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Reduction of noise-induced streak artifacts in X-ray computed tomography through spline-based penalized-likelihood sinogram smoothing
We present a statistically principled sinogram smoothing approach for X-ray computed tomography (CT) with the intent of reducing noise-induced streak artifacts. These artifacts arise in CT when some subset of the transmission measurements capture relatively few photons because of high attenuation al...
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| Published in: | IEEE transactions on medical imaging 2005-01, Vol.24 (1), p.105-111 |
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| creator | La Riviere, P.J. Billmire, D.M. |
| description | We present a statistically principled sinogram smoothing approach for X-ray computed tomography (CT) with the intent of reducing noise-induced streak artifacts. These artifacts arise in CT when some subset of the transmission measurements capture relatively few photons because of high attenuation along the measurement lines. Attempts to reduce these artifacts have focused on the use of adaptive filters that strive to tailor the degree of smoothing to the local noise levels in the measurements. While these approaches involve loose consideration of the measurement statistics to determine smoothing levels, they do not explicitly model the statistical distributions of the measurement data. We present an explicitly statistical approach to sinogram smoothing in the presence of photon-starved measurements. It is an extension of a nonparametric sinogram smoothing approach using penalized Poisson-likelihood functions that we have previously developed for emission tomography. Because the approach explicitly models the data statistics, it is naturally adaptive-it will smooth more variable measurements more heavily than it does less variable measurements. We find that it significantly reduces streak artifacts and noise levels without comprising image resolution. |
| doi_str_mv | 10.1109/TMI.2004.838324 |
| format | article |
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These artifacts arise in CT when some subset of the transmission measurements capture relatively few photons because of high attenuation along the measurement lines. Attempts to reduce these artifacts have focused on the use of adaptive filters that strive to tailor the degree of smoothing to the local noise levels in the measurements. While these approaches involve loose consideration of the measurement statistics to determine smoothing levels, they do not explicitly model the statistical distributions of the measurement data. We present an explicitly statistical approach to sinogram smoothing in the presence of photon-starved measurements. It is an extension of a nonparametric sinogram smoothing approach using penalized Poisson-likelihood functions that we have previously developed for emission tomography. Because the approach explicitly models the data statistics, it is naturally adaptive-it will smooth more variable measurements more heavily than it does less variable measurements. We find that it significantly reduces streak artifacts and noise levels without comprising image resolution.</description><identifier>ISSN: 0278-0062</identifier><identifier>EISSN: 1558-254X</identifier><identifier>DOI: 10.1109/TMI.2004.838324</identifier><identifier>PMID: 15638189</identifier><identifier>CODEN: ITMID4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Adaptive filters ; Algorithms ; Artificial Intelligence ; Attenuation measurement ; Computed tomography ; Computer Simulation ; Humans ; Information Storage and Retrieval - methods ; Models, Biological ; Models, Statistical ; Noise ; Noise level ; Noise measurement ; Noise reduction ; Numerical Analysis, Computer-Assisted ; Phantoms, Imaging ; Poisson distributions ; Radiographic Image Enhancement - methods ; Radiographic Image Interpretation, Computer-Assisted - methods ; Radiography, Thoracic - instrumentation ; Radiography, Thoracic - methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Smoothing methods ; Spline ; Statistical distributions ; Stochastic Processes ; Studies ; Tomography, X-Ray Computed - instrumentation ; Tomography, X-Ray Computed - methods ; X-ray imaging ; X-ray tomography</subject><ispartof>IEEE transactions on medical imaging, 2005-01, Vol.24 (1), p.105-111</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2005</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c468t-25ce0c2ddfc7ca25249eeaf5c0db0a3906c362bb33892b09b38bca2efcd3c64f3</citedby><cites>FETCH-LOGICAL-c468t-25ce0c2ddfc7ca25249eeaf5c0db0a3906c362bb33892b09b38bca2efcd3c64f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1375164$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,786,790,27957,27958,55147</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15638189$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>La Riviere, P.J.</creatorcontrib><creatorcontrib>Billmire, D.M.</creatorcontrib><title>Reduction of noise-induced streak artifacts in X-ray computed tomography through spline-based penalized-likelihood sinogram smoothing</title><title>IEEE transactions on medical imaging</title><addtitle>TMI</addtitle><addtitle>IEEE Trans Med Imaging</addtitle><description>We present a statistically principled sinogram smoothing approach for X-ray computed tomography (CT) with the intent of reducing noise-induced streak artifacts. These artifacts arise in CT when some subset of the transmission measurements capture relatively few photons because of high attenuation along the measurement lines. Attempts to reduce these artifacts have focused on the use of adaptive filters that strive to tailor the degree of smoothing to the local noise levels in the measurements. While these approaches involve loose consideration of the measurement statistics to determine smoothing levels, they do not explicitly model the statistical distributions of the measurement data. We present an explicitly statistical approach to sinogram smoothing in the presence of photon-starved measurements. It is an extension of a nonparametric sinogram smoothing approach using penalized Poisson-likelihood functions that we have previously developed for emission tomography. Because the approach explicitly models the data statistics, it is naturally adaptive-it will smooth more variable measurements more heavily than it does less variable measurements. We find that it significantly reduces streak artifacts and noise levels without comprising image resolution.</description><subject>Adaptive filters</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Attenuation measurement</subject><subject>Computed tomography</subject><subject>Computer Simulation</subject><subject>Humans</subject><subject>Information Storage and Retrieval - methods</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>Noise</subject><subject>Noise level</subject><subject>Noise measurement</subject><subject>Noise reduction</subject><subject>Numerical Analysis, Computer-Assisted</subject><subject>Phantoms, Imaging</subject><subject>Poisson distributions</subject><subject>Radiographic Image Enhancement - methods</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Radiography, Thoracic - instrumentation</subject><subject>Radiography, Thoracic - methods</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Smoothing methods</subject><subject>Spline</subject><subject>Statistical distributions</subject><subject>Stochastic Processes</subject><subject>Studies</subject><subject>Tomography, X-Ray Computed - instrumentation</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>X-ray imaging</subject><subject>X-ray tomography</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><recordid>eNqF0bFr3DAUBnBRWppL2rlDoYgO3XyR9CRbHktIm0BKoaSQzcjy81mJbbmSPFz3_t_VcQeBLp0Eer_3weMj5B1nW85ZfXn_7XYrGJNbDRqEfEE2XCldCCUfXpINE5UuGCvFGTmP8ZExLhWrX5MzrkrQXNcb8ucHdqtNzs_U93T2LmLh5vyFHY0poHmiJiTXG5sidTN9KILZU-unZU2ZJD_5XTDLsKdpCH7dDTQuo5uxaE3M8wVnM7rf2BWje8LRDd7nXDcfliYaJ-_T4ObdG_KqN2PEt6f3gvz8cn1_dVPcff96e_X5rrCy1CmfZZFZ0XW9rawRSsga0fTKsq5lBmpWWihF2wLoWrSsbkG32WFvO7Cl7OGCfDrmLsH_WjGmZnLR4jiaGf0am7KCUivg_4W8UsBqxTL8-A989GvIR8dGa5AACiCjyyOywccYsG-W4CYT9g1nzaHHJvfYHHpsjj3mjQ-n2LWdsHv2p-IyeH8EDhGfx1ApXkr4C5B8pQk</recordid><startdate>200501</startdate><enddate>200501</enddate><creator>La Riviere, P.J.</creator><creator>Billmire, D.M.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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methods</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>Noise</topic><topic>Noise level</topic><topic>Noise measurement</topic><topic>Noise reduction</topic><topic>Numerical Analysis, Computer-Assisted</topic><topic>Phantoms, Imaging</topic><topic>Poisson distributions</topic><topic>Radiographic Image Enhancement - methods</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Radiography, Thoracic - instrumentation</topic><topic>Radiography, Thoracic - methods</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Smoothing methods</topic><topic>Spline</topic><topic>Statistical distributions</topic><topic>Stochastic Processes</topic><topic>Studies</topic><topic>Tomography, X-Ray Computed - instrumentation</topic><topic>Tomography, X-Ray Computed - methods</topic><topic>X-ray imaging</topic><topic>X-ray tomography</topic><toplevel>online_resources</toplevel><creatorcontrib>La Riviere, P.J.</creatorcontrib><creatorcontrib>Billmire, D.M.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>La Riviere, P.J.</au><au>Billmire, D.M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reduction of noise-induced streak artifacts in X-ray computed tomography through spline-based penalized-likelihood sinogram smoothing</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>2005-01</date><risdate>2005</risdate><volume>24</volume><issue>1</issue><spage>105</spage><epage>111</epage><pages>105-111</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><notes>ObjectType-Article-2</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-1</notes><notes>content type line 23</notes><notes>ObjectType-Article-1</notes><notes>ObjectType-Feature-2</notes><abstract>We present a statistically principled sinogram smoothing approach for X-ray computed tomography (CT) with the intent of reducing noise-induced streak artifacts. These artifacts arise in CT when some subset of the transmission measurements capture relatively few photons because of high attenuation along the measurement lines. Attempts to reduce these artifacts have focused on the use of adaptive filters that strive to tailor the degree of smoothing to the local noise levels in the measurements. While these approaches involve loose consideration of the measurement statistics to determine smoothing levels, they do not explicitly model the statistical distributions of the measurement data. We present an explicitly statistical approach to sinogram smoothing in the presence of photon-starved measurements. It is an extension of a nonparametric sinogram smoothing approach using penalized Poisson-likelihood functions that we have previously developed for emission tomography. Because the approach explicitly models the data statistics, it is naturally adaptive-it will smooth more variable measurements more heavily than it does less variable measurements. We find that it significantly reduces streak artifacts and noise levels without comprising image resolution.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>15638189</pmid><doi>10.1109/TMI.2004.838324</doi><tpages>7</tpages></addata></record> |
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| ispartof | IEEE transactions on medical imaging, 2005-01, Vol.24 (1), p.105-111 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Journals |
| subjects | Adaptive filters Algorithms Artificial Intelligence Attenuation measurement Computed tomography Computer Simulation Humans Information Storage and Retrieval - methods Models, Biological Models, Statistical Noise Noise level Noise measurement Noise reduction Numerical Analysis, Computer-Assisted Phantoms, Imaging Poisson distributions Radiographic Image Enhancement - methods Radiographic Image Interpretation, Computer-Assisted - methods Radiography, Thoracic - instrumentation Radiography, Thoracic - methods Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Smoothing methods Spline Statistical distributions Stochastic Processes Studies Tomography, X-Ray Computed - instrumentation Tomography, X-Ray Computed - methods X-ray imaging X-ray tomography |
| title | Reduction of noise-induced streak artifacts in X-ray computed tomography through spline-based penalized-likelihood sinogram smoothing |
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