Dev

Brachytherapy Imaging — Dev Tier

(3 scenes)

Blind evaluation tier — no ground truth available.

What you get

Measurements (y), ideal forward operator (H), and spec ranges only.

How to use

Apply your pipeline from the Public tier. Use consistency as self-check.

What to submit

Reconstructed signals and corrected spec. Scored server-side.

Parameter Specifications

🔒

True spec hidden — estimate parameters from spec ranges below.

Parameter Spec Range Unit
source_position_error -0.48 – 0.72 mm
attenuation_coefficient 0.188 – 0.218 1/cm
detector_gain_drift 0.988 – 1.018 -
scatter_fraction 0.126 – 0.186 -

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 DiffusionSeed + gradient 0.777 32.87 0.951 0.84 ✓ Certified Gao et al., Med. Phys. 2024
2 CTFormer + gradient 0.765 31.59 0.938 0.87 ✓ Certified Wang et al., MICCAI 2023
3 DuDoTrans + gradient 0.758 31.95 0.942 0.81 ✓ Certified Wang et al., IEEE TMI 2022
4 Learned Primal-Dual + gradient 0.750 31.08 0.932 0.83 ✓ Certified Adler & Oktem, IEEE TMI 2018
5 Metal-AR-Net + gradient 0.712 28.55 0.892 0.85 ✓ Certified Zhang & Yu, IEEE TMI 2018
6 FBPConvNet + gradient 0.695 27.59 0.872 0.85 ✓ Certified Jin et al., IEEE TIP 2017
7 RED-CNN + gradient 0.687 27.8 0.877 0.79 ✓ Certified Chen et al., IEEE TMI 2017
8 FDK + gradient 0.639 24.64 0.791 0.88 ✓ Certified Feldkamp et al., J. Opt. Soc. Am. A 1984
9 TV-ADMM + gradient 0.597 23.56 0.753 0.8 ✓ Certified Boyd et al., Found. Trends Mach. Learn. 2011

Visible Data Fields

y H_ideal spec_ranges

Dataset

Format: HDF5
Scenes: 3

Scoring Formula

0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)

PSNR: 40% SSIM: 40% Consistency: 20%
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