Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages
Objectives To evaluate the impact of model-based iterative reconstruction (MBIR) on image quality and low-contrast lesion detection compared with filtered back projection (FBP) in abdominal computed tomography (CT) of simulated medium and large patients at different tube voltages. Methods A phantom...
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| Veröffentlicht in: | European radiology 2017-12, Vol.27 (12), p.5252-5259 |
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| creator | Euler, André Stieltjes, Bram Szucs-Farkas, Zsolt Eichenberger, Reto Reisinger, Clemens Hirschmann, Anna Zaehringer, Caroline Kircher, Achim Streif, Matthias Bucher, Sabine Buergler, David D’Errico, Luigia Kopp, Sebastién Wilhelm, Markus Schindera, Sebastian T. |
| description | Objectives
To evaluate the impact of model-based iterative reconstruction (MBIR) on image quality and low-contrast lesion detection compared with filtered back projection (FBP) in abdominal computed tomography (CT) of simulated medium and large patients at different tube voltages.
Methods
A phantom with 45 hypoattenuating lesions was placed in two water containers and scanned at 70, 80, 100, and 120 kVp. The 120-kVp protocol served as reference, and the volume CT dose index (CTDI
vol
) was kept constant for all protocols. The datasets were reconstructed with MBIR and FBP. Image noise and contrast-to-noise-ratio (CNR) were assessed. Low-contrast lesion detectability was evaluated by 12 radiologists.
Results
MBIR decreased the image noise by 24% and 27%, and increased the CNR by 30% and 29% for the medium and large phantoms, respectively. Lower tube voltages increased the CNR by 58%, 46%, and 16% at 70, 80, and 100 kVp, respectively, compared with 120 kVp in the medium phantom and by 9%, 18% and 12% in the large phantom. No significant difference in lesion detection rate was observed (medium: 79-82%; large: 57-65%;
P
> 0.37).
Conclusions
Although MBIR improved quantitative image quality compared with FBP, it did not result in increased low-contrast lesion detection in abdominal CT at different tube voltages in simulated medium and large patients.
Key Points
•
MBIR improved quantitative image quality but not lesion detection compared with FBP
.
•
Increased CNR by low tube voltages did not improve lesion detection
.
•
Changes in image noise and CNR do not directly influence diagnostic accuracy
. |
| doi_str_mv | 10.1007/s00330-017-4825-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1884169783</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1884169783</sourcerecordid><originalsourceid>FETCH-LOGICAL-c372t-6d85b54c3f30dee59d254594cfb2c85e9665750cc3e06b0edb7723e505d1af843</originalsourceid><addsrcrecordid>eNp1kcGKFDEQhhtR3NnVB_AiAS9eopVO0p14k0HdhQUv67lJJ9W7PaY7s0l6l3lSX8cMPSsiCIFA1Vf1_9RfVW8YfGAA7ccEwDlQYC0VqpZUP6s2TPCaMlDiebUBzRVttRZn1XlKOwDQTLQvq7Na8VaAgk3162raG5tJGMgUHHram4SOjBmjyeMDkog2zCnHxeYxzKQ8Hx5pqeVoUiYe07HsMOMKmLlMT-YWyf1i_JgPZCzF3oVpnI0n25tPxBBW04jGYTzJ2VBcnAT3d2bOYSIpL-5AHsd8R4bRFz-F6439SfYx7J7EMnHjMJTenEleeiQPwecinl5VLwbjE74-_RfVj69fbraX9Pr7t6vt52tqeVtn2jgleyksHzg4RKldLYXUwg59bZVE3TSylWAtR2h6QNe3bc1RgnTMDErwi-r9ure4ul8w5W4ak0XvzYxhSR1TSrBGt4oX9N0_6C4ssRylULphUCumVaHYStkYUoo4dPtY7hkPHYPumHq3pt6V1Ltj6p0uM29Pm5d-Qvdn4inmAtQrkEprvsX4l_R_t_4GU6O8Ug</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1961028198</pqid></control><display><type>article</type><title>Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Euler, André ; Stieltjes, Bram ; Szucs-Farkas, Zsolt ; Eichenberger, Reto ; Reisinger, Clemens ; Hirschmann, Anna ; Zaehringer, Caroline ; Kircher, Achim ; Streif, Matthias ; Bucher, Sabine ; Buergler, David ; D’Errico, Luigia ; Kopp, Sebastién ; Wilhelm, Markus ; Schindera, Sebastian T.</creator><creatorcontrib>Euler, André ; Stieltjes, Bram ; Szucs-Farkas, Zsolt ; Eichenberger, Reto ; Reisinger, Clemens ; Hirschmann, Anna ; Zaehringer, Caroline ; Kircher, Achim ; Streif, Matthias ; Bucher, Sabine ; Buergler, David ; D’Errico, Luigia ; Kopp, Sebastién ; Wilhelm, Markus ; Schindera, Sebastian T.</creatorcontrib><description>Objectives
To evaluate the impact of model-based iterative reconstruction (MBIR) on image quality and low-contrast lesion detection compared with filtered back projection (FBP) in abdominal computed tomography (CT) of simulated medium and large patients at different tube voltages.
Methods
A phantom with 45 hypoattenuating lesions was placed in two water containers and scanned at 70, 80, 100, and 120 kVp. The 120-kVp protocol served as reference, and the volume CT dose index (CTDI
vol
) was kept constant for all protocols. The datasets were reconstructed with MBIR and FBP. Image noise and contrast-to-noise-ratio (CNR) were assessed. Low-contrast lesion detectability was evaluated by 12 radiologists.
Results
MBIR decreased the image noise by 24% and 27%, and increased the CNR by 30% and 29% for the medium and large phantoms, respectively. Lower tube voltages increased the CNR by 58%, 46%, and 16% at 70, 80, and 100 kVp, respectively, compared with 120 kVp in the medium phantom and by 9%, 18% and 12% in the large phantom. No significant difference in lesion detection rate was observed (medium: 79-82%; large: 57-65%;
P
> 0.37).
Conclusions
Although MBIR improved quantitative image quality compared with FBP, it did not result in increased low-contrast lesion detection in abdominal CT at different tube voltages in simulated medium and large patients.
Key Points
•
MBIR improved quantitative image quality but not lesion detection compared with FBP
.
•
Increased CNR by low tube voltages did not improve lesion detection
.
•
Changes in image noise and CNR do not directly influence diagnostic accuracy
.</description><identifier>ISSN: 0938-7994</identifier><identifier>EISSN: 1432-1084</identifier><identifier>DOI: 10.1007/s00330-017-4825-9</identifier><identifier>PMID: 28374080</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Abdomen ; Algorithms ; Change detection ; Computed Tomography ; Computer simulation ; Containers ; Diagnostic Radiology ; Diagnostic systems ; Humans ; Image contrast ; Image detection ; Image processing ; Image quality ; Image reconstruction ; Imaging ; Internal Medicine ; Interventional Radiology ; Iterative methods ; Lesions ; Medicine ; Medicine & Public Health ; Neuroradiology ; Noise ; Patients ; Phantoms, Imaging ; Radiation Dosage ; Radiographic Image Interpretation, Computer-Assisted - methods ; Radiology ; Tomography, X-Ray Computed - standards ; Ultrasound</subject><ispartof>European radiology, 2017-12, Vol.27 (12), p.5252-5259</ispartof><rights>European Society of Radiology 2017. corrected publication August 2017</rights><rights>European Radiology is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-6d85b54c3f30dee59d254594cfb2c85e9665750cc3e06b0edb7723e505d1af843</citedby><cites>FETCH-LOGICAL-c372t-6d85b54c3f30dee59d254594cfb2c85e9665750cc3e06b0edb7723e505d1af843</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00330-017-4825-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00330-017-4825-9$$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/28374080$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Euler, André</creatorcontrib><creatorcontrib>Stieltjes, Bram</creatorcontrib><creatorcontrib>Szucs-Farkas, Zsolt</creatorcontrib><creatorcontrib>Eichenberger, Reto</creatorcontrib><creatorcontrib>Reisinger, Clemens</creatorcontrib><creatorcontrib>Hirschmann, Anna</creatorcontrib><creatorcontrib>Zaehringer, Caroline</creatorcontrib><creatorcontrib>Kircher, Achim</creatorcontrib><creatorcontrib>Streif, Matthias</creatorcontrib><creatorcontrib>Bucher, Sabine</creatorcontrib><creatorcontrib>Buergler, David</creatorcontrib><creatorcontrib>D’Errico, Luigia</creatorcontrib><creatorcontrib>Kopp, Sebastién</creatorcontrib><creatorcontrib>Wilhelm, Markus</creatorcontrib><creatorcontrib>Schindera, Sebastian T.</creatorcontrib><title>Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages</title><title>European radiology</title><addtitle>Eur Radiol</addtitle><addtitle>Eur Radiol</addtitle><description>Objectives
To evaluate the impact of model-based iterative reconstruction (MBIR) on image quality and low-contrast lesion detection compared with filtered back projection (FBP) in abdominal computed tomography (CT) of simulated medium and large patients at different tube voltages.
Methods
A phantom with 45 hypoattenuating lesions was placed in two water containers and scanned at 70, 80, 100, and 120 kVp. The 120-kVp protocol served as reference, and the volume CT dose index (CTDI
vol
) was kept constant for all protocols. The datasets were reconstructed with MBIR and FBP. Image noise and contrast-to-noise-ratio (CNR) were assessed. Low-contrast lesion detectability was evaluated by 12 radiologists.
Results
MBIR decreased the image noise by 24% and 27%, and increased the CNR by 30% and 29% for the medium and large phantoms, respectively. Lower tube voltages increased the CNR by 58%, 46%, and 16% at 70, 80, and 100 kVp, respectively, compared with 120 kVp in the medium phantom and by 9%, 18% and 12% in the large phantom. No significant difference in lesion detection rate was observed (medium: 79-82%; large: 57-65%;
P
> 0.37).
Conclusions
Although MBIR improved quantitative image quality compared with FBP, it did not result in increased low-contrast lesion detection in abdominal CT at different tube voltages in simulated medium and large patients.
Key Points
•
MBIR improved quantitative image quality but not lesion detection compared with FBP
.
•
Increased CNR by low tube voltages did not improve lesion detection
.
•
Changes in image noise and CNR do not directly influence diagnostic accuracy
.</description><subject>Abdomen</subject><subject>Algorithms</subject><subject>Change detection</subject><subject>Computed Tomography</subject><subject>Computer simulation</subject><subject>Containers</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Humans</subject><subject>Image contrast</subject><subject>Image detection</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>Iterative methods</subject><subject>Lesions</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Neuroradiology</subject><subject>Noise</subject><subject>Patients</subject><subject>Phantoms, Imaging</subject><subject>Radiation Dosage</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Radiology</subject><subject>Tomography, X-Ray Computed - standards</subject><subject>Ultrasound</subject><issn>0938-7994</issn><issn>1432-1084</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</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>eNp1kcGKFDEQhhtR3NnVB_AiAS9eopVO0p14k0HdhQUv67lJJ9W7PaY7s0l6l3lSX8cMPSsiCIFA1Vf1_9RfVW8YfGAA7ccEwDlQYC0VqpZUP6s2TPCaMlDiebUBzRVttRZn1XlKOwDQTLQvq7Na8VaAgk3162raG5tJGMgUHHram4SOjBmjyeMDkog2zCnHxeYxzKQ8Hx5pqeVoUiYe07HsMOMKmLlMT-YWyf1i_JgPZCzF3oVpnI0n25tPxBBW04jGYTzJ2VBcnAT3d2bOYSIpL-5AHsd8R4bRFz-F6439SfYx7J7EMnHjMJTenEleeiQPwecinl5VLwbjE74-_RfVj69fbraX9Pr7t6vt52tqeVtn2jgleyksHzg4RKldLYXUwg59bZVE3TSylWAtR2h6QNe3bc1RgnTMDErwi-r9ure4ul8w5W4ak0XvzYxhSR1TSrBGt4oX9N0_6C4ssRylULphUCumVaHYStkYUoo4dPtY7hkPHYPumHq3pt6V1Ltj6p0uM29Pm5d-Qvdn4inmAtQrkEprvsX4l_R_t_4GU6O8Ug</recordid><startdate>20171201</startdate><enddate>20171201</enddate><creator>Euler, André</creator><creator>Stieltjes, Bram</creator><creator>Szucs-Farkas, Zsolt</creator><creator>Eichenberger, Reto</creator><creator>Reisinger, Clemens</creator><creator>Hirschmann, Anna</creator><creator>Zaehringer, Caroline</creator><creator>Kircher, Achim</creator><creator>Streif, Matthias</creator><creator>Bucher, Sabine</creator><creator>Buergler, David</creator><creator>D’Errico, Luigia</creator><creator>Kopp, Sebastién</creator><creator>Wilhelm, Markus</creator><creator>Schindera, Sebastian T.</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>7X8</scope></search><sort><creationdate>20171201</creationdate><title>Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages</title><author>Euler, André ; Stieltjes, Bram ; Szucs-Farkas, Zsolt ; Eichenberger, Reto ; Reisinger, Clemens ; Hirschmann, Anna ; Zaehringer, Caroline ; Kircher, Achim ; Streif, Matthias ; Bucher, Sabine ; Buergler, David ; D’Errico, Luigia ; Kopp, Sebastién ; Wilhelm, Markus ; Schindera, Sebastian T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-6d85b54c3f30dee59d254594cfb2c85e9665750cc3e06b0edb7723e505d1af843</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Abdomen</topic><topic>Algorithms</topic><topic>Change detection</topic><topic>Computed Tomography</topic><topic>Computer simulation</topic><topic>Containers</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Humans</topic><topic>Image contrast</topic><topic>Image detection</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>Iterative methods</topic><topic>Lesions</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Neuroradiology</topic><topic>Noise</topic><topic>Patients</topic><topic>Phantoms, Imaging</topic><topic>Radiation Dosage</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Radiology</topic><topic>Tomography, X-Ray Computed - standards</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Euler, André</creatorcontrib><creatorcontrib>Stieltjes, Bram</creatorcontrib><creatorcontrib>Szucs-Farkas, Zsolt</creatorcontrib><creatorcontrib>Eichenberger, Reto</creatorcontrib><creatorcontrib>Reisinger, Clemens</creatorcontrib><creatorcontrib>Hirschmann, Anna</creatorcontrib><creatorcontrib>Zaehringer, Caroline</creatorcontrib><creatorcontrib>Kircher, Achim</creatorcontrib><creatorcontrib>Streif, Matthias</creatorcontrib><creatorcontrib>Bucher, Sabine</creatorcontrib><creatorcontrib>Buergler, David</creatorcontrib><creatorcontrib>D’Errico, Luigia</creatorcontrib><creatorcontrib>Kopp, Sebastién</creatorcontrib><creatorcontrib>Wilhelm, Markus</creatorcontrib><creatorcontrib>Schindera, Sebastian T.</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 One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><jtitle>European radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Euler, André</au><au>Stieltjes, Bram</au><au>Szucs-Farkas, Zsolt</au><au>Eichenberger, Reto</au><au>Reisinger, Clemens</au><au>Hirschmann, Anna</au><au>Zaehringer, Caroline</au><au>Kircher, Achim</au><au>Streif, Matthias</au><au>Bucher, Sabine</au><au>Buergler, David</au><au>D’Errico, Luigia</au><au>Kopp, Sebastién</au><au>Wilhelm, Markus</au><au>Schindera, Sebastian T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages</atitle><jtitle>European radiology</jtitle><stitle>Eur Radiol</stitle><addtitle>Eur Radiol</addtitle><date>2017-12-01</date><risdate>2017</risdate><volume>27</volume><issue>12</issue><spage>5252</spage><epage>5259</epage><pages>5252-5259</pages><issn>0938-7994</issn><eissn>1432-1084</eissn><abstract>Objectives
To evaluate the impact of model-based iterative reconstruction (MBIR) on image quality and low-contrast lesion detection compared with filtered back projection (FBP) in abdominal computed tomography (CT) of simulated medium and large patients at different tube voltages.
Methods
A phantom with 45 hypoattenuating lesions was placed in two water containers and scanned at 70, 80, 100, and 120 kVp. The 120-kVp protocol served as reference, and the volume CT dose index (CTDI
vol
) was kept constant for all protocols. The datasets were reconstructed with MBIR and FBP. Image noise and contrast-to-noise-ratio (CNR) were assessed. Low-contrast lesion detectability was evaluated by 12 radiologists.
Results
MBIR decreased the image noise by 24% and 27%, and increased the CNR by 30% and 29% for the medium and large phantoms, respectively. Lower tube voltages increased the CNR by 58%, 46%, and 16% at 70, 80, and 100 kVp, respectively, compared with 120 kVp in the medium phantom and by 9%, 18% and 12% in the large phantom. No significant difference in lesion detection rate was observed (medium: 79-82%; large: 57-65%;
P
> 0.37).
Conclusions
Although MBIR improved quantitative image quality compared with FBP, it did not result in increased low-contrast lesion detection in abdominal CT at different tube voltages in simulated medium and large patients.
Key Points
•
MBIR improved quantitative image quality but not lesion detection compared with FBP
.
•
Increased CNR by low tube voltages did not improve lesion detection
.
•
Changes in image noise and CNR do not directly influence diagnostic accuracy
.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>28374080</pmid><doi>10.1007/s00330-017-4825-9</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
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| ispartof | European radiology, 2017-12, Vol.27 (12), p.5252-5259 |
| issn | 0938-7994 1432-1084 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1884169783 |
| source | MEDLINE; SpringerLink Journals |
| subjects | Abdomen Algorithms Change detection Computed Tomography Computer simulation Containers Diagnostic Radiology Diagnostic systems Humans Image contrast Image detection Image processing Image quality Image reconstruction Imaging Internal Medicine Interventional Radiology Iterative methods Lesions Medicine Medicine & Public Health Neuroradiology Noise Patients Phantoms, Imaging Radiation Dosage Radiographic Image Interpretation, Computer-Assisted - methods Radiology Tomography, X-Ray Computed - standards Ultrasound |
| title | Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages |
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