Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images
Objective To investigate the effect of a deep learning-based denoising algorithm, PixelShine (PS), on the quality of 70 kVp pelvic arterial phase CT images. Materials and methods A retrospective analysis was performed on arterial phase pelvic CT images from 33 patients (body-mass index ≤ 20 kg/m 2 )...
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| Veröffentlicht in: | Japanese journal of radiology 2019-02, Vol.37 (2), p.186-190 |
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| creator | Tian, Shi-feng Liu, Ai-lian Liu, Jing-hong Liu, Yi-jun Pan, Ju-dong |
| description | Objective
To investigate the effect of a deep learning-based denoising algorithm, PixelShine (PS), on the quality of 70 kVp pelvic arterial phase CT images.
Materials and methods
A retrospective analysis was performed on arterial phase pelvic CT images from 33 patients (body-mass index ≤ 20 kg/m
2
) obtained with a GE Revolution CT (70 kVp tube voltage; adaptive statistical iterative reconstruction-Veo-filtered back projection, 50% blending) and designated group A. Group B images were then obtained by applying PS to group A image datasets. Subjective image quality was evaluated by two radiologists with a 5-point scoring system; the scores of the groups were compared. Image signal was assessed using CT values of the urinary bladder. CT and standard deviation (SD) values of the gluteus maximus were measured, and SD values of the gluteus maximus were used to represent image noise. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the bladder were calculated. Image noise, SNR, and CNR of two groups were compared using paired t-tests.
Results
The subjective visual image quality scores of groups A and B, respectively, were 3.11 ± 0.30 vs. 3.82 ± 0.57; image noise was 15.79 ± 2.05 Hounsfield units (HU) vs. 11.06 ± 2.22 HU; SNRs of bladder were 0.50 ± 0.23 vs. 0.79 ± 0.39; and CNRs of bladder were 3.72 ± 0.85 vs. 5.14 ± 1.27. Group B showed better subjective image quality, lower image noise, and improved SNR and CNR, compared to group A; these differences were statistically significant (
P
|
| doi_str_mv | 10.1007/s11604-018-0798-0 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2155929074</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2155929074</sourcerecordid><originalsourceid>FETCH-LOGICAL-c462t-88f5efdbeb072142cece01876e204ac87a72a8936395f7b7878ebc9e365a9a323</originalsourceid><addsrcrecordid>eNp1kV1rFDEUhoMotlZ_gDcS8EaQ0SSTmSSXZakfULDYWrwLmeyZ3dRsMk0yxf6L_uRm2FpB8CYncJ7zno8XodeUfKCEiI-Z0p7whlDZEKHq8wQdUtmLhhL58-njX9AD9CLnK0J63nL-HB20pGMtV-oQ3Z3FAqE44_GN8TPgOOKyBXzmfoM_37oAeA0wYQ8mBRc22PhNTK5sd3iMCbtgE5i8JK5n4125XQQEwb8up_fH5-57c4kT2BhySbMtLgY8gb9xFptUIC1tp63JgFcX2O3MBvJL9Gw0PsOrh3iEfnw6uVh9aU6_ff66Oj5tLO9ZaaQcOxjXAwxEMMqZBQv1DqIHRrixUhjBjFRt36puFIOQQsJgFbR9Z5RpWXuE3u11pxSvZ8hF71y24L0JEOesGe06xRQRvKJv_0Gv4pxCnW6hiOKcU1EpuqdsijknGPWU6krpVlOiF7v03i5dx9SLXZrUmjcPyvOwg_VjxR9_KsD2QK6psIH0t_X_Ve8BA5Wg5A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2150944417</pqid></control><display><type>article</type><title>Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images</title><source>SpringerNature Journals</source><creator>Tian, Shi-feng ; Liu, Ai-lian ; Liu, Jing-hong ; Liu, Yi-jun ; Pan, Ju-dong</creator><creatorcontrib>Tian, Shi-feng ; Liu, Ai-lian ; Liu, Jing-hong ; Liu, Yi-jun ; Pan, Ju-dong</creatorcontrib><description>Objective
To investigate the effect of a deep learning-based denoising algorithm, PixelShine (PS), on the quality of 70 kVp pelvic arterial phase CT images.
Materials and methods
A retrospective analysis was performed on arterial phase pelvic CT images from 33 patients (body-mass index ≤ 20 kg/m
2
) obtained with a GE Revolution CT (70 kVp tube voltage; adaptive statistical iterative reconstruction-Veo-filtered back projection, 50% blending) and designated group A. Group B images were then obtained by applying PS to group A image datasets. Subjective image quality was evaluated by two radiologists with a 5-point scoring system; the scores of the groups were compared. Image signal was assessed using CT values of the urinary bladder. CT and standard deviation (SD) values of the gluteus maximus were measured, and SD values of the gluteus maximus were used to represent image noise. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the bladder were calculated. Image noise, SNR, and CNR of two groups were compared using paired t-tests.
Results
The subjective visual image quality scores of groups A and B, respectively, were 3.11 ± 0.30 vs. 3.82 ± 0.57; image noise was 15.79 ± 2.05 Hounsfield units (HU) vs. 11.06 ± 2.22 HU; SNRs of bladder were 0.50 ± 0.23 vs. 0.79 ± 0.39; and CNRs of bladder were 3.72 ± 0.85 vs. 5.14 ± 1.27. Group B showed better subjective image quality, lower image noise, and improved SNR and CNR, compared to group A; these differences were statistically significant (
P
< 0.05). The noise of group B was approximately 30% lower than that of group A; the SNR and CNR values of group B were improved by approximately 58% and 38%, respectively.
Conclusion
Using 70 kVp +ASiR-V, PS can improve the image quality of pelvic arterial phase CT images, significantly reduce the image noise, and improve the SNR and CNR.</description><identifier>ISSN: 1867-1071</identifier><identifier>EISSN: 1867-108X</identifier><identifier>DOI: 10.1007/s11604-018-0798-0</identifier><identifier>PMID: 30523499</identifier><language>eng</language><publisher>Tokyo: Springer Japan</publisher><subject>Adaptive filters ; Algorithms ; Bladder ; Computed tomography ; Deep learning ; Image contrast ; Image quality ; Image reconstruction ; Imaging ; Medical imaging ; Medicine ; Medicine & Public Health ; Noise ; Noise reduction ; Nuclear Medicine ; Original Article ; Quality ; Radiology ; Radiotherapy ; Statistical analysis ; Tomography ; Urinary bladder</subject><ispartof>Japanese journal of radiology, 2019-02, Vol.37 (2), p.186-190</ispartof><rights>Japan Radiological Society 2018</rights><rights>Japanese Journal of Radiology is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c462t-88f5efdbeb072142cece01876e204ac87a72a8936395f7b7878ebc9e365a9a323</citedby><cites>FETCH-LOGICAL-c462t-88f5efdbeb072142cece01876e204ac87a72a8936395f7b7878ebc9e365a9a323</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/s11604-018-0798-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11604-018-0798-0$$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/30523499$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tian, Shi-feng</creatorcontrib><creatorcontrib>Liu, Ai-lian</creatorcontrib><creatorcontrib>Liu, Jing-hong</creatorcontrib><creatorcontrib>Liu, Yi-jun</creatorcontrib><creatorcontrib>Pan, Ju-dong</creatorcontrib><title>Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images</title><title>Japanese journal of radiology</title><addtitle>Jpn J Radiol</addtitle><addtitle>Jpn J Radiol</addtitle><description>Objective
To investigate the effect of a deep learning-based denoising algorithm, PixelShine (PS), on the quality of 70 kVp pelvic arterial phase CT images.
Materials and methods
A retrospective analysis was performed on arterial phase pelvic CT images from 33 patients (body-mass index ≤ 20 kg/m
2
) obtained with a GE Revolution CT (70 kVp tube voltage; adaptive statistical iterative reconstruction-Veo-filtered back projection, 50% blending) and designated group A. Group B images were then obtained by applying PS to group A image datasets. Subjective image quality was evaluated by two radiologists with a 5-point scoring system; the scores of the groups were compared. Image signal was assessed using CT values of the urinary bladder. CT and standard deviation (SD) values of the gluteus maximus were measured, and SD values of the gluteus maximus were used to represent image noise. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the bladder were calculated. Image noise, SNR, and CNR of two groups were compared using paired t-tests.
Results
The subjective visual image quality scores of groups A and B, respectively, were 3.11 ± 0.30 vs. 3.82 ± 0.57; image noise was 15.79 ± 2.05 Hounsfield units (HU) vs. 11.06 ± 2.22 HU; SNRs of bladder were 0.50 ± 0.23 vs. 0.79 ± 0.39; and CNRs of bladder were 3.72 ± 0.85 vs. 5.14 ± 1.27. Group B showed better subjective image quality, lower image noise, and improved SNR and CNR, compared to group A; these differences were statistically significant (
P
< 0.05). The noise of group B was approximately 30% lower than that of group A; the SNR and CNR values of group B were improved by approximately 58% and 38%, respectively.
Conclusion
Using 70 kVp +ASiR-V, PS can improve the image quality of pelvic arterial phase CT images, significantly reduce the image noise, and improve the SNR and CNR.</description><subject>Adaptive filters</subject><subject>Algorithms</subject><subject>Bladder</subject><subject>Computed tomography</subject><subject>Deep learning</subject><subject>Image contrast</subject><subject>Image quality</subject><subject>Image reconstruction</subject><subject>Imaging</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Noise</subject><subject>Noise reduction</subject><subject>Nuclear Medicine</subject><subject>Original Article</subject><subject>Quality</subject><subject>Radiology</subject><subject>Radiotherapy</subject><subject>Statistical analysis</subject><subject>Tomography</subject><subject>Urinary bladder</subject><issn>1867-1071</issn><issn>1867-108X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kV1rFDEUhoMotlZ_gDcS8EaQ0SSTmSSXZakfULDYWrwLmeyZ3dRsMk0yxf6L_uRm2FpB8CYncJ7zno8XodeUfKCEiI-Z0p7whlDZEKHq8wQdUtmLhhL58-njX9AD9CLnK0J63nL-HB20pGMtV-oQ3Z3FAqE44_GN8TPgOOKyBXzmfoM_37oAeA0wYQ8mBRc22PhNTK5sd3iMCbtgE5i8JK5n4125XQQEwb8up_fH5-57c4kT2BhySbMtLgY8gb9xFptUIC1tp63JgFcX2O3MBvJL9Gw0PsOrh3iEfnw6uVh9aU6_ff66Oj5tLO9ZaaQcOxjXAwxEMMqZBQv1DqIHRrixUhjBjFRt36puFIOQQsJgFbR9Z5RpWXuE3u11pxSvZ8hF71y24L0JEOesGe06xRQRvKJv_0Gv4pxCnW6hiOKcU1EpuqdsijknGPWU6krpVlOiF7v03i5dx9SLXZrUmjcPyvOwg_VjxR9_KsD2QK6psIH0t_X_Ve8BA5Wg5A</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Tian, Shi-feng</creator><creator>Liu, Ai-lian</creator><creator>Liu, Jing-hong</creator><creator>Liu, Yi-jun</creator><creator>Pan, Ju-dong</creator><general>Springer Japan</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7TK</scope><scope>7U7</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>C1K</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></search><sort><creationdate>20190201</creationdate><title>Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images</title><author>Tian, Shi-feng ; Liu, Ai-lian ; Liu, Jing-hong ; Liu, Yi-jun ; Pan, Ju-dong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c462t-88f5efdbeb072142cece01876e204ac87a72a8936395f7b7878ebc9e365a9a323</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adaptive filters</topic><topic>Algorithms</topic><topic>Bladder</topic><topic>Computed tomography</topic><topic>Deep learning</topic><topic>Image contrast</topic><topic>Image quality</topic><topic>Image reconstruction</topic><topic>Imaging</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Noise</topic><topic>Noise reduction</topic><topic>Nuclear Medicine</topic><topic>Original Article</topic><topic>Quality</topic><topic>Radiology</topic><topic>Radiotherapy</topic><topic>Statistical analysis</topic><topic>Tomography</topic><topic>Urinary bladder</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tian, Shi-feng</creatorcontrib><creatorcontrib>Liu, Ai-lian</creatorcontrib><creatorcontrib>Liu, Jing-hong</creatorcontrib><creatorcontrib>Liu, Yi-jun</creatorcontrib><creatorcontrib>Pan, Ju-dong</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</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>Environmental Sciences and Pollution Management</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>Japanese journal of radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tian, Shi-feng</au><au>Liu, Ai-lian</au><au>Liu, Jing-hong</au><au>Liu, Yi-jun</au><au>Pan, Ju-dong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images</atitle><jtitle>Japanese journal of radiology</jtitle><stitle>Jpn J Radiol</stitle><addtitle>Jpn J Radiol</addtitle><date>2019-02-01</date><risdate>2019</risdate><volume>37</volume><issue>2</issue><spage>186</spage><epage>190</epage><pages>186-190</pages><issn>1867-1071</issn><eissn>1867-108X</eissn><abstract>Objective
To investigate the effect of a deep learning-based denoising algorithm, PixelShine (PS), on the quality of 70 kVp pelvic arterial phase CT images.
Materials and methods
A retrospective analysis was performed on arterial phase pelvic CT images from 33 patients (body-mass index ≤ 20 kg/m
2
) obtained with a GE Revolution CT (70 kVp tube voltage; adaptive statistical iterative reconstruction-Veo-filtered back projection, 50% blending) and designated group A. Group B images were then obtained by applying PS to group A image datasets. Subjective image quality was evaluated by two radiologists with a 5-point scoring system; the scores of the groups were compared. Image signal was assessed using CT values of the urinary bladder. CT and standard deviation (SD) values of the gluteus maximus were measured, and SD values of the gluteus maximus were used to represent image noise. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the bladder were calculated. Image noise, SNR, and CNR of two groups were compared using paired t-tests.
Results
The subjective visual image quality scores of groups A and B, respectively, were 3.11 ± 0.30 vs. 3.82 ± 0.57; image noise was 15.79 ± 2.05 Hounsfield units (HU) vs. 11.06 ± 2.22 HU; SNRs of bladder were 0.50 ± 0.23 vs. 0.79 ± 0.39; and CNRs of bladder were 3.72 ± 0.85 vs. 5.14 ± 1.27. Group B showed better subjective image quality, lower image noise, and improved SNR and CNR, compared to group A; these differences were statistically significant (
P
< 0.05). The noise of group B was approximately 30% lower than that of group A; the SNR and CNR values of group B were improved by approximately 58% and 38%, respectively.
Conclusion
Using 70 kVp +ASiR-V, PS can improve the image quality of pelvic arterial phase CT images, significantly reduce the image noise, and improve the SNR and CNR.</abstract><cop>Tokyo</cop><pub>Springer Japan</pub><pmid>30523499</pmid><doi>10.1007/s11604-018-0798-0</doi><tpages>5</tpages></addata></record> |
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| language | eng |
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| source | SpringerNature Journals |
| subjects | Adaptive filters Algorithms Bladder Computed tomography Deep learning Image contrast Image quality Image reconstruction Imaging Medical imaging Medicine Medicine & Public Health Noise Noise reduction Nuclear Medicine Original Article Quality Radiology Radiotherapy Statistical analysis Tomography Urinary bladder |
| title | Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images |
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