple cylinders as a starting point, various techniques for smoothing, filtering, and other processing steps can be investigated to aid in determining if they are appropriate for a specific desired statistic for a real dataset. The second area of investigation is the influence of post-processing, particularly segmentation, on measuring the damage statistics in high purity Cu. The most important parts of the pathways of processing are highlighted.

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