Research Article
RLMD-PA: A Reinforcement Learning-Based Myocarditis Diagnosis Combined with a Population-Based Algorithm for Pretraining Weights
Table 1
Characteristics of the Z-Alizadeh Sani myocarditis dataset.
| | Protocols | TE (mm) | TR (mm) | NF | Slice thickness (mm) | Concatenation and slice number | NE | Breath-hold time (s) |
| | CINE_segmented (true FISP) long axis (LAX) | 1.15 | 33.60 | 15 | 7 | 3 | 1 | 8 | | CINE_segmented (true FISP) short axis (SAX) | 1.11 | 31.92 | 15 | 7 | 15 | 1 | 8 | | T2-weighted (TIRM) LAX, precontrast | 52 | 800 | Noncine | 10 | 3 | 1 | 9 | | T2-weighted (TIRM) SAX, precontrast | 52 | 800 | Noncine | 10 | 5 | 1 | 10 | | T1 relative-weighted TSE (Trigger)-AXIA-dark blood pre- and postcontrast | 24 | 525 | Noncine | 8 | 5 | 1 | 7 | | Late-GD enhancement LGE (high-resolution PSIR) SAX and LAX | 3.16 | 666 | Noncine | 8 | 1 | 1 | 7 |
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TE: time echo, TR: time repetition, NF: number of frames, NE: number of excitations.
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