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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 07403194 |
| DOI: | 10.1002/mrm.30226 |
