Physics‐guided self‐supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma.
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| Title: | Physics‐guided self‐supervised learning for retrospective T |
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| Authors: | Qiu, Shihan1,2 (AUTHOR), Wang, Lixia1 (AUTHOR), Sati, Pascal1,3 (AUTHOR), Christodoulou, Anthony G.2,4 (AUTHOR), Xie, Yibin1 (AUTHOR), Li, Debiao1,2 (AUTHOR) debiao.li@cshs.org |
| Source: | Magnetic Resonance in Medicine. Dec2024, Vol. 92 Issue 6, p2683-2695. 13p. |
| Subjects: | Magnetic resonance imaging, Brain tumors, Deep learning, Diagnostic imaging, Glioblastoma multiforme |
| Abstract: | Purpose: To develop a self‐supervised learning method to retrospectively estimate T1 and T2 values from clinical weighted MRI. Methods: A self‐supervised learning approach was constructed to estimate T1, T2, and proton density maps from conventional T1‐ and T2‐weighted images. MR physics models were employed to regenerate the weighted images from the network outputs, and the network was optimized based on loss calculated between the synthesized and input weighted images, alongside additional constraints based on prior information. The method was evaluated on healthy volunteer data, with conventional mapping as references. The reproducibility was examined on two 3.0T scanners. Performance in tumor characterization was inspected by applying the method to a public glioblastoma dataset. Results: For T1 and T2 estimation from three weighted images (T1 MPRAGE, T1 gradient echo sequences, and T2 turbo spin echo), the deep learning method achieved global voxel‐wise error ≤9% in brain parenchyma and regional error ≤12.2% in six types of brain tissues. The regional measurements obtained from two scanners showed mean differences ≤2.4% and correlation coefficients >0.98, demonstrating excellent reproducibility. In the 50 glioblastoma patients, the retrospective quantification results were in line with literature reports from prospective methods, and the T2 values were found to be higher in tumor regions, with sensitivity of 0.90 and specificity of 0.92 in a voxel‐wise classification task between normal and abnormal regions. Conclusion: The self‐supervised learning method is promising for retrospective T1 and T2 quantification from clinical MR images, with the potential to improve the availability of quantitative MRI and facilitate brain tumor characterization. [ABSTRACT FROM AUTHOR] |
| Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 179962633 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Physics‐guided self‐supervised learning for retrospective T<subscript>1</subscript> and T<subscript>2</subscript> mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Qiu%2C+Shihan%22">Qiu, Shihan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Lixia%22">Wang, Lixia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sati%2C+Pascal%22">Sati, Pascal</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Christodoulou%2C+Anthony+G%2E%22">Christodoulou, Anthony G.</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Yibin%22">Xie, Yibin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Debiao%22">Li, Debiao</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> debiao.li@cshs.org</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Magnetic+Resonance+in+Medicine%22">Magnetic Resonance in Medicine</searchLink>. Dec2024, Vol. 92 Issue 6, p2683-2695. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+tumors%22">Brain tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Glioblastoma+multiforme%22">Glioblastoma multiforme</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Purpose: To develop a self‐supervised learning method to retrospectively estimate T1 and T2 values from clinical weighted MRI. Methods: A self‐supervised learning approach was constructed to estimate T1, T2, and proton density maps from conventional T1‐ and T2‐weighted images. MR physics models were employed to regenerate the weighted images from the network outputs, and the network was optimized based on loss calculated between the synthesized and input weighted images, alongside additional constraints based on prior information. The method was evaluated on healthy volunteer data, with conventional mapping as references. The reproducibility was examined on two 3.0T scanners. Performance in tumor characterization was inspected by applying the method to a public glioblastoma dataset. Results: For T1 and T2 estimation from three weighted images (T1 MPRAGE, T1 gradient echo sequences, and T2 turbo spin echo), the deep learning method achieved global voxel‐wise error ≤9% in brain parenchyma and regional error ≤12.2% in six types of brain tissues. The regional measurements obtained from two scanners showed mean differences ≤2.4% and correlation coefficients >0.98, demonstrating excellent reproducibility. In the 50 glioblastoma patients, the retrospective quantification results were in line with literature reports from prospective methods, and the T2 values were found to be higher in tumor regions, with sensitivity of 0.90 and specificity of 0.92 in a voxel‐wise classification task between normal and abnormal regions. Conclusion: The self‐supervised learning method is promising for retrospective T1 and T2 quantification from clinical MR images, with the potential to improve the availability of quantitative MRI and facilitate brain tumor characterization. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Magnetic Resonance in Medicine is the property of Wiley-Blackwell 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mrm.30226 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 2683 Subjects: – SubjectFull: Magnetic resonance imaging Type: general – SubjectFull: Brain tumors Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Diagnostic imaging Type: general – SubjectFull: Glioblastoma multiforme Type: general Titles: – TitleFull: Physics‐guided self‐supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Qiu, Shihan – PersonEntity: Name: NameFull: Wang, Lixia – PersonEntity: Name: NameFull: Sati, Pascal – PersonEntity: Name: NameFull: Christodoulou, Anthony G. – PersonEntity: Name: NameFull: Xie, Yibin – PersonEntity: Name: NameFull: Li, Debiao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 07403194 Numbering: – Type: volume Value: 92 – Type: issue Value: 6 Titles: – TitleFull: Magnetic Resonance in Medicine Type: main |
| ResultId | 1 |
