A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions
Objectives To assess the image quality and diagnostic accuracy of a noise-optimized virtual monoenergetic imaging (VMI+) algorithm compared with standard virtual monoenergetic imaging (VMI) and linearly-blended (M_0.6) reconstructions for the detection of hypervascular liver lesions in dual-energy C...
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| Veröffentlicht in: | European radiology 2018-08, Vol.28 (8), p.3393-3404 |
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| creator | De Cecco, Carlo N. Caruso, Damiano Schoepf, U. Joseph De Santis, Domenico Muscogiuri, Giuseppe Albrecht, Moritz H. Meinel, Felix G. Wichmann, Julian L. Burchett, Philip F. Varga-Szemes, Akos Sheafor, Douglas H. Hardie, Andrew D. |
| description | Objectives
To assess the image quality and diagnostic accuracy of a noise-optimized virtual monoenergetic imaging (VMI+) algorithm compared with standard virtual monoenergetic imaging (VMI) and linearly-blended (M_0.6) reconstructions for the detection of hypervascular liver lesions in dual-energy CT (DECT).
Methods
Thirty patients who underwent clinical liver MRI were prospectively enrolled. Within 60 days of MRI, arterial phase DECT images were acquired on a third-generation dual-source CT and reconstructed with M_0.6, VMI and VMI+ algorithms from 40 to 100 keV in 5-keV intervals. Liver parenchyma and lesion contrast-to-noise-ratios (CNR) were calculated. Two radiologists assessed image quality. Lesion sensitivity, specificity and area under the receiver operating characteristic curves (AUCs) were calculated for the three algorithms with MRI as the reference standard.
Results
VMI+ datasets from 40 to 60 keV provided the highest liver parenchyma and lesion CNR (
p
≤0.021); 50 keV VMI+ provided the highest subjective image quality (4.40±0.54), significantly higher compared to VMI and M_0.6 (all
p |
| doi_str_mv | 10.1007/s00330-018-5313-6 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2007114898</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2007114898</sourcerecordid><originalsourceid>FETCH-LOGICAL-c438t-d71a5ecc83554568a489738adcc888af62833311907a588907da16823857dc993</originalsourceid><addsrcrecordid>eNp1kU-L1TAUxYMozpvRD-BGAm7cRJMmbdPl8PAfDLgZ1-Wa3r5maJuapA-en24-mvdNRwXBTQK5v3NOksPYKyXfKSnr90lKraWQyopSKy2qJ2ynjC6EktY8ZTvZaCvqpjEX7DKlOyllo0z9nF0UjanIoNyx-2s-B59QhCX7yf_Ejh99zCuMfApzwBnjAbN3PKILc8pxddmHmcN4CNHnYeJ-WmI4YuJ5QN55OMwhnQXg3BrBnXjo-QgZ-YALPAxixugpYBkgkYSyxEPOie9veR_i5oQZtyjSD6cF4xGSW0eIfPRHpBUTTdML9qyHMeHLx_2Kffv44Xb_Wdx8_fRlf30jnNE2i65WUKJzVpelKSsLxja1ttDRkbXQV4XVWivVyBpKa2nrQFW20LasO9c0-oq93XzptT9WTLmdfHI4jjBjWFNb0H8qRa6W0Df_oHdhjTPd7kzpotTaGKLURrkYUorYt0v0E8RTq2R7rrfd6m2p3vZcb1uR5vWj8_p9wu6P4nefBBQbkGg0HzD-jf6_6y_FI7Pb</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2003253344</pqid></control><display><type>article</type><title>A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>De Cecco, Carlo N. ; Caruso, Damiano ; Schoepf, U. Joseph ; De Santis, Domenico ; Muscogiuri, Giuseppe ; Albrecht, Moritz H. ; Meinel, Felix G. ; Wichmann, Julian L. ; Burchett, Philip F. ; Varga-Szemes, Akos ; Sheafor, Douglas H. ; Hardie, Andrew D.</creator><creatorcontrib>De Cecco, Carlo N. ; Caruso, Damiano ; Schoepf, U. Joseph ; De Santis, Domenico ; Muscogiuri, Giuseppe ; Albrecht, Moritz H. ; Meinel, Felix G. ; Wichmann, Julian L. ; Burchett, Philip F. ; Varga-Szemes, Akos ; Sheafor, Douglas H. ; Hardie, Andrew D.</creatorcontrib><description>Objectives
To assess the image quality and diagnostic accuracy of a noise-optimized virtual monoenergetic imaging (VMI+) algorithm compared with standard virtual monoenergetic imaging (VMI) and linearly-blended (M_0.6) reconstructions for the detection of hypervascular liver lesions in dual-energy CT (DECT).
Methods
Thirty patients who underwent clinical liver MRI were prospectively enrolled. Within 60 days of MRI, arterial phase DECT images were acquired on a third-generation dual-source CT and reconstructed with M_0.6, VMI and VMI+ algorithms from 40 to 100 keV in 5-keV intervals. Liver parenchyma and lesion contrast-to-noise-ratios (CNR) were calculated. Two radiologists assessed image quality. Lesion sensitivity, specificity and area under the receiver operating characteristic curves (AUCs) were calculated for the three algorithms with MRI as the reference standard.
Results
VMI+ datasets from 40 to 60 keV provided the highest liver parenchyma and lesion CNR (
p
≤0.021); 50 keV VMI+ provided the highest subjective image quality (4.40±0.54), significantly higher compared to VMI and M_0.6 (all
p
<0.001), and the best diagnostic accuracy in < 1-cm diameter lesions (AUC=0.833 vs. 0.777 and 0.749, respectively;
p
≤0.003).
Conclusions
50-keV VMI+ provides superior image quality and diagnostic accuracy for the detection of hypervascular liver lesions with a diameter < 1cm compared to VMI or M_0.6 reconstructions.
Key Points
• Low-keV VMI+ are characterized by higher contrast resulting from maximum iodine attenuation.
• VMI+ provides superior image quality compared with VMI or M_0.6.
• 50-keV_VMI+ provides higher accuracy for the detection of hypervascular liver lesions < 1cm.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-018-5313-6</identifier><identifier>PMID: 29460075</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Adult ; Aged ; Algorithms ; Computed Tomography ; Diagnostic Radiology ; Diagnostic systems ; Female ; Humans ; Image acquisition ; Image contrast ; Image detection ; Image Processing, Computer-Assisted - methods ; Image quality ; Image reconstruction ; Imaging ; Internal Medicine ; Interventional Radiology ; Iodine ; Lesions ; Liver ; Liver - blood supply ; Liver - diagnostic imaging ; Liver Neoplasms - blood supply ; Liver Neoplasms - diagnostic imaging ; Magnetic resonance imaging ; Male ; Mathematical analysis ; Medical diagnosis ; Medicine ; Medicine & Public Health ; Middle Aged ; Neuroradiology ; Noise ; Parenchyma ; Prospective Studies ; Quality ; Quality assessment ; Radiography, Dual-Energy Scanned Projection - methods ; Radiology ; Reproducibility of Results ; Sensitivity analysis ; Sensitivity and Specificity ; Signal-To-Noise Ratio ; Tomography, X-Ray Computed - methods ; Ultrasound</subject><ispartof>European radiology, 2018-08, Vol.28 (8), p.3393-3404</ispartof><rights>European Society of Radiology 2018</rights><rights>European Radiology is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c438t-d71a5ecc83554568a489738adcc888af62833311907a588907da16823857dc993</citedby><cites>FETCH-LOGICAL-c438t-d71a5ecc83554568a489738adcc888af62833311907a588907da16823857dc993</cites><orcidid>0000-0002-6164-5641</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-018-5313-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-018-5313-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29460075$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>De Cecco, Carlo N.</creatorcontrib><creatorcontrib>Caruso, Damiano</creatorcontrib><creatorcontrib>Schoepf, U. Joseph</creatorcontrib><creatorcontrib>De Santis, Domenico</creatorcontrib><creatorcontrib>Muscogiuri, Giuseppe</creatorcontrib><creatorcontrib>Albrecht, Moritz H.</creatorcontrib><creatorcontrib>Meinel, Felix G.</creatorcontrib><creatorcontrib>Wichmann, Julian L.</creatorcontrib><creatorcontrib>Burchett, Philip F.</creatorcontrib><creatorcontrib>Varga-Szemes, Akos</creatorcontrib><creatorcontrib>Sheafor, Douglas H.</creatorcontrib><creatorcontrib>Hardie, Andrew D.</creatorcontrib><title>A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To assess the image quality and diagnostic accuracy of a noise-optimized virtual monoenergetic imaging (VMI+) algorithm compared with standard virtual monoenergetic imaging (VMI) and linearly-blended (M_0.6) reconstructions for the detection of hypervascular liver lesions in dual-energy CT (DECT).
Methods
Thirty patients who underwent clinical liver MRI were prospectively enrolled. Within 60 days of MRI, arterial phase DECT images were acquired on a third-generation dual-source CT and reconstructed with M_0.6, VMI and VMI+ algorithms from 40 to 100 keV in 5-keV intervals. Liver parenchyma and lesion contrast-to-noise-ratios (CNR) were calculated. Two radiologists assessed image quality. Lesion sensitivity, specificity and area under the receiver operating characteristic curves (AUCs) were calculated for the three algorithms with MRI as the reference standard.
Results
VMI+ datasets from 40 to 60 keV provided the highest liver parenchyma and lesion CNR (
p
≤0.021); 50 keV VMI+ provided the highest subjective image quality (4.40±0.54), significantly higher compared to VMI and M_0.6 (all
p
<0.001), and the best diagnostic accuracy in < 1-cm diameter lesions (AUC=0.833 vs. 0.777 and 0.749, respectively;
p
≤0.003).
Conclusions
50-keV VMI+ provides superior image quality and diagnostic accuracy for the detection of hypervascular liver lesions with a diameter < 1cm compared to VMI or M_0.6 reconstructions.
Key Points
• Low-keV VMI+ are characterized by higher contrast resulting from maximum iodine attenuation.
• VMI+ provides superior image quality compared with VMI or M_0.6.
• 50-keV_VMI+ provides higher accuracy for the detection of hypervascular liver lesions < 1cm.</description><subject>Accuracy</subject><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Computed Tomography</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Female</subject><subject>Humans</subject><subject>Image acquisition</subject><subject>Image contrast</subject><subject>Image detection</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image quality</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Iodine</subject><subject>Lesions</subject><subject>Liver</subject><subject>Liver - blood supply</subject><subject>Liver - diagnostic imaging</subject><subject>Liver Neoplasms - blood supply</subject><subject>Liver Neoplasms - diagnostic imaging</subject><subject>Magnetic resonance imaging</subject><subject>Male</subject><subject>Mathematical analysis</subject><subject>Medical diagnosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Neuroradiology</subject><subject>Noise</subject><subject>Parenchyma</subject><subject>Prospective Studies</subject><subject>Quality</subject><subject>Quality assessment</subject><subject>Radiography, Dual-Energy Scanned Projection - methods</subject><subject>Radiology</subject><subject>Reproducibility of Results</subject><subject>Sensitivity analysis</subject><subject>Sensitivity and Specificity</subject><subject>Signal-To-Noise Ratio</subject><subject>Tomography, X-Ray Computed - methods</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp1kU-L1TAUxYMozpvRD-BGAm7cRJMmbdPl8PAfDLgZ1-Wa3r5maJuapA-en24-mvdNRwXBTQK5v3NOksPYKyXfKSnr90lKraWQyopSKy2qJ2ynjC6EktY8ZTvZaCvqpjEX7DKlOyllo0z9nF0UjanIoNyx-2s-B59QhCX7yf_Ejh99zCuMfApzwBnjAbN3PKILc8pxddmHmcN4CNHnYeJ-WmI4YuJ5QN55OMwhnQXg3BrBnXjo-QgZ-YALPAxixugpYBkgkYSyxEPOie9veR_i5oQZtyjSD6cF4xGSW0eIfPRHpBUTTdML9qyHMeHLx_2Kffv44Xb_Wdx8_fRlf30jnNE2i65WUKJzVpelKSsLxja1ttDRkbXQV4XVWivVyBpKa2nrQFW20LasO9c0-oq93XzptT9WTLmdfHI4jjBjWFNb0H8qRa6W0Df_oHdhjTPd7kzpotTaGKLURrkYUorYt0v0E8RTq2R7rrfd6m2p3vZcb1uR5vWj8_p9wu6P4nefBBQbkGg0HzD-jf6_6y_FI7Pb</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>De Cecco, Carlo N.</creator><creator>Caruso, Damiano</creator><creator>Schoepf, U. Joseph</creator><creator>De Santis, Domenico</creator><creator>Muscogiuri, Giuseppe</creator><creator>Albrecht, Moritz H.</creator><creator>Meinel, Felix G.</creator><creator>Wichmann, Julian L.</creator><creator>Burchett, Philip F.</creator><creator>Varga-Szemes, Akos</creator><creator>Sheafor, Douglas H.</creator><creator>Hardie, Andrew D.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PHGZM</scope><scope>PHGZT</scope><scope>PJZUB</scope><scope>PKEHL</scope><scope>PPXIY</scope><scope>PQEST</scope><scope>PQGLB</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6164-5641</orcidid></search><sort><creationdate>20180801</creationdate><title>A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions</title><author>De Cecco, Carlo N. ; Caruso, Damiano ; Schoepf, U. Joseph ; De Santis, Domenico ; Muscogiuri, Giuseppe ; Albrecht, Moritz H. ; Meinel, Felix G. ; Wichmann, Julian L. ; Burchett, Philip F. ; Varga-Szemes, Akos ; Sheafor, Douglas H. ; Hardie, Andrew D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c438t-d71a5ecc83554568a489738adcc888af62833311907a588907da16823857dc993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Accuracy</topic><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Computed Tomography</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Female</topic><topic>Humans</topic><topic>Image acquisition</topic><topic>Image contrast</topic><topic>Image detection</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Image quality</topic><topic>Image reconstruction</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Iodine</topic><topic>Lesions</topic><topic>Liver</topic><topic>Liver - blood supply</topic><topic>Liver - diagnostic imaging</topic><topic>Liver Neoplasms - blood supply</topic><topic>Liver Neoplasms - diagnostic imaging</topic><topic>Magnetic resonance imaging</topic><topic>Male</topic><topic>Mathematical analysis</topic><topic>Medical diagnosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Neuroradiology</topic><topic>Noise</topic><topic>Parenchyma</topic><topic>Prospective Studies</topic><topic>Quality</topic><topic>Quality assessment</topic><topic>Radiography, Dual-Energy Scanned Projection - methods</topic><topic>Radiology</topic><topic>Reproducibility of Results</topic><topic>Sensitivity analysis</topic><topic>Sensitivity and Specificity</topic><topic>Signal-To-Noise Ratio</topic><topic>Tomography, X-Ray Computed - methods</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>De Cecco, Carlo N.</creatorcontrib><creatorcontrib>Caruso, Damiano</creatorcontrib><creatorcontrib>Schoepf, U. Joseph</creatorcontrib><creatorcontrib>De Santis, Domenico</creatorcontrib><creatorcontrib>Muscogiuri, Giuseppe</creatorcontrib><creatorcontrib>Albrecht, Moritz H.</creatorcontrib><creatorcontrib>Meinel, Felix G.</creatorcontrib><creatorcontrib>Wichmann, Julian L.</creatorcontrib><creatorcontrib>Burchett, Philip F.</creatorcontrib><creatorcontrib>Varga-Szemes, Akos</creatorcontrib><creatorcontrib>Sheafor, Douglas H.</creatorcontrib><creatorcontrib>Hardie, Andrew D.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>ProQuest Health & Medical Research Collection</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Health & Nursing</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Applied & Life Sciences</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>De Cecco, Carlo N.</au><au>Caruso, Damiano</au><au>Schoepf, U. Joseph</au><au>De Santis, Domenico</au><au>Muscogiuri, Giuseppe</au><au>Albrecht, Moritz H.</au><au>Meinel, Felix G.</au><au>Wichmann, Julian L.</au><au>Burchett, Philip F.</au><au>Varga-Szemes, Akos</au><au>Sheafor, Douglas H.</au><au>Hardie, Andrew D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2018-08-01</date><risdate>2018</risdate><volume>28</volume><issue>8</issue><spage>3393</spage><epage>3404</epage><pages>3393-3404</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To assess the image quality and diagnostic accuracy of a noise-optimized virtual monoenergetic imaging (VMI+) algorithm compared with standard virtual monoenergetic imaging (VMI) and linearly-blended (M_0.6) reconstructions for the detection of hypervascular liver lesions in dual-energy CT (DECT).
Methods
Thirty patients who underwent clinical liver MRI were prospectively enrolled. Within 60 days of MRI, arterial phase DECT images were acquired on a third-generation dual-source CT and reconstructed with M_0.6, VMI and VMI+ algorithms from 40 to 100 keV in 5-keV intervals. Liver parenchyma and lesion contrast-to-noise-ratios (CNR) were calculated. Two radiologists assessed image quality. Lesion sensitivity, specificity and area under the receiver operating characteristic curves (AUCs) were calculated for the three algorithms with MRI as the reference standard.
Results
VMI+ datasets from 40 to 60 keV provided the highest liver parenchyma and lesion CNR (
p
≤0.021); 50 keV VMI+ provided the highest subjective image quality (4.40±0.54), significantly higher compared to VMI and M_0.6 (all
p
<0.001), and the best diagnostic accuracy in < 1-cm diameter lesions (AUC=0.833 vs. 0.777 and 0.749, respectively;
p
≤0.003).
Conclusions
50-keV VMI+ provides superior image quality and diagnostic accuracy for the detection of hypervascular liver lesions with a diameter < 1cm compared to VMI or M_0.6 reconstructions.
Key Points
• Low-keV VMI+ are characterized by higher contrast resulting from maximum iodine attenuation.
• VMI+ provides superior image quality compared with VMI or M_0.6.
• 50-keV_VMI+ provides higher accuracy for the detection of hypervascular liver lesions < 1cm.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>29460075</pmid><doi>10.1007/s00330-018-5313-6</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-6164-5641</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0938-7994 |
| ispartof | European radiology, 2018-08, Vol.28 (8), p.3393-3404 |
| issn | 0938-7994 1432-1084 |
| language | eng |
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| source | MEDLINE; SpringerLink Journals |
| subjects | Accuracy Adult Aged Algorithms Computed Tomography Diagnostic Radiology Diagnostic systems Female Humans Image acquisition Image contrast Image detection Image Processing, Computer-Assisted - methods Image quality Image reconstruction Imaging Internal Medicine Interventional Radiology Iodine Lesions Liver Liver - blood supply Liver - diagnostic imaging Liver Neoplasms - blood supply Liver Neoplasms - diagnostic imaging Magnetic resonance imaging Male Mathematical analysis Medical diagnosis Medicine Medicine & Public Health Middle Aged Neuroradiology Noise Parenchyma Prospective Studies Quality Quality assessment Radiography, Dual-Energy Scanned Projection - methods Radiology Reproducibility of Results Sensitivity analysis Sensitivity and Specificity Signal-To-Noise Ratio Tomography, X-Ray Computed - methods Ultrasound |
| title | A noise-optimized virtual monoenergetic reconstruction algorithm improves the diagnostic accuracy of late hepatic arterial phase dual-energy CT for the detection of hypervascular liver lesions |
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