Related Experiment Video
Updated: Jan 10, 2026
Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
Published on: May 7, 2019
Deep-learning reconstruction enables about one minute 3D T1-weighted MRI: quantitative evaluation of
Keita Watanabe1,2, Kazuhiko Oyu3, Sera Kasai3
1Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan. kw0928@koto.kpu-m.ac.jp.
Purpose:
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.
Methods:
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.
Results:
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).
Conclusion:
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.
Trial Registration:
Not applicable.
