Sub-millisievert CT colonography: effect of knowledge-based iterative reconstruction on the detection of colonic polyps
Objectives To assess the feasibility of ultra-low dose computed tomography colonography (CTC) using knowledge-based iterative reconstruction (IR) and to determine its effect on polyp detection. Methods Forty-nine prospectively-enrolled patients underwent ultra-low dose CTC in the supine (100 kVp/20...
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description | Objectives
To assess the feasibility of ultra-low dose computed tomography colonography (CTC) using knowledge-based iterative reconstruction (IR) and to determine its effect on polyp detection.
Methods
Forty-nine prospectively-enrolled patients underwent ultra-low dose CTC in the supine (100 kVp/20 mAs) and prone positions (80 kVp/20 mAs), followed by same-day colonoscopy. Thereafter, images were reconstructed using filtered back projection (FBP) and knowledge-based IR (IMR; Philips Healthcare, Best, Netherlands) algorithms. Effective radiation dose of CTC was recorded. Pooled per-polyp sensitivity and positive predictive value of three radiologists was analysed and compared between FBP and IMR. Image quality was assessed on a five-point scale and image noise was recorded using standard deviations.
Results
Mean effective radiation dose of ultra-low dose CTC was 0.90 ± 0.06 mSv. Eighty-nine polyps were detected on colonoscopy (mean, 8.5 ± 4.7 mm). The pooled per-polyp sensitivity for polyps 6.0-9.9 mm (
n
= 22) on CTC reconstructed with IMR (36/66, 54.5%) was not significantly different with that using FBP algorithm (34/66, 51.5%) (
p
= 0.414). For polyps ≥10 mm (
n
= 35), however, the pooled per-polyp sensitivity on CTC with IMR (73/105, 69.5%) was significantly higher than that with FBP (55/105, 52.4%) (
p
< 0.001). In particular, the difference of per-polyp sensitivity was statistically significant in intermediate (
p
= 0.014) and novice (
p
= 0.003) reviewers. Furthermore, mean image noise of IMR (8.4 ± 6.2 HU) was significantly lower than that of FBP (37.5 ± 13.9 HU) (
p
< 0.001) and image quality with IMR was significantly better than with FBP in all evaluated segments in all reviewers (all
p
s < 0.001).
Conclusions
Sub-mSv CTC reconstructed with IMR was feasible for the detection of clinically significant polyps, demonstrating 70% per-polyp sensitivity of polyps ≥10 mm, while allowing significant noise reduction and improvement in image quality compared with FBP reconstruction.
Key Points
• Sub-mSv CTC using IMR demonstrated 70% per-polyp sensitivity for polyps ≥10 mm.
• CTC using IMR significantly outperformed CTC reconstructed with FBP.
• IMR allows significantly more noise reduction and improvement in image quality than FBP. |
doi_str_mv | 10.1007/s00330-018-5545-5 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2060866555</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2060866555</sourcerecordid><originalsourceid>FETCH-LOGICAL-c438t-d79e9adb763ee0252fad82f7b97bc2c0f63fdfe3b4cd31b648f2d1777ba480d03</originalsourceid><addsrcrecordid>eNp1kU1r3DAQhkVpabZpf0AvRdBLL2pGX5bdW1nSDwjkkORsbGm0ceq1XElO2H9fLd62UCgIBkaP3tHwEPKWw0cOYC4SgJTAgNdMa6WZfkY2XEnBONTqOdlAI2tmmkadkVcpPQBAw5V5Sc5E6dVQyQ15ull6th_GcUgDPmLMdHtLbRjDFHaxm-8Pnyh6jzbT4OmPKTyN6HbI-i6ho0PG2OXhEWlEG6aU42LzECZaTr5H6jDjqeHX0MHSOYyHOb0mL3w3Jnxzqufk7svl7fYbu7r--n37-YpZJevMnGmw6VxvKokIQgvfuVp40zemt8KCr6R3HmWvrJO8r1TthePGmL4rCzqQ5-TDmjvH8HPBlNv9kCyOYzdhWFIroIK6qrTWBX3_D_oQljiV3xVKC60U8KZQfKVsDClF9O0ch30XDy2H9milXa20xUp7tNIek9-dkpd-j-7Pi98aCiBWIJWraYfx7-j_p_4CBQGZ7Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2052544019</pqid></control><display><type>article</type><title>Sub-millisievert CT colonography: effect of knowledge-based iterative reconstruction on the detection of colonic polyps</title><source>MEDLINE</source><source>SpringerNature Journals</source><creator>Kang, Hyo-Jin ; Kim, Se Hyung ; Shin, Cheong-Il ; Joo, Ijin ; Ryu, Hwaseong ; Kim, Sang Gyun ; Im, Jong Pil ; Han, Joon Koo</creator><creatorcontrib>Kang, Hyo-Jin ; Kim, Se Hyung ; Shin, Cheong-Il ; Joo, Ijin ; Ryu, Hwaseong ; Kim, Sang Gyun ; Im, Jong Pil ; Han, Joon Koo</creatorcontrib><description>Objectives
To assess the feasibility of ultra-low dose computed tomography colonography (CTC) using knowledge-based iterative reconstruction (IR) and to determine its effect on polyp detection.
Methods
Forty-nine prospectively-enrolled patients underwent ultra-low dose CTC in the supine (100 kVp/20 mAs) and prone positions (80 kVp/20 mAs), followed by same-day colonoscopy. Thereafter, images were reconstructed using filtered back projection (FBP) and knowledge-based IR (IMR; Philips Healthcare, Best, Netherlands) algorithms. Effective radiation dose of CTC was recorded. Pooled per-polyp sensitivity and positive predictive value of three radiologists was analysed and compared between FBP and IMR. Image quality was assessed on a five-point scale and image noise was recorded using standard deviations.
Results
Mean effective radiation dose of ultra-low dose CTC was 0.90 ± 0.06 mSv. Eighty-nine polyps were detected on colonoscopy (mean, 8.5 ± 4.7 mm). The pooled per-polyp sensitivity for polyps 6.0-9.9 mm (
n
= 22) on CTC reconstructed with IMR (36/66, 54.5%) was not significantly different with that using FBP algorithm (34/66, 51.5%) (
p
= 0.414). For polyps ≥10 mm (
n
= 35), however, the pooled per-polyp sensitivity on CTC with IMR (73/105, 69.5%) was significantly higher than that with FBP (55/105, 52.4%) (
p
< 0.001). In particular, the difference of per-polyp sensitivity was statistically significant in intermediate (
p
= 0.014) and novice (
p
= 0.003) reviewers. Furthermore, mean image noise of IMR (8.4 ± 6.2 HU) was significantly lower than that of FBP (37.5 ± 13.9 HU) (
p
< 0.001) and image quality with IMR was significantly better than with FBP in all evaluated segments in all reviewers (all
p
s < 0.001).
Conclusions
Sub-mSv CTC reconstructed with IMR was feasible for the detection of clinically significant polyps, demonstrating 70% per-polyp sensitivity of polyps ≥10 mm, while allowing significant noise reduction and improvement in image quality compared with FBP reconstruction.
Key Points
• Sub-mSv CTC using IMR demonstrated 70% per-polyp sensitivity for polyps ≥10 mm.
• CTC using IMR significantly outperformed CTC reconstructed with FBP.
• IMR allows significantly more noise reduction and improvement in image quality than FBP.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-018-5545-5</identifier><identifier>PMID: 29948063</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adult ; Aged ; Algorithms ; Clinical Competence ; Colon ; Colonic Polyps - diagnosis ; Colonography, Computed Tomographic - methods ; Colonoscopy ; Computed tomography ; Diagnostic Radiology ; Feasibility studies ; Female ; Gastrointestinal ; Health care ; Humans ; Image filters ; Image quality ; Image reconstruction ; Imaging ; Internal Medicine ; Interventional Radiology ; Iterative methods ; Knowledge base ; Male ; Medical imaging ; Medicine ; Medicine & Public Health ; Middle Aged ; Neuroradiology ; Noise ; Noise reduction ; Polyps ; Quality assessment ; Radiation ; Radiation Dosage ; Radiographic Image Interpretation, Computer-Assisted - methods ; Radiology ; Sensitivity ; Sensitivity analysis ; Statistical analysis ; Ultrasound</subject><ispartof>European radiology, 2018-12, Vol.28 (12), p.5258-5266</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-d79e9adb763ee0252fad82f7b97bc2c0f63fdfe3b4cd31b648f2d1777ba480d03</citedby><cites>FETCH-LOGICAL-c438t-d79e9adb763ee0252fad82f7b97bc2c0f63fdfe3b4cd31b648f2d1777ba480d03</cites><orcidid>0000-0003-1462-9689</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/s00330-018-5545-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-018-5545-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29948063$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kang, Hyo-Jin</creatorcontrib><creatorcontrib>Kim, Se Hyung</creatorcontrib><creatorcontrib>Shin, Cheong-Il</creatorcontrib><creatorcontrib>Joo, Ijin</creatorcontrib><creatorcontrib>Ryu, Hwaseong</creatorcontrib><creatorcontrib>Kim, Sang Gyun</creatorcontrib><creatorcontrib>Im, Jong Pil</creatorcontrib><creatorcontrib>Han, Joon Koo</creatorcontrib><title>Sub-millisievert CT colonography: effect of knowledge-based iterative reconstruction on the detection of colonic polyps</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To assess the feasibility of ultra-low dose computed tomography colonography (CTC) using knowledge-based iterative reconstruction (IR) and to determine its effect on polyp detection.
Methods
Forty-nine prospectively-enrolled patients underwent ultra-low dose CTC in the supine (100 kVp/20 mAs) and prone positions (80 kVp/20 mAs), followed by same-day colonoscopy. Thereafter, images were reconstructed using filtered back projection (FBP) and knowledge-based IR (IMR; Philips Healthcare, Best, Netherlands) algorithms. Effective radiation dose of CTC was recorded. Pooled per-polyp sensitivity and positive predictive value of three radiologists was analysed and compared between FBP and IMR. Image quality was assessed on a five-point scale and image noise was recorded using standard deviations.
Results
Mean effective radiation dose of ultra-low dose CTC was 0.90 ± 0.06 mSv. Eighty-nine polyps were detected on colonoscopy (mean, 8.5 ± 4.7 mm). The pooled per-polyp sensitivity for polyps 6.0-9.9 mm (
n
= 22) on CTC reconstructed with IMR (36/66, 54.5%) was not significantly different with that using FBP algorithm (34/66, 51.5%) (
p
= 0.414). For polyps ≥10 mm (
n
= 35), however, the pooled per-polyp sensitivity on CTC with IMR (73/105, 69.5%) was significantly higher than that with FBP (55/105, 52.4%) (
p
< 0.001). In particular, the difference of per-polyp sensitivity was statistically significant in intermediate (
p
= 0.014) and novice (
p
= 0.003) reviewers. Furthermore, mean image noise of IMR (8.4 ± 6.2 HU) was significantly lower than that of FBP (37.5 ± 13.9 HU) (
p
< 0.001) and image quality with IMR was significantly better than with FBP in all evaluated segments in all reviewers (all
p
s < 0.001).
Conclusions
Sub-mSv CTC reconstructed with IMR was feasible for the detection of clinically significant polyps, demonstrating 70% per-polyp sensitivity of polyps ≥10 mm, while allowing significant noise reduction and improvement in image quality compared with FBP reconstruction.
Key Points
• Sub-mSv CTC using IMR demonstrated 70% per-polyp sensitivity for polyps ≥10 mm.
• CTC using IMR significantly outperformed CTC reconstructed with FBP.
• IMR allows significantly more noise reduction and improvement in image quality than FBP.</description><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Clinical Competence</subject><subject>Colon</subject><subject>Colonic Polyps - diagnosis</subject><subject>Colonography, Computed Tomographic - methods</subject><subject>Colonoscopy</subject><subject>Computed tomography</subject><subject>Diagnostic Radiology</subject><subject>Feasibility studies</subject><subject>Female</subject><subject>Gastrointestinal</subject><subject>Health care</subject><subject>Humans</subject><subject>Image filters</subject><subject>Image quality</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Iterative methods</subject><subject>Knowledge base</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Middle Aged</subject><subject>Neuroradiology</subject><subject>Noise</subject><subject>Noise reduction</subject><subject>Polyps</subject><subject>Quality assessment</subject><subject>Radiation</subject><subject>Radiation Dosage</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Radiology</subject><subject>Sensitivity</subject><subject>Sensitivity analysis</subject><subject>Statistical analysis</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>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kU1r3DAQhkVpabZpf0AvRdBLL2pGX5bdW1nSDwjkkORsbGm0ceq1XElO2H9fLd62UCgIBkaP3tHwEPKWw0cOYC4SgJTAgNdMa6WZfkY2XEnBONTqOdlAI2tmmkadkVcpPQBAw5V5Sc5E6dVQyQ15ull6th_GcUgDPmLMdHtLbRjDFHaxm-8Pnyh6jzbT4OmPKTyN6HbI-i6ho0PG2OXhEWlEG6aU42LzECZaTr5H6jDjqeHX0MHSOYyHOb0mL3w3Jnxzqufk7svl7fYbu7r--n37-YpZJevMnGmw6VxvKokIQgvfuVp40zemt8KCr6R3HmWvrJO8r1TthePGmL4rCzqQ5-TDmjvH8HPBlNv9kCyOYzdhWFIroIK6qrTWBX3_D_oQljiV3xVKC60U8KZQfKVsDClF9O0ch30XDy2H9milXa20xUp7tNIek9-dkpd-j-7Pi98aCiBWIJWraYfx7-j_p_4CBQGZ7Q</recordid><startdate>20181201</startdate><enddate>20181201</enddate><creator>Kang, Hyo-Jin</creator><creator>Kim, Se Hyung</creator><creator>Shin, Cheong-Il</creator><creator>Joo, Ijin</creator><creator>Ryu, Hwaseong</creator><creator>Kim, Sang Gyun</creator><creator>Im, Jong Pil</creator><creator>Han, Joon Koo</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>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1462-9689</orcidid></search><sort><creationdate>20181201</creationdate><title>Sub-millisievert CT colonography: effect of knowledge-based iterative reconstruction on the detection of colonic polyps</title><author>Kang, Hyo-Jin ; Kim, Se Hyung ; Shin, Cheong-Il ; Joo, Ijin ; Ryu, Hwaseong ; Kim, Sang Gyun ; Im, Jong Pil ; Han, Joon Koo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c438t-d79e9adb763ee0252fad82f7b97bc2c0f63fdfe3b4cd31b648f2d1777ba480d03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Clinical Competence</topic><topic>Colon</topic><topic>Colonic Polyps - diagnosis</topic><topic>Colonography, Computed Tomographic - methods</topic><topic>Colonoscopy</topic><topic>Computed tomography</topic><topic>Diagnostic Radiology</topic><topic>Feasibility studies</topic><topic>Female</topic><topic>Gastrointestinal</topic><topic>Health care</topic><topic>Humans</topic><topic>Image filters</topic><topic>Image quality</topic><topic>Image reconstruction</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Iterative methods</topic><topic>Knowledge base</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Middle Aged</topic><topic>Neuroradiology</topic><topic>Noise</topic><topic>Noise reduction</topic><topic>Polyps</topic><topic>Quality assessment</topic><topic>Radiation</topic><topic>Radiation Dosage</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Radiology</topic><topic>Sensitivity</topic><topic>Sensitivity analysis</topic><topic>Statistical analysis</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kang, Hyo-Jin</creatorcontrib><creatorcontrib>Kim, Se Hyung</creatorcontrib><creatorcontrib>Shin, Cheong-Il</creatorcontrib><creatorcontrib>Joo, Ijin</creatorcontrib><creatorcontrib>Ryu, Hwaseong</creatorcontrib><creatorcontrib>Kim, Sang Gyun</creatorcontrib><creatorcontrib>Im, Jong Pil</creatorcontrib><creatorcontrib>Han, Joon Koo</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 (ProQuest)</collection><collection>Natural Science Collection (ProQuest)</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 One Academic Eastern Edition (DO NOT USE)</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>Kang, Hyo-Jin</au><au>Kim, Se Hyung</au><au>Shin, Cheong-Il</au><au>Joo, Ijin</au><au>Ryu, Hwaseong</au><au>Kim, Sang Gyun</au><au>Im, Jong Pil</au><au>Han, Joon Koo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sub-millisievert CT colonography: effect of knowledge-based iterative reconstruction on the detection of colonic polyps</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2018-12-01</date><risdate>2018</risdate><volume>28</volume><issue>12</issue><spage>5258</spage><epage>5266</epage><pages>5258-5266</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To assess the feasibility of ultra-low dose computed tomography colonography (CTC) using knowledge-based iterative reconstruction (IR) and to determine its effect on polyp detection.
Methods
Forty-nine prospectively-enrolled patients underwent ultra-low dose CTC in the supine (100 kVp/20 mAs) and prone positions (80 kVp/20 mAs), followed by same-day colonoscopy. Thereafter, images were reconstructed using filtered back projection (FBP) and knowledge-based IR (IMR; Philips Healthcare, Best, Netherlands) algorithms. Effective radiation dose of CTC was recorded. Pooled per-polyp sensitivity and positive predictive value of three radiologists was analysed and compared between FBP and IMR. Image quality was assessed on a five-point scale and image noise was recorded using standard deviations.
Results
Mean effective radiation dose of ultra-low dose CTC was 0.90 ± 0.06 mSv. Eighty-nine polyps were detected on colonoscopy (mean, 8.5 ± 4.7 mm). The pooled per-polyp sensitivity for polyps 6.0-9.9 mm (
n
= 22) on CTC reconstructed with IMR (36/66, 54.5%) was not significantly different with that using FBP algorithm (34/66, 51.5%) (
p
= 0.414). For polyps ≥10 mm (
n
= 35), however, the pooled per-polyp sensitivity on CTC with IMR (73/105, 69.5%) was significantly higher than that with FBP (55/105, 52.4%) (
p
< 0.001). In particular, the difference of per-polyp sensitivity was statistically significant in intermediate (
p
= 0.014) and novice (
p
= 0.003) reviewers. Furthermore, mean image noise of IMR (8.4 ± 6.2 HU) was significantly lower than that of FBP (37.5 ± 13.9 HU) (
p
< 0.001) and image quality with IMR was significantly better than with FBP in all evaluated segments in all reviewers (all
p
s < 0.001).
Conclusions
Sub-mSv CTC reconstructed with IMR was feasible for the detection of clinically significant polyps, demonstrating 70% per-polyp sensitivity of polyps ≥10 mm, while allowing significant noise reduction and improvement in image quality compared with FBP reconstruction.
Key Points
• Sub-mSv CTC using IMR demonstrated 70% per-polyp sensitivity for polyps ≥10 mm.
• CTC using IMR significantly outperformed CTC reconstructed with FBP.
• IMR allows significantly more noise reduction and improvement in image quality than FBP.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>29948063</pmid><doi>10.1007/s00330-018-5545-5</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-1462-9689</orcidid></addata></record> |
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subjects | Adult Aged Algorithms Clinical Competence Colon Colonic Polyps - diagnosis Colonography, Computed Tomographic - methods Colonoscopy Computed tomography Diagnostic Radiology Feasibility studies Female Gastrointestinal Health care Humans Image filters Image quality Image reconstruction Imaging Internal Medicine Interventional Radiology Iterative methods Knowledge base Male Medical imaging Medicine Medicine & Public Health Middle Aged Neuroradiology Noise Noise reduction Polyps Quality assessment Radiation Radiation Dosage Radiographic Image Interpretation, Computer-Assisted - methods Radiology Sensitivity Sensitivity analysis Statistical analysis Ultrasound |
title | Sub-millisievert CT colonography: effect of knowledge-based iterative reconstruction on the detection of colonic polyps |
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