Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique
Objectives To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning–based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT). Methods From December 2016 to May 2...
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| Veröffentlicht in: | European radiology 2021-04, Vol.31 (4), p.2218-2226 |
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| creator | Lee, Seunghyun Choi, Young Hun Cho, Yeon Jin Lee, Seul Bi Cheon, Jung-Eun Kim, Woo Sun Ahn, Chul Kyun Kim, Jong Hyo |
| description | Objectives
To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning–based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT).
Methods
From December 2016 to May 2017, DECT with 300 mg•I/mL contrast medium was performed in 29 pediatric patients (17 boys, 12 girls; age, 2–19 years). The DECT images were reconstructed using a noise-optimized virtual monoenergetic reconstruction image (VMI) with and without a deep learning method. SECT images with 350 mg•I/mL contrast medium, performed within the last 3 months before the DECT, served as reference images. The quantitative and qualitative parameters were compared using paired
t
tests and Wilcoxon signed-rank tests, and the differences in radiation dose and total iodine administration were assessed.
Results
The linearly blended DECT showed lower attenuation and higher noise than SECT. The 60-keV VMI showed an increase in attenuation and higher noise than SECT. The combined 60-keV VMI plus deep learning images showed low noise, no difference in contrast-to-noise ratios, and overall image quality or diagnostic image quality, but showed a higher signal-to-noise ratio in the liver and lower enhancement of lesions than SECT. The overall image and diagnostic quality of lesions were maintained on the combined noise reduction approach. The CT dose index volume and total iodine administration in DECT were respectively 19.6% and 14.3% lower than those in SECT.
Conclusion
Low iodine concentration DECT, combined with deep learning in pediatric abdominal CT, can maintain image quality while reducing the radiation dose and iodine load, compared with standard SECT.
Key Points
•
An image noise reduction approach combining deep learning and noise-optimized virtual monoenergetic image reconstruction can maintain image quality while reducing radiation dose and iodine load.
•
The 60-keV virtual monoenergetic image reconstruction plus deep learning images showed low noise, no difference in contrast-to-noise ratio, and overall image quality, but showed a higher signal-to-noise ratio in the liver and a lower enhancement of lesion than single-energy polychromatic CT.
•
This combination could offer a 19.6% reduction in radiation dose and a 14.3% reduction in iodine load, in comparison with a control group that underwent single-energy polychromatic CT with the standard protocol. |
| doi_str_mv | 10.1007/s00330-020-07349-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2449263019</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2503048556</sourcerecordid><originalsourceid>FETCH-LOGICAL-c375t-ff240949d7ee56f17e87f024823933d3438c7d51cc4f03b0626b2ad54116589e3</originalsourceid><addsrcrecordid>eNp9kc1u1TAQhS0EorctL8ACWWLDJjD-i-MluioUqWo37dpy7Mmtq8QJdrLo2-P2FpBYsBiNRvPNGc0cQt4z-MwA9JcCIAQ0wGtoIU1jXpEdk4I3DDr5muzAiK7RxsgTclrKAwAYJvVbclLHBCgtdiRcz7EgzRg2v8Y5UbcseXb-nsZEFwzRrTl66vowTzG5ke5vqZ-nPqaYDjQgLnREl58rlwINmxsbTJgPj3RFf5_izw3PyZvBjQXfveQzcvft4nZ_2VzdfP-x_3rVeKHV2gwDl2CkCRpRtQPT2OkBuOy4MEIEIUXndVDMezmA6KHlbc9dUJKxVnUGxRn5dNStJ9S1ZbVTLB7H0SWct2K5lIa3Apip6Md_0Id5y_XASqn6HNkp1VaKHymf51IyDnbJcXL50TKwTx7Yowe2emCfPbBP0h9epLd-wvBn5PfTKyCOQKmtdMD8d_d_ZH8BUIaQpA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2503048556</pqid></control><display><type>article</type><title>Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Lee, Seunghyun ; Choi, Young Hun ; Cho, Yeon Jin ; Lee, Seul Bi ; Cheon, Jung-Eun ; Kim, Woo Sun ; Ahn, Chul Kyun ; Kim, Jong Hyo</creator><creatorcontrib>Lee, Seunghyun ; Choi, Young Hun ; Cho, Yeon Jin ; Lee, Seul Bi ; Cheon, Jung-Eun ; Kim, Woo Sun ; Ahn, Chul Kyun ; Kim, Jong Hyo</creatorcontrib><description>Objectives
To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning–based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT).
Methods
From December 2016 to May 2017, DECT with 300 mg•I/mL contrast medium was performed in 29 pediatric patients (17 boys, 12 girls; age, 2–19 years). The DECT images were reconstructed using a noise-optimized virtual monoenergetic reconstruction image (VMI) with and without a deep learning method. SECT images with 350 mg•I/mL contrast medium, performed within the last 3 months before the DECT, served as reference images. The quantitative and qualitative parameters were compared using paired
t
tests and Wilcoxon signed-rank tests, and the differences in radiation dose and total iodine administration were assessed.
Results
The linearly blended DECT showed lower attenuation and higher noise than SECT. The 60-keV VMI showed an increase in attenuation and higher noise than SECT. The combined 60-keV VMI plus deep learning images showed low noise, no difference in contrast-to-noise ratios, and overall image quality or diagnostic image quality, but showed a higher signal-to-noise ratio in the liver and lower enhancement of lesions than SECT. The overall image and diagnostic quality of lesions were maintained on the combined noise reduction approach. The CT dose index volume and total iodine administration in DECT were respectively 19.6% and 14.3% lower than those in SECT.
Conclusion
Low iodine concentration DECT, combined with deep learning in pediatric abdominal CT, can maintain image quality while reducing the radiation dose and iodine load, compared with standard SECT.
Key Points
•
An image noise reduction approach combining deep learning and noise-optimized virtual monoenergetic image reconstruction can maintain image quality while reducing radiation dose and iodine load.
•
The 60-keV virtual monoenergetic image reconstruction plus deep learning images showed low noise, no difference in contrast-to-noise ratio, and overall image quality, but showed a higher signal-to-noise ratio in the liver and a lower enhancement of lesion than single-energy polychromatic CT.
•
This combination could offer a 19.6% reduction in radiation dose and a 14.3% reduction in iodine load, in comparison with a control group that underwent single-energy polychromatic CT with the standard protocol.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-020-07349-9</identifier><identifier>PMID: 33030573</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Abdomen ; Adolescent ; Adult ; Attenuation ; Child ; Child, Preschool ; Computed tomography ; Contrast Media ; Deep Learning ; Diagnostic Radiology ; Diagnostic systems ; Energy ; Female ; Humans ; Image contrast ; Image enhancement ; Image processing ; Image quality ; Image reconstruction ; Imaging ; Internal Medicine ; Interventional Radiology ; Iodine ; Lesions ; Liver ; Low noise ; Male ; Medical imaging ; Medicine ; Medicine & Public Health ; Neuroradiology ; Noise reduction ; Noise standards ; Paediatric ; Pediatrics ; Radiation ; Radiation dosage ; Radiography, Dual-Energy Scanned Projection ; Radiology ; Rank tests ; Retrospective Studies ; Signal to noise ratio ; Tomography, X-Ray Computed ; Ultrasound ; Young Adult</subject><ispartof>European radiology, 2021-04, Vol.31 (4), p.2218-2226</ispartof><rights>European Society of Radiology 2020</rights><rights>European Society of Radiology 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c375t-ff240949d7ee56f17e87f024823933d3438c7d51cc4f03b0626b2ad54116589e3</citedby><cites>FETCH-LOGICAL-c375t-ff240949d7ee56f17e87f024823933d3438c7d51cc4f03b0626b2ad54116589e3</cites><orcidid>0000-0002-1842-9062</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/s00330-020-07349-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-020-07349-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33030573$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Seunghyun</creatorcontrib><creatorcontrib>Choi, Young Hun</creatorcontrib><creatorcontrib>Cho, Yeon Jin</creatorcontrib><creatorcontrib>Lee, Seul Bi</creatorcontrib><creatorcontrib>Cheon, Jung-Eun</creatorcontrib><creatorcontrib>Kim, Woo Sun</creatorcontrib><creatorcontrib>Ahn, Chul Kyun</creatorcontrib><creatorcontrib>Kim, Jong Hyo</creatorcontrib><title>Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning–based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT).
Methods
From December 2016 to May 2017, DECT with 300 mg•I/mL contrast medium was performed in 29 pediatric patients (17 boys, 12 girls; age, 2–19 years). The DECT images were reconstructed using a noise-optimized virtual monoenergetic reconstruction image (VMI) with and without a deep learning method. SECT images with 350 mg•I/mL contrast medium, performed within the last 3 months before the DECT, served as reference images. The quantitative and qualitative parameters were compared using paired
t
tests and Wilcoxon signed-rank tests, and the differences in radiation dose and total iodine administration were assessed.
Results
The linearly blended DECT showed lower attenuation and higher noise than SECT. The 60-keV VMI showed an increase in attenuation and higher noise than SECT. The combined 60-keV VMI plus deep learning images showed low noise, no difference in contrast-to-noise ratios, and overall image quality or diagnostic image quality, but showed a higher signal-to-noise ratio in the liver and lower enhancement of lesions than SECT. The overall image and diagnostic quality of lesions were maintained on the combined noise reduction approach. The CT dose index volume and total iodine administration in DECT were respectively 19.6% and 14.3% lower than those in SECT.
Conclusion
Low iodine concentration DECT, combined with deep learning in pediatric abdominal CT, can maintain image quality while reducing the radiation dose and iodine load, compared with standard SECT.
Key Points
•
An image noise reduction approach combining deep learning and noise-optimized virtual monoenergetic image reconstruction can maintain image quality while reducing radiation dose and iodine load.
•
The 60-keV virtual monoenergetic image reconstruction plus deep learning images showed low noise, no difference in contrast-to-noise ratio, and overall image quality, but showed a higher signal-to-noise ratio in the liver and a lower enhancement of lesion than single-energy polychromatic CT.
•
This combination could offer a 19.6% reduction in radiation dose and a 14.3% reduction in iodine load, in comparison with a control group that underwent single-energy polychromatic CT with the standard protocol.</description><subject>Abdomen</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Attenuation</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>Computed tomography</subject><subject>Contrast Media</subject><subject>Deep Learning</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Energy</subject><subject>Female</subject><subject>Humans</subject><subject>Image contrast</subject><subject>Image enhancement</subject><subject>Image processing</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>Low noise</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neuroradiology</subject><subject>Noise reduction</subject><subject>Noise standards</subject><subject>Paediatric</subject><subject>Pediatrics</subject><subject>Radiation</subject><subject>Radiation dosage</subject><subject>Radiography, Dual-Energy Scanned Projection</subject><subject>Radiology</subject><subject>Rank tests</subject><subject>Retrospective Studies</subject><subject>Signal to noise ratio</subject><subject>Tomography, X-Ray Computed</subject><subject>Ultrasound</subject><subject>Young Adult</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</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>eNp9kc1u1TAQhS0EorctL8ACWWLDJjD-i-MluioUqWo37dpy7Mmtq8QJdrLo2-P2FpBYsBiNRvPNGc0cQt4z-MwA9JcCIAQ0wGtoIU1jXpEdk4I3DDr5muzAiK7RxsgTclrKAwAYJvVbclLHBCgtdiRcz7EgzRg2v8Y5UbcseXb-nsZEFwzRrTl66vowTzG5ke5vqZ-nPqaYDjQgLnREl58rlwINmxsbTJgPj3RFf5_izw3PyZvBjQXfveQzcvft4nZ_2VzdfP-x_3rVeKHV2gwDl2CkCRpRtQPT2OkBuOy4MEIEIUXndVDMezmA6KHlbc9dUJKxVnUGxRn5dNStJ9S1ZbVTLB7H0SWct2K5lIa3Apip6Md_0Id5y_XASqn6HNkp1VaKHymf51IyDnbJcXL50TKwTx7Yowe2emCfPbBP0h9epLd-wvBn5PfTKyCOQKmtdMD8d_d_ZH8BUIaQpA</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Lee, Seunghyun</creator><creator>Choi, Young 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reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique</title><author>Lee, Seunghyun ; Choi, Young Hun ; Cho, Yeon Jin ; Lee, Seul Bi ; Cheon, Jung-Eun ; Kim, Woo Sun ; Ahn, Chul Kyun ; Kim, Jong Hyo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c375t-ff240949d7ee56f17e87f024823933d3438c7d51cc4f03b0626b2ad54116589e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Abdomen</topic><topic>Adolescent</topic><topic>Adult</topic><topic>Attenuation</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>Computed tomography</topic><topic>Contrast Media</topic><topic>Deep Learning</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Energy</topic><topic>Female</topic><topic>Humans</topic><topic>Image contrast</topic><topic>Image enhancement</topic><topic>Image processing</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>Low noise</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neuroradiology</topic><topic>Noise reduction</topic><topic>Noise standards</topic><topic>Paediatric</topic><topic>Pediatrics</topic><topic>Radiation</topic><topic>Radiation dosage</topic><topic>Radiography, Dual-Energy Scanned Projection</topic><topic>Radiology</topic><topic>Rank tests</topic><topic>Retrospective Studies</topic><topic>Signal to noise ratio</topic><topic>Tomography, X-Ray Computed</topic><topic>Ultrasound</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Seunghyun</creatorcontrib><creatorcontrib>Choi, Young Hun</creatorcontrib><creatorcontrib>Cho, Yeon Jin</creatorcontrib><creatorcontrib>Lee, Seul Bi</creatorcontrib><creatorcontrib>Cheon, Jung-Eun</creatorcontrib><creatorcontrib>Kim, Woo Sun</creatorcontrib><creatorcontrib>Ahn, Chul Kyun</creatorcontrib><creatorcontrib>Kim, Jong Hyo</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 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China</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Seunghyun</au><au>Choi, Young Hun</au><au>Cho, Yeon Jin</au><au>Lee, Seul Bi</au><au>Cheon, Jung-Eun</au><au>Kim, Woo Sun</au><au>Ahn, Chul Kyun</au><au>Kim, Jong Hyo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2021-04-01</date><risdate>2021</risdate><volume>31</volume><issue>4</issue><spage>2218</spage><epage>2226</epage><pages>2218-2226</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning–based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychromatic CT (SECT).
Methods
From December 2016 to May 2017, DECT with 300 mg•I/mL contrast medium was performed in 29 pediatric patients (17 boys, 12 girls; age, 2–19 years). The DECT images were reconstructed using a noise-optimized virtual monoenergetic reconstruction image (VMI) with and without a deep learning method. SECT images with 350 mg•I/mL contrast medium, performed within the last 3 months before the DECT, served as reference images. The quantitative and qualitative parameters were compared using paired
t
tests and Wilcoxon signed-rank tests, and the differences in radiation dose and total iodine administration were assessed.
Results
The linearly blended DECT showed lower attenuation and higher noise than SECT. The 60-keV VMI showed an increase in attenuation and higher noise than SECT. The combined 60-keV VMI plus deep learning images showed low noise, no difference in contrast-to-noise ratios, and overall image quality or diagnostic image quality, but showed a higher signal-to-noise ratio in the liver and lower enhancement of lesions than SECT. The overall image and diagnostic quality of lesions were maintained on the combined noise reduction approach. The CT dose index volume and total iodine administration in DECT were respectively 19.6% and 14.3% lower than those in SECT.
Conclusion
Low iodine concentration DECT, combined with deep learning in pediatric abdominal CT, can maintain image quality while reducing the radiation dose and iodine load, compared with standard SECT.
Key Points
•
An image noise reduction approach combining deep learning and noise-optimized virtual monoenergetic image reconstruction can maintain image quality while reducing radiation dose and iodine load.
•
The 60-keV virtual monoenergetic image reconstruction plus deep learning images showed low noise, no difference in contrast-to-noise ratio, and overall image quality, but showed a higher signal-to-noise ratio in the liver and a lower enhancement of lesion than single-energy polychromatic CT.
•
This combination could offer a 19.6% reduction in radiation dose and a 14.3% reduction in iodine load, in comparison with a control group that underwent single-energy polychromatic CT with the standard protocol.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>33030573</pmid><doi>10.1007/s00330-020-07349-9</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-1842-9062</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0938-7994 |
| ispartof | European radiology, 2021-04, Vol.31 (4), p.2218-2226 |
| issn | 0938-7994 1432-1084 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2449263019 |
| source | MEDLINE; SpringerLink Journals |
| subjects | Abdomen Adolescent Adult Attenuation Child Child, Preschool Computed tomography Contrast Media Deep Learning Diagnostic Radiology Diagnostic systems Energy Female Humans Image contrast Image enhancement Image processing Image quality Image reconstruction Imaging Internal Medicine Interventional Radiology Iodine Lesions Liver Low noise Male Medical imaging Medicine Medicine & Public Health Neuroradiology Noise reduction Noise standards Paediatric Pediatrics Radiation Radiation dosage Radiography, Dual-Energy Scanned Projection Radiology Rank tests Retrospective Studies Signal to noise ratio Tomography, X-Ray Computed Ultrasound Young Adult |
| title | Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique |
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