scholarly journals Computed tomographic beam-hardening artefacts: mathematical characterization and analysis

2015 ◽  
Vol 373 (2043) ◽  
pp. 20140388 ◽  
Author(s):  
Hyoung Suk Park ◽  
Yong Eun Chung ◽  
Jin Keun Seo
Keyword(s):  
Radon Transform ◽  
Mathematical Analysis ◽  
Reconstruction Algorithm ◽  
Ct Reconstruction ◽  
Photon Beams ◽  
Beam Hardening ◽  
X Ray ◽  
Computed Tomographic ◽  
Metal Artefacts ◽  
Metal Artefact

This paper presents a mathematical characterization and analysis of beam-hardening artefacts in X-ray computed tomography (CT). In the field of dental and medical radiography, metal artefact reduction in CT is becoming increasingly important as artificial prostheses and metallic implants become more widespread in ageing populations. Metal artefacts are mainly caused by the beam-hardening of polychromatic X-ray photon beams, which causes mismatch between the actual sinogram data and the data model being the Radon transform of the unknown attenuation distribution in the CT reconstruction algorithm. We investigate the beam-hardening factor through a mathematical analysis of the discrepancy between the data and the Radon transform of the attenuation distribution at a fixed energy level. Separation of cupping artefacts from beam-hardening artefacts allows causes and effects of streaking artefacts to be analysed. Various computer simulations and experiments are performed to support our mathematical analysis.

scholarly journals Beam Hardening Artifact Reduction in X-Ray CT Reconstruction of 3D Printed Metal Parts Leveraging Deep Learning and CAD Models

2020 ◽  
Author(s):  
Amirkoushyar Ziabari ◽  
Singanallur Venkatakrishnan ◽  
Michael Kirka ◽  
Paul Brackman ◽  
Ryan Dehoff ◽  
...  
Keyword(s):  
Computer Aided Design ◽  
Critical Role ◽  
Simulated Data ◽  
Data Sets ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Beam Hardening ◽  
X Ray ◽  
Cad Models ◽  
Aided Design

Abstract Nondestructive evaluation (NDE) of additively manufactured (AM) parts is important for understanding the impacts of various process parameters and qualifying the built part. X-ray computed tomography (XCT) has played a critical role in rapid NDE and characterization of AM parts. However, XCT of metal AM parts can be challenging because of artifacts produced by standard reconstruction algorithms as a result of a confounding effect called “beam hardening.” Beam hardening artifacts complicate the analysis of XCT images and adversely impact the process of detecting defects, such as pores and cracks, which is key to ensuring the quality of the parts being printed. In this work, we propose a novel framework based on using available computer-aided design (CAD) models for parts to be manufactured, accurate XCT simulations, and a deep-neural network to produce high-quality XCT reconstructions from data that are affected by noise and beam hardening. Using extensive experiments with simulated data sets, we demonstrate that our method can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art. We also present promising preliminary results of applying the deep networks trained using CAD models to experimental data obtained from XCT of an AM jet-engine turbine blade.

scholarly journals Impact of metal implants on xSPECT/CT Bone reconstruction: the “shining metal artefact”

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Thiago V. M. Lima ◽  
Ujwal Bhure ◽  
Maria de Sol Pérez Lago ◽  
Yannick Thali ◽  
Savo Matijasevic ◽  
...  
Keyword(s):  
Image Quality ◽  
New Technology ◽  
Reconstruction Algorithm ◽  
Tracer Uptake ◽  
Bone Reconstruction ◽  
Reconstruction Algorithms ◽  
Metal Implants ◽  
Metal Artefacts ◽  
Metal Artefact ◽  
Xspect Bone

Abstract Background Novel reconstruction algorithms, such as xSPECT Bone, are gaining more and more importance in Nuclear Medicine. With xSPECT Bone, the reconstructed emission image is enhanced by the information obtained in the corresponding CT image. The CT defines tissue classes according to the Hounsfield units. In the iterative reconstruction, each tissue class is handled separately in the forward projection step, and all together in the back projection step. As a consequence, xSPECT Bone reconstruction generates images with improved boundary delineation and better anatomic representation of tracer activity. Applying this technique, however, showed that artefacts may occur, when no uptake regions, like metal implants, exhibit fictitious uniform tracer uptake. Due to limitations in spatial resolution in gamma cameras, the xSPECT Bone reconstructed image resulted in spill-out activity from surrounding high uptake region being uniformly distributed over the metal implants. This new technology of xSPECT Bone reconstruction in general enhances the image quality of SPECT/CT; however, the potential introduction of specific artefacts which inadvertently come along with this new technology and their frequency have not yet been addressed in the literature. Therefore, the purpose of this work was to identify and characterize these specific metal artefacts (the so-called shining metal artefact) in order to reduce false positives and avoid potentially misdiagnosing loosened or infected implants. Case presentation In this work, we report five cases imaged with bone SPECT/CT of 5 anatomical regions (foot, elbow, spine, shoulder, ribs and knee). All cases demonstrated “shining metal artefacts” in xSPECT Bone reconstruction. Conclusion While xSPECT Bone reconstruction algorithm significantly improves image quality for the diagnosis of bone and joint disorders with SPECT/CT, specific “shining metal artefacts” caused by the xSPECT Bone have to be recognized in order to avoid image misinterpretation suggesting metallic implant loosening or possible infection. The simultaneous analysis of conventionally reconstructed SPECT images (for Siemens the Flash3D reconstruction) helps to avoid misinterpretation of potential artefacts introduced by xSPECT Bone reconstruction.

scholarly journals Implementation of a Framelet-Based Spectral Reconstruction for Multi-Slice Spiral CT

2021 ◽  
Vol 9 ◽  
Author(s):  
Xin Li ◽  
Yanbo Zhang ◽  
Shuwei Mao ◽  
Jiehua Zhu ◽  
Yangbo Ye
Keyword(s):  
Color Image ◽  
Reconstruction Algorithm ◽  
Computational Time ◽  
Spectral Information ◽  
Ct Reconstruction ◽  
Spectral Ct ◽  
Clinical Images ◽  
X Ray ◽  
Spiral Scanning ◽  
Spectral Reconstruction

Spectral CT utilizes spectral information of X-ray sources to reconstruct energy-resolved X-ray images and has wide medical applications. Compared with conventional energy-integrated CT scanners, however, spectral CT faces serious technical difficulties in hardware, and hence its clinical use has been expensive and limited. The goal of this paper is to present a software solution and an implementation of a framelet-based spectral reconstruction algorithm for multi-slice spiral scanning based on a conventional energy-integrated CT hardware platform. In the present work, we implement the framelet-based spectral reconstruction algorithm using compute unified device architecture (CUDA) with bowtie filtration. The platform CUDA enables fast execution of the program, while the bowtie filter reduces radiation exposure. We also adopt an order-subset technique to accelerate the convergence. The multi-slice spiral scanning geometry with these additional features will make the framelet-based spectral reconstruction algorithm more powerful for clinical applications. The method provides spectral information from just one scan with a standard energy-integrating detector and produces color CT images, spectral curves of the attenuation coefficient at every point inside the object, and photoelectric images, which are all valuable imaging tools in cancerous diagnosis. The proposed algorithm is tested with a Catphan phantom and real patient data sets for its performance. In experiments with the Catphan 504 phantom, the synthesized color image reveals changes in the level of colors and details and the yellow color in Teflon indicates a special spectral property which is invisible in regular CT reconstruction. In experiments with clinical images, the synthesized color images provide some extra details which are helpful for clinical diagnosis, for example, details about the renal pelvis and lumbar join. The numerical studies indicate that the proposed method provides spectral image information which can reveal fine structures in clinical images and that the algorithm is efficient regarding to the computational time. Thus, the proposed algorithm has a great potential in practical application.

CT reconstruction algorithm for a dental panoramic X-ray unit

1992 ◽  
Vol 37 (12) ◽  
pp. 2161-2174
Author(s):  
M D Lee ◽  
R G Waggener ◽  
W D McDavid ◽  
S B Dove ◽  
U Welander ◽  
...  
Keyword(s):  
Reconstruction Algorithm ◽  
Ct Reconstruction ◽  
X Ray

scholarly journals Beam Hardening Artifact Reduction in X-Ray CT Reconstruction of 3D Printed Metal Parts Leveraging Deep Learning and CAD Models

2021 ◽  
Author(s):  
Amir Ziabari ◽  
Singanallur Venkatakrishnan ◽  
Michael Kirka ◽  
Pauk Brackman ◽  
Ryan Dehoff ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artifact Reduction ◽  
Ct Reconstruction ◽  
Beam Hardening ◽  
X Ray ◽  
Metal Parts ◽  
Cad Models ◽  
3D Printed

scholarly journals Pulsed excitation terahertz tomography – multiparametric approach

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 111-116
Author(s):  
Przemyslaw Lopato
Keyword(s):  
Computed Tomography ◽  
Radon Transform ◽  
Reconstruction Algorithm ◽  
Time Signal ◽  
Pulsed Excitation ◽  
X Ray ◽  
Inverse Radon Transform ◽  
Inverse Radon

Abstract This article deals with pulsed excitation terahertz computed tomography (THz CT). Opposite to x-ray CT, where just a single value (pixel) is obtained, in case of pulsed THz CT the time signal is acquired for each position. Recorded waveform can be parametrized - many features carrying various information about examined structure can be calculated. Based on this, multiparametric reconstruction algorithm was proposed: inverse Radon transform based reconstruction is applied for each parameter and then fusion of results is utilized. Performance of the proposed imaging scheme was experimentally verified using dielectric phantoms.

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