Table 2.
Evaluation of model performance on the mouse brain vasculature in ssim metrics at different noise levels.
| MBLr | FIRe | 2S-FIRe | TV | U-Net | |
|---|---|---|---|---|---|
| 6 dB SNR | 0.736 ± 0.048 | 0.788 ± 0.038 | 0.797 ± 0.047 | 0.515 ± 0.031 | 0.432 ± 0.049 |
| 9 dB SNR | 0.831 ± 0.044 | 0.847 ± 0.033 | 0.853 ± 0.039 | 0.583 ± 0.032 | 0.559 ± 0.049 |
| 12 dB SNR | 0.887 ± 0.037 | 0.886 ± 0.030 | 0.886 ± 0.033 | 0.635 ± 0.034 | 0.636 ± 0.052 |
| 15 dB SNR | 0.907 ± 0.033 | 0.901 ± 0.028 | 0.915 ± 0.026 | 0.660 ± 0.034 | 0.699 ± 0.051 |
| 18 dB SNR | 0.926 ± 0.027 | 0.918 ± 0.026 | 0.912 ± 0.027 | 0.698 ± 0.035 | 0.692 ± 0.052 |
| 21 dB SNR | 0.931 ± 0.025 | 0.923 ± 0.025 | 0.916 ± 0.026 | 0.712 ± 0.036 | 0.701 ± 0.051 |
| 24 dB SNR | 0.934 ± 0.024 | 0.926 ± 0.024 | 0.919 ± 0.025 | 0.720 ± 0.036 | 0.705 ± 0.051 |
| 27 dB SNR | 0.935 ± 0.024 | 0.927 ± 0.024 | 0.920 ± 0.025 | 0.724 ± 0.036 | 0.708 ± 0.051 |
| 30 dB SNR | 0.936 ± 0.024 | 0.928 ± 0.024 | 0.920 ± 0.025 | 0.726 ± 0.037 | 0.708 ± 0.051 |
| - | 0.936 ± 0.024 | 0.928 ± 0.024 | 0.921 ± 0.025 | 0.729 ± 0.037 | 0.709 ± 0.051 |
MBLr: model-based learning. FIRe: fast iterative reconstruction. 2S-FIRe: two-stage fast iterative reconstruction.TV: total variation. U-Net: single-step post-processing. -: no noise included in collected sensor data.