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A Method for Correcting Bone Induced Artifacts in Computed Tomography Scanners
Abstract
A method that corrects for artifacts in X-ray transmission scanners created by the alteration of the energy spectrum by bone is described. The method involves two reconstructions of the pictures: the first establishes the approximate distribution of bone and the second enables the artifacts to be eliminated. The first reconstruction is needed to establish the total amount of bone along each ray. Assuming that the X-ray energy spectrum is known, it is shown how the line integrals can be corrected. The method was tested on several mathematical phantoms as well as on human head data. The nonlinear nature of the spectral artifacts is shown to produce streaks in certain phantoms.
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“the large extracranial metal components and dental cement of the implant can cause beam hardening that can appear as cupping, streaks, dark bands or flare in the µCT38–40.”
From Oxytocin neurons enable social transmission of maternal behaviour (2021) · cites this paper as [40]
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A bench‐top megavoltage fan‐beam CT using ‐photodiode detectors. II. Image performance evaluation
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Dose reduction using a dynamic, piecewise‐linear attenuator
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A simple computational method for reducing streak artifacts in CT images
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Intracranial CT angiography obtained from a cerebral CT perfusion examination
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Morphology and dimensions of the thoracic cord by computer-assisted metrizamide myelography.
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Self-citationHumphries, T., Winn, J., & Faridani, A. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa7c2d · Cited by 24
“While techniques similar to soft tissue correction can be extended to account for artifacts of this nature (Joseph & Spital 1978, Joseph & Ruth 1997), they typically require image segmentation and do not account for mixtures of different tissue types within pixels.”
MethodModeling of polychromatic attenuation using computed tomography reconstructed images
Yan, C. H., Whalen, R. T., Beaupré, G. S., et al. (1999) · Medical Physics · doi:10.1118/1.598563 · Cited by 23
Assessment of bone mass in the radius by computed tomography
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Data consistency condition-based beam-hardening correction
Tang, S., Mou, X., Xu, Q., et al. (2011) · Optical Engineering · doi:10.1117/1.3599869 · Cited by 22
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From EMI to AI: a brief history of commercial CT reconstruction algorithms
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“20 Aliasing artifacts caused by under-sampling the fan beam detector were mitigated by interlacing the fan-beam projections by displacing the detector array by a quarter of the detector spacing, 21 although in practice the transition to smaller detector cells and the introduction of flying focal…”
BackgroundEvaluation of detector readout gain mode and bowtie filters for cone-beam CT imaging of the head
Xu, J., Sisniega, A., Zbijewski, W. B., et al. (2016) · Physics in Medicine and Biology · doi:10.1088/0031-9155/61/16/5973 · Cited by 19
Beam hardening correction for fan-beam CT imaging with multiple materials
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“And numerous postreconstruction methods were proposed to deal with dualmaterial case [ 5 , 6]. But algorithms to address multi-material BH problem are very rare since the correction complexity and instability increase with the increase of the kinds of materials in the object.”
MethodImproving PET/CT Attenuation Correction with Iterative Beam Hardening Correction
Kachelrieß, M., & Kalender, W. A. (2006) · 2006 IEEE Nuclear Science Symposium Conference Record · doi:10.1109/nssmic.2005.1596704 · Cited by 19
“Therefore dedicated first, second or higher order correction algorithms have been designed and there is a vast amount of literature describing these [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28].”
BackgroundMethodTomographic Reconstruction Of Axially Symmetric Objects From A Single Radiograph
Hanson, K. M. (1985) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.967907 · Cited by 19
Porat‐Mosenco, Y., Schwarz, T., & Kass, P. H. (2004) · Veterinary Radiology & Ultrasound · doi:10.1111/j.1740-8261.2004.04021.x · Cited by 19
Shading correction assisted iterative cone-beam CT reconstruction
Yang, C., Wu, P., Gong, S., Wang, J., Lyu, Q., Tang, X., & Niu, T. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa8e62 · Cited by 18
“In the past, the shading artifacts in CBCT images were successfully addressed by dealing with the beam hardening (Joseph and Spital 1978) and scatter (Glover 1982, Ning et al 2004), separately, before or after image reconstruction with the FBP algorithm.”
MethodYoon, S., Mäkiharju, S. A., Fessler, J. A., et al. (2017) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2017.2775603 · Cited by 18
Dual-Energy Computed Tomography
Krauß, B. (2018) · Radiologic Clinics of North America · doi:10.1016/j.rcl.2018.03.008 · Cited by 19
CT Reprojection Polychromaticity Correction for Three Attenuators
Olson, E. A., Han, K. S., & Pisano, D. J. (1981) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.1981.4331811 · Cited by 18
“(6) which is certainly not equivalent to Equation (2).”
BackgroundInfluentialArtifacts associated with implementation of the Grangeat formula
Lee, S. W., Cho, G., & Wang, G. (2002) · Medical Physics · doi:10.1118/1.1522748 · Cited by 18
Dynamic fluence field modulation in computed tomography using multiple aperture devices
Gang, G. J., Mao, A., Wang, W., et al. (2019) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab155e · Cited by 18
“where κ carries the same dependencies as gM and accounts for spectral effects from the MADs, similar to a 1st order beam hardening correction factor (Joseph and Spital 1978).”
BackgroundGoodsitt, M. M., Johnson, R. H., & Chesnut, C. H. (1991) · Bone and Mineral · doi:10.1016/0169-6009(91)90070-g · Cited by 18
Current status of the CBCT project at Varian Medical Systems
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Ultrasonic Imaging by Reconstructive Tomography
Wade, G. (1980) · Acoustical imaging · doi:10.1007/978-1-4684-3755-3_24 · Cited by 18
Cancelliere, N. M., Hummel, E., van Nijnatten, F., et al. (2022) · Journal of NeuroInterventional Surgery · doi:10.1136/neurintsurg-2021-018553 · Cited by 17
Model-based three-material decomposition in dual-energy CT using the volume conservation constraint
Liu, S. Z., Tivnan, M., Osgood, G. M., et al. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac7a8b · Cited by 17
Zhang, J., Yan, C.-H., Chui, C., et al. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2045767 · Cited by 17
“Single energy approaches such as water correction and bone correction [ 11 ] are mainly built-in algorithms for the “abdomen mode” and “head mode” in conventional CT scanners.”
MethodBayat, F., Ruan, D., Miften, M. M., & Altunbas, C. (2023) · Medical Physics · doi:10.1002/mp.16681 · Cited by 16
Optimization based beam-hardening correction in CT under data integral invariant constraint
Tang, S., Huang, K., Cheng, Y., et al. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aaca14 · Cited by 16
“…may co-exist in the subject to be imaged, which can be a human or animal body, biological specimen or prosthetic part (McDavid et al 1977, Joseph and Spital 1978, Herman 1979, Nalcioglu and Lou 1979, De Man et al 1999, Hsieh et al 2000, Kachelrieß et al 2006, Tang et al 2006, 2008, 2011,…”
BackgroundMethodInfluentialA Single Scatter Model for X-ray CT Energy Spectrum Estimation and Polychromatic Reconstruction
Lee, J.-S., & Chen, J. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2395438 · Cited by 16
“clusters, which is needed when Joseph and Spital's approach is used [15].”
MethodBismark, R., Frysch, R., Abdurahman, S., et al. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2019.10.002 · Cited by 16
Grimmer, R., & Kachelrieß, M. (2011) · Medical Physics · doi:10.1118/1.3561506 · Cited by 16
Evaluation des Elektronendichte-Phantoms CIRS Model 62
Pemler, P., Schneider, U., & Besserer, J. (2001) · Zeitschrift für Medizinische Physik · doi:10.1016/s0939-3889(15)70384-3 · Cited by 16
Cao, W., Sun, T., Fardell, G., et al. (2018) · Journal of Microscopy · doi:10.1111/jmi.12746 · Cited by 15
Statistical x-ray-computed tomography image reconstruction with beam- hardening correction
Elbakri, I. A., & Fessler, J. A. (2001) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.430961 · Cited by 15
“j=1 ij wi 2 aij ij ( j n j ) + [A ]i l̂i 2 : (18) A similar development can be pursued for the convex penalty term R( ) yielding a separable penalty surrogate, denoted S( ; ).”
BackgroundWater calibration for CT scanners with tube voltage modulation
Ritschl, L., Bergner, F., Fleischmann, C., et al. (2010) · Physics in Medicine and Biology · doi:10.1088/0031-9155/55/14/010 · Cited by 15
Estrela, C., Estrela, C., Bueno, M. R., et al. (2011) · Dental Press Endodontics · doi:10.14436/2178-3713.1.1.028-036.oar · Cited by 15
Aspects of Semiconductor Current Mode Detectors for X-Ray Computed Tomography
Glasow, P. A., Conrad, B., Killig, K., et al. (1981) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.1981.4331240 · Cited by 15
AAPM Task Group Report 238: 3D C‐arms with volumetric imaging capability*
Supanich, M. P., Siewerdsen, J. H., Fahrig, R., Farahani, K., Gang, G. J., Helm, P. A., Jans, J. C., Jones, A. K., Koenig, T. W., Kuhls-Gilcrist, A. T., et al. (2023) · Medical Physics · doi:10.1002/mp.16245 · Cited by 14
“Methods and specifications for performing these evaluations are in AAPM Reports 4, 43 70, 44 and 74.”
Task-based statistical image reconstruction for high-quality cone-beam CT
Dang, H., Stayman, J. W., Xu, J., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa90fd · Cited by 14
“Projection data were corrected for x-ray scatter using a Monte Carlo scatter correction method (Sisniega et al 2015) and for beam hardening using the Joseph and Spital method (Joseph and Spital 1978).”
MethodA fast poly-energetic iterative FBP algorithm
Lin, Y., & Samei, E. (2014) · Physics in Medicine and Biology · doi:10.1088/0031-9155/59/7/1655 · Cited by 13
A method to correct for spectral artifacts in optical-CT dosimetry
Thomas, A. S. W., Pierquet, M., Jordan, K. J., et al. (2011) · Physics in Medicine and Biology · doi:10.1088/0031-9155/56/11/014 · Cited by 13
Hsieh, S. S., & Pelc, N. J. (2014) · Physics in Medicine and Biology · doi:10.1088/0031-9155/59/11/2829 · Cited by 13
“Preliminary simulations have suggested that the resulting beam hardening artifacts can be corrected (Hsieh and Pelc 2013b), especially with the use of a twopass algorithm (Joseph and Spital 1978).”
BackgroundHampel, U. (2015) · Elsevier eBooks · doi:10.1016/b978-1-78242-118-4.00006-x · Cited by 13
Modified MLEM Algorithm for Artifact Suppression in CT
Oehler, M., & Buzug, T. M. (2006) · 2006 IEEE Nuclear Science Symposium Conference Record · doi:10.1109/nssmic.2006.353757 · Cited by 13
“Fig. 5:X-MLEMreconstruction withX 1.0ofground truth data frf(a), X-MLEM reconstruction withX 1.0ofmetaldata(b),X-MLEM-LI reconstruction withXopt= 0.3(c)andX-MLEM-POLY reconstruction with )opt = 0. 5 (d).”
BackgroundInfluentialSimple beam hardening correction method (2DCalBH) based on 2D linearization
Martínez, C., Fessler, J. A., Desco, M., et al. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac5f71 · Cited by 12
“The proposedmethod, 2DCalBH,was evaluated using simulations and real data by comparingwith JSpol, the polynomial approximation of Joseph and Spital (1978) suggested inAbella et al (2020), andwith EBHC (Kyriakou et al 2010) and sfEBHC (Schuller et al 2015).”
MethodSoft tissue variations influence HR-pQCT density measurements in a spatially dependent manner
Wu, P., Gupta, T., Chang, H., Petrenko, D. M., Schafer, A. L., & Kazakia, G. J. (2020) · Bone · doi:10.1016/j.bone.2020.115505 · Cited by 13
Nonlinear partial volume artifact correction in helical CT
Hsieh, J. (1999) · IEEE Transactions on Nuclear Science · doi:10.1109/23.775608 · Cited by 12
Organ detection in abdominal computerized tomography scans: Application to the kidney
Selfridge, P. G., & Prewitt, J. M. S. (1981) · Computer Graphics and Image Processing · doi:10.1016/0146-664x(81)90059-9 · Cited by 12
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Segmentation-free statistical image reconstruction for polyenergetic X-ray computed tomography
Elbakri, I. A., & Fessler, J. A. (2003) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2002.1029387 · Cited by 11
“We model the attenuation coefficient of the jth pixel as the sum over K materials of the product of the (known) energy-dependent mass attenuation coefficient mk(E) (cm/g), the (unknown) energyindependent density ρj (g/cm) [11, 8, 12] and the material fraction at the pixel f j .”
InfluentialCao, W., Sun, T., Kerckhofs, G., et al. (2018) · Measurement Science and Technology · doi:10.1088/1361-6501/aacba0 · Cited by 11
“A segmentation based linearisation method was implemented based on the Joseph–Spital method (Joseph and Spital 1978).”
MethodLow‐dose megavoltage cone‐beam computed tomography using a novel multi‐layer imager (MLI)
Myronakis, M. E., Huber, P., Lehmann, M., et al. (2020) · Medical Physics · doi:10.1002/mp.14017 · Cited by 11
“The scatter-corrected projections were then corrected for beam hardening by transforming them to water-equivalent path lengths [20].”
MethodUltrasonic non-destructive measurements of cortical bone thickness in human cadaver femur
Singh, S. (1989) · Ultrasonics · doi:10.1016/0041-624x(89)90022-x · Cited by 11
Polyquant CT: direct electron and mass density reconstruction from a single polyenergetic source
Mason, J. H., Perelli, A., Nailon, W. H., et al. (2017) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aa9162 · Cited by 10
“…approximates the CT model as linear by taking the logarithm of the raw measurements (Chang et al., 2014), calibrating them to correct for beam-hardening artefacts for the polyenergetic source (Joseph and Spital, 1978), and includes a statistical weighting to approximate the Poisson noise in (3).”
BackgroundMethodResultInfluentialCalibration‐free beam hardening correction for myocardial perfusion imaging using CT
Levi, J., Eck, B., Fahmi, R., et al. (2019) · Medical Physics · doi:10.1002/mp.13402 · Cited by 10
“(2) would be modified by replacing l with (x).”
BackgroundKachelrieß, M. (2008) · Handbook of experimental pharmacology · doi:10.1007/978-3-540-72718-7_2 · Cited by 10
The Use of Computed Tomography Numbers in Dose Calculations for Radiation Therapy
Huizenga, H., & Storchi, P. R. M. (1985) · Acta Radiologica Oncology · doi:10.3109/02841868509134425 · Cited by 10
Control algorithms for dynamic attenuators
Hsieh, S. S., & Pelc, N. J. (2014) · Medical Physics · doi:10.1118/1.4875727 · Cited by 10
Mou, X., Tang, S., & Yu, H. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.682870 · Cited by 10
A Deep-Learning-Based Method for Correction of Bone-Induced CT Beam-Hardening Artifacts
Ji, X., Gao, D.-Z., Gan, Y. P., et al. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3276030 · Cited by 9
“Therefore, as the polychromatic X-ray beam penetrates the image object, the mean energy of the beam increases, which leads to beam-hardening artifacts in computed tomography (CT) images [1], [2], [3], [4], [5],”
BackgroundA new statistical image reconstruction algorithm for polyenergetic X-ray CT
Abella, M., & Fessler, J. A. (2009) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2009.5193009 · Cited by 9
“The method developed by Joseph and Spital [4] improves upon the water correction method and uses only one or two additional parameters that can be tuned empirically rather than requiring the entire Xray spectrum.”
BackgroundMethodInfluentialEmpirical Water Precorrection for Cone-Beam Computed Tomography
Sourbelle, K., Kachelrieß, M., & Kalender, W. A. (2006) · 2006 IEEE Nuclear Science Symposium Conference Record · doi:10.1109/nssmic.2005.1596696 · Cited by 9
“More complicated beam-hardening methods of iterative type exist as well [5], [6], [7] but are more intended to correct for artifacts induced by highly attenuating objects like bones.”
BackgroundSegmentation algorithms for abdominal computerized tomography scans
Selfridge, P. G., Judith, M., Prewitt, M. S., et al. (2005) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/cmpsac.1979.762560 · Cited by 9
Cao, W., Hawker, S. P. M., Fardell, G., et al. (2019) · Measurement Science and Technology · doi:10.1088/1361-6501/ab30bb · Cited by 8
“The commonly used segmentation scenario is the JS method (Joseph and Spital 1978, Joseph and Ruth 1997).”
MethodSingle‐material beam hardening correction via an analytical energy response model for diagnostic CT
Haase, V., Hähn, K., Schöndube, H., et al. (2022) · Medical Physics · doi:10.1002/mp.15787 · Cited by 8
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Physics-based spectral compensation algorithm for x-ray CT with primary modulator
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“ε bi(ε) exp ( − ∫ Li μ(ε)dl ) dε, (1) where bi(ε) denotes the unattenuated signal of detector pixel i at energy ε, and μ(ε) denotes the energy dependent linear attenuation coefficient of the object.”
Computer tomography for virtual models in dental imaging
Natali, A., & Viola, M. (2003) · CRC Press eBooks · doi:10.1201/9780203514849.ch3 · Cited by 8
Convergent algorithms for statistical image reconstruction in emission tomography.
Fessler, J. A., & Ahn, S. (2004) · Deep Blue (University of Michigan) · Cited by 8
Oxytocin Neurons Enable Social Transmission of Maternal Behavior
Carcea, I., Caraballo, N. L., Marlin, B. J., Ooyama, R., Riceberg, J. S., Mendoza Navarro, J. M., Opendak, M., Diaz, V. E., Schuster, L., Alvarado Torres, M. I., et al. (2019) · doi:10.1101/845495 · Cited by 8
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SOME MATHEMATICAL CONCEPTS FOR TOMOGRAPHIC RECONSTRUCTION
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Quantitative cone-beam CT reconstruction with polyenergetic scatter model fusion
Mason, J. H., Perelli, A., Nailon, W. H., et al. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aae794 · Cited by 7
Superiorized method for metal artifact reduction
Humphries, T., & Wang, B. (. (2020) · Medical Physics · doi:10.1002/mp.14332 · Cited by 7
Beam hardening correction based on HL consistency in polychromatic transmission tomography
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“Compared to a dozen of published methods, the combination of water correction [1] and bone correction [2] is still the most efficient that has been used in current medical X-ray CT, nevertheless, it is just a method in engineering practice, but not a perfect analytic scheme, that means there is still the space to impr…”
BackgroundMethodInfluentialCorrection of Bowtie-Filter Normalization and Crescent Artifacts for a Clinical CBCT System
Zhang, H., Kong, V., Huang, K. C., et al. (2016) · Technology in Cancer Research & Treatment · doi:10.1177/1533034615627584 · Cited by 7
Impact of a novel multilayer imager on metal artifacts in MV-CBCT
Harris, T. C., Jacobson, M. W., Myronakis, M. E., et al. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ace09a · Cited by 6
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Simplified Statistical Image Reconstruction Algorithm for Polyenergetic X-Ray CT
Srivastava, S., & Fessler, J. A. (2006) · 2006 IEEE Nuclear Science Symposium Conference Record · doi:10.1109/nssmic.2005.1596614 · Cited by 6
“The standard approach used with filtered backprojection (FBP) reconstruction is the Joseph and Spital (JS) method [1].”
Achieving accurate radiochromic optical-CT imaging when using a polychromatic light source
Thomas, A., Pierquet, M., & Oldham, M. A. (2010) · Journal of Physics Conference Series · doi:10.1088/1742-6596/250/1/012045 · Cited by 6
An algorithm to estimate the object support in truncated images
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A framelet-based iterative maximum-likelihood reconstruction algorithm for spectral CT
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Statistical reconstruction algorithms for polyenergetic X -ray computed tomography.
Elbakri, I. A., & Fessler, J. A. (2003) · Cited by 6
Interhemispheric differences in regional density of the normal brain
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Iterative Reconstruction Algorithms for Polyenergetic X-ray Computerized Tomography
Rezvani, N. (2012) · TSpace (University of Toronto) · Cited by 6
Shape prior metal artefact reduction algorithm for industrial 3D cone beam CT
Jeon, S., Kim, S., & Lee, C.-O. (2020) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2019.1709457 · Cited by 6
Extension of the cone‐beam CT field‐of‐view using two complementary short scans
Belotti, G., Fattori, G., Baroni, G., et al. (2023) · Medical Physics · doi:10.1002/mp.16869 · Cited by 5
Characterization of Metal Artifacts in X‐Ray Computed Tomography
Park, H. S., Choi, J. K., & Seo, J. K. (2017) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.21680 · Cited by 5
“However, patients with metal implants may not receive the benefits of CT scanning because the quality of the image can be greatly degraded by metal streaking artifacts that appear as dark and bright streaks [33].”
BackgroundMetal Artifact Reduction Method Based on Noncoplanar Scanning in CBCT Imaging
Gao, L., Sui, J., Lin, T., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2962386 · Cited by 5
“Material decomposition-inspired methods were also proposed [24]–[26] to reduce beam hardening artifacts.”
MethodBone Densitometry Within Titanium Lumbar Interbody Fusion Cages
Robertson, D. D., Sharma, G. B., Gilbertson, L. G., et al. (2009) · Spine · doi:10.1097/brs.0b013e3181b61e00 · Cited by 5
Oh, D., Kim, S., Park, D., et al. (2018) · Medical Physics · doi:10.1002/mp.13072 · Cited by 5
Metadata
- Authors
- Joseph, P. M., & Spital, R. D.
- Affiliations
- NewYork–Presbyterian Hospital (US)
- Venue
- Journal of Computer Assisted Tomography
- Published
- 1978-01
- Volume
- 2
- Issue
- 1
- Pages
- 100-108
- ISSN
- 0363-8715 (ISSN-L)
- OpenAlex
- W2000441297
- Cited by
- 281
- References
- 0
- Citations by year
- 2026: 7 · 2025: 4 · 2024: 10 · 2023: 6 · 2022: 13 · 2021: 6 · 2020: 9 · 2019: 12 · 2018: 17 · 2017: 10 · 2016: 10 · 2015: 9 · 2014: 10 · 2013: 10 · 2012: 13
- FWCI
- 8.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computer Assisted Tomography (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced X-ray and CT Imaging
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiation Dose and Imaging
- Keywords
- bone artifacts, spectral artifacts, X-ray spectra, streak artifacts, head CT, image reconstruction, mathematical phantoms
- MeSH terms
- Bone and Bones, Humans, Mathematics, Models, Structural, Skull, Technology, Radiologic, Tomography, X-Ray Computed
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