Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.
Saved in:
| Title: | Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques. |
|---|---|
| Authors: | Cao, Jinjin1 (AUTHOR), Mroueh, Nayla1 (AUTHOR), Lennartz, Simon1,2 (AUTHOR), Mercaldo, Nathaniel D.1 (AUTHOR), Pisuchpen, Nisanard1,3 (AUTHOR), Kongboonvijit, Sasiprang1,3 (AUTHOR), Srinivas Rao, Shravya1 (AUTHOR), Yuenyongsinchai, Kampon1,3 (AUTHOR), Pierce, Theodore T.1 (AUTHOR), Sertic, Madeleine1 (AUTHOR), Chung, Ryan1 (AUTHOR), Kambadakone, Avinash R.1 (AUTHOR) akambadakone@mgh.harvard.edu |
| Source: | European Radiology. Feb2025, Vol. 35 Issue 2, p885-896. 12p. |
| Subjects: | Patient portals, Computed tomography, Image reconstruction, Dual energy CT (Tomography), Portal vein |
| Abstract: | Objectives: To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). Methods: This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. Results: DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. Conclusion: Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. Clinical relevance statement: Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. Key Points: Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR) showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight ≥ 90 kg) yielded similar findings to the overall study population. [ABSTRACT FROM AUTHOR] |
| Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 182564620 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Cao%2C+Jinjin%22">Cao, Jinjin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mroueh%2C+Nayla%22">Mroueh, Nayla</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lennartz%2C+Simon%22">Lennartz, Simon</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mercaldo%2C+Nathaniel+D%2E%22">Mercaldo, Nathaniel D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pisuchpen%2C+Nisanard%22">Pisuchpen, Nisanard</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kongboonvijit%2C+Sasiprang%22">Kongboonvijit, Sasiprang</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Srinivas+Rao%2C+Shravya%22">Srinivas Rao, Shravya</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yuenyongsinchai%2C+Kampon%22">Yuenyongsinchai, Kampon</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pierce%2C+Theodore+T%2E%22">Pierce, Theodore T.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sertic%2C+Madeleine%22">Sertic, Madeleine</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chung%2C+Ryan%22">Chung, Ryan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kambadakone%2C+Avinash+R%2E%22">Kambadakone, Avinash R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> akambadakone@mgh.harvard.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Feb2025, Vol. 35 Issue 2, p885-896. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Patient+portals%22">Patient portals</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Dual+energy+CT+%28Tomography%29%22">Dual energy CT (Tomography)</searchLink><br /><searchLink fieldCode="DE" term="%22Portal+vein%22">Portal vein</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objectives: To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). Methods: This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. Results: DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. Conclusion: Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. Clinical relevance statement: Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. Key Points: Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR) showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight ≥ 90 kg) yielded similar findings to the overall study population. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=182564620 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00330-024-10974-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 885 Subjects: – SubjectFull: Patient portals Type: general – SubjectFull: Computed tomography Type: general – SubjectFull: Image reconstruction Type: general – SubjectFull: Dual energy CT (Tomography) Type: general – SubjectFull: Portal vein Type: general Titles: – TitleFull: Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Cao, Jinjin – PersonEntity: Name: NameFull: Mroueh, Nayla – PersonEntity: Name: NameFull: Lennartz, Simon – PersonEntity: Name: NameFull: Mercaldo, Nathaniel D. – PersonEntity: Name: NameFull: Pisuchpen, Nisanard – PersonEntity: Name: NameFull: Kongboonvijit, Sasiprang – PersonEntity: Name: NameFull: Srinivas Rao, Shravya – PersonEntity: Name: NameFull: Yuenyongsinchai, Kampon – PersonEntity: Name: NameFull: Pierce, Theodore T. – PersonEntity: Name: NameFull: Sertic, Madeleine – PersonEntity: Name: NameFull: Chung, Ryan – PersonEntity: Name: NameFull: Kambadakone, Avinash R. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09387994 Numbering: – Type: volume Value: 35 – Type: issue Value: 2 Titles: – TitleFull: European Radiology Type: main |
| ResultId | 1 |
