Public
Bioluminescence Tomography (BLT) — Public Tier
(5 scenes)Full-access development tier with all data visible.
What you get
Measurements (y), ideal forward operator (H), spec ranges, ground truth (x_true), and true mismatch spec.
How to use
Load HDF5 → compare reconstruction vs x_true → check consistency → iterate.
What to submit
Reconstructed signals (x_hat) and corrected spec as HDF5.
Parameter Specifications
✓
True spec visible — use these exact values for Scenario III oracle reconstruction.
| Parameter | Spec Range | True Value | Unit |
|---|---|---|---|
| optical_property_error_(mu_a,_mu_s') | -4.0 – 8.0 | 2.0 | relative |
| source_depth_ambiguity | -1.0 – 2.0 | 0.5 | mm |
| autofluorescence_background | -6.0 – 12.0 | 3.0 | - |
InverseNet Baseline Scores
Method: CPU_baseline — Mismatch parameter: nominal
Scenario I (Ideal)
7.77 dB
SSIM 0.3772
Scenario II (Mismatch)
7.65 dB
SSIM 0.2288
Scenario III (Oracle)
16.34 dB
SSIM 0.4542
Per-scene breakdown (4 scenes)
| Scene | PSNR I | SSIM I | PSNR II | SSIM II | PSNR III | SSIM III |
|---|---|---|---|---|---|---|
| scene_00 | 7.57 | 0.3615 | 7.52 | 0.2249 | 16.28 | 0.4660 |
| scene_01 | 7.70 | 0.3833 | 7.81 | 0.2328 | 16.28 | 0.4522 |
| scene_02 | 7.97 | 0.3846 | 7.55 | 0.2285 | 16.43 | 0.4500 |
| scene_03 | 7.84 | 0.3794 | 7.72 | 0.2289 | 16.35 | 0.4487 |
Public Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | ScoreBLT + gradient | 0.826 | 35.41 | 0.97 | 0.92 | ✓ Certified | Score-based BLT with uncertainty, 2024 |
| 2 | PhysDiff-BLT + gradient | 0.825 | 35.78 | 0.972 | 0.89 | ✓ Certified | Physics-constrained diffusion for BLT, 2025 |
| 3 | BLT-Former + gradient | 0.782 | 32.76 | 0.95 | 0.87 | ✓ Certified | Transformer for optical tomography, MICCAI 2023 |
| 4 | DiffusionPINN-BLT + gradient | 0.755 | 31.06 | 0.932 | 0.86 | ✓ Certified | Cai et al., Phys. Med. Biol. 68:035005, 2023 |
| 5 | LISTA-BLT + gradient | 0.740 | 29.19 | 0.904 | 0.93 | ✓ Certified | Gregor & LeCun, ICML 2010; adapted BLT 2020 |
| 6 | BLT-CNN + gradient | 0.713 | 27.74 | 0.875 | 0.93 | ✓ Certified | Gao et al., Sci. Rep. 8:8363, 2018 |
| 7 | PnP-ADMM (BLT) + gradient | 0.638 | 24.19 | 0.775 | 0.93 | ✓ Certified | Venkatakrishnan et al., IEEE GlobalSIP 2013 |
| 8 | Tikhonov-PR + gradient | 0.521 | 20.06 | 0.602 | 0.9 | ✓ Certified | Han et al., Opt. Express 14(8):3673, 2006 |
| 9 | Tikhonov-BLT + gradient | 0.423 | 16.7 | 0.435 | 0.91 | ✓ Certified | Lv et al., Phys. Med. Biol. 51:1479, 2006 |
Visible Data Fields
y
H_ideal
spec_ranges
x_true
true_spec
Dataset
Format: HDF5
Scenes: 5
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%