Deep-learning reconstruction enables about one minute 3D T1-weighted MRI: quantitative evaluation of Acceleration-quality Trade-offs and motion reduction.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan. [email protected].
- Department of Radiology, Hirosaki University, Hirosaki, Japan. [email protected].
- Department of Radiology, Hirosaki University, Hirosaki, Japan.
- GE Healthcare, Hino, Japan.
- GE healthcare, Menlo Park, United States.
Abstract
Deep learning-based reconstruction techniques, such as the prototype DL-Speed, have been developed to accelerate 3D T1-weighted imaging, but their clinical utility and impact on quantitative analysis have not been systematically validated. The purpose of this study is to evaluate the utility of a deep learning-based reconstruction method for substantially accelerating image acquisition without compromising quantitative image quality and morphometry. Six healthy volunteers were scanned with 3D MPRAGE using acceleration factors ranging from 2 to 16. Image quality was assessed using the CAT12 Image Quality Rating (IQR), and morphometry with CAT12 global cortical thickness and total gray matter (GM) volume. In a clinical cohort of 40 patients, DL-Speed with 11-fold acceleration (1 min 10 s) was compared to conventional imaging (4 min 59 s). Head motion was quantified via total vector change (TVC) using volumetric navigators. In healthy volunteers, image quality remained within CAT12 Rank A at all acceleration levels; IQR followed a quadratic trend (R² = 0.98). Global cortical thickness decreased approximately linearly with acceleration (e.g., DLS2→DLS11: -0.068 mm [- 2.8%]; DLS2→DLS16: -0.140 mm [- 5.7%]). Total GM volume showed a shallow, monotonic decline (group means: 748.8 mL at DLS2 → 729.5 mL at DLS11 [- 2.6%] → 726.3 mL at DLS16 [- 3.0%]). In clinical cohort, DL-Speed significantly reduced head motion (TVC: 52.3 ± 9.4 mm vs. 140.4 ± 32.8 mm, p < 0.001) while maintaining acceptable image quality (IQR: 93.9 ± 1.0% vs. 94.8 ± 1.2%, p < 0.001). Bland-Altman analysis showed narrow limits of agreement (-2.49% to + 0.75%). Regarding cortical thickness and GM volume, we found significant correlations between conventional images and DL-Speed (thickness r = 0.97; GM r = 0.99; both p < 0.001). DL-Speed enables about-one-minute 3D T1-weighted imaging that markedly reduces motion while preserving quantitative image integrity suitable for morphometry. Because image quality (IQR) follows a non-linear (quadratic) dependency whereas cortical thickness and GM volume decline nearly linearly, the data support a practical operating range (e.g., 6-11-fold acceleration) rather than a single optimal factor. Not applicable.