©The Author(s) 2023.
World J Radiol. Dec 28, 2023; 15(12): 338-349
Published online Dec 28, 2023. doi: 10.4329/wjr.v15.i12.338
Published online Dec 28, 2023. doi: 10.4329/wjr.v15.i12.338
Table 1 Details of imaging parameters
| Parameters | FOCUS-DLR+/– | FOCUS-conv |
| Repetition time, ms | 3000–10000 | 3500–15000 |
| Echo time, ms | 60 | 60 |
| Flip angle, degree | 90 | 90 |
| FOV, mm2 | 220 × 110 | 220 × 110 |
| Matrix | 120 × 64 | 130 × 40 |
| FOV reduction | Anterior-posterior | Anterior-posterior |
| Slice thickness, mm | 3 | 4 |
| Slice gap, mm | 3 | 5 |
| Number of slices | 20–30 | 15–20 |
| Number of excitations | 4 | 8 |
| b-values, s/mm2 | 0 and 600 | 0 and 600 |
| Band width, Hz/pixel | 1950 | 1300 |
| Respiratory compensation | Respiratory-triggered with navigator echo | Respiratory-triggered with or without navigator echo |
| Deep learning reconstruction factor | Moderate | N/A |
| Scan time, min | 2–5 | 3–10 |
- Citation: Takayama Y, Sato K, Tanaka S, Murayama R, Goto N, Yoshimitsu K. Deep learning-based magnetic resonance imaging reconstruction for improving the image quality of reduced-field-of-view diffusion-weighted imaging of the pancreas. World J Radiol 2023; 15(12): 338-349
- URL: https://www.wjgnet.com/1949-8470/full/v15/i12/338.htm
- DOI: https://dx.doi.org/10.4329/wjr.v15.i12.338