DOT

Diffuse Optical Tomography

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Standard reconstruction benchmark — forward model perfectly known, no calibration needed. Score = 0.5 × clip((PSNR−15)/30, 0, 1) + 0.5 × SSIM

# Method Score PSNR (dB) SSIM Source
🥇 DiffusionDOT 0.877 39.0 0.954 ✓ Certified Gao et al. 2024
🥈 PhysDOT 0.846 37.5 0.942 ✓ Certified Chen et al. 2024
🥉 SwinDOT 0.817 36.1 0.930 ✓ Certified Wang et al. 2023
4 TransDOT 0.775 34.2 0.910 ✓ Certified Li et al. 2022
5 DOT-Net 0.707 31.4 0.868 ✓ Certified Guo et al. 2021
6 DnCNN-DOT 0.641 28.7 0.825 ✓ Certified Yoo et al. 2019
7 FEM-DOT 0.567 25.9 0.771 ✓ Certified Schweiger et al. 2005
8 TV-DOT 0.506 23.5 0.729 ✓ Certified Borsic et al., IEEE TMI 2010
9 Born-Approx 0.437 20.8 0.681 ✓ Certified Arridge, Inverse Probl. 1999

Dataset: PWM Benchmark (9 algorithms)

Blind Reconstruction Challenge — forward model has unknown mismatch, must calibrate from data. Score = 0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)

# Method Overall Score Public
PSNR / SSIM
 Dev
PSNR / SSIM
 Hidden
PSNR / SSIM
 Trust Source
🥇 DiffusionDOT + gradient 0.787
0.835
36.94 dB / 0.978
0.788
33.47 dB / 0.957
0.739
29.98 dB / 0.917
✓ Certified Gao et al., NeurIPS 2024
🥈 PhysDOT + gradient 0.752
0.815
35.18 dB / 0.969
0.739
29.65 dB / 0.911
0.701
28.25 dB / 0.886
✓ Certified Chen et al., Opt. Express 2024
🥉 SwinDOT + gradient 0.738
0.821
34.82 dB / 0.967
0.727
29.87 dB / 0.915
0.666
26.85 dB / 0.854
✓ Certified Wang et al., Biomed. Opt. Express 2023
4 TransDOT + gradient 0.735
0.775
32.28 dB / 0.946
0.733
29.49 dB / 0.909
0.697
27.8 dB / 0.877
✓ Certified Li et al., IEEE TMI 2022
5 FEM-DOT + gradient 0.599
0.607
23.11 dB / 0.735
0.595
23.42 dB / 0.747
0.595
23.25 dB / 0.741
✓ Certified Schweiger et al., J. Biomed. Opt. 2005
6 DOT-Net + gradient 0.560
0.730
29.33 dB / 0.906
0.517
20.68 dB / 0.631
0.433
17.55 dB / 0.478
✓ Certified Guo et al., Biomed. Opt. Express 2021
7 DnCNN-DOT + gradient 0.532
0.707
27.39 dB / 0.867
0.498
19.76 dB / 0.587
0.390
15.97 dB / 0.400
✓ Certified Yoo et al., Sci. Rep. 2019
8 Born-Approx + gradient 0.440
0.502
19.08 dB / 0.554
0.423
17.35 dB / 0.468
0.396
16.38 dB / 0.420
✓ Certified Arridge, Inverse Probl. 1999
9 TV-DOT + gradient 0.389
0.559
21.59 dB / 0.672
0.358
14.99 dB / 0.354
0.251
11.42 dB / 0.211
✓ Certified Borsic et al., IEEE TMI 2010

Complete score requires all 3 tiers (Public + Dev + Hidden).

Join the competition →
Scoring: 0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖) PSNR 40% · SSIM 40% · Consistency 20%
Public 3 scenes

Full-access development tier with all data visible.

What you get & how to use

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.

Public Leaderboard
# Method Score PSNR SSIM
1 DiffusionDOT + gradient 0.835 36.94 0.978
2 SwinDOT + gradient 0.821 34.82 0.967
3 PhysDOT + gradient 0.815 35.18 0.969
4 TransDOT + gradient 0.775 32.28 0.946
5 DOT-Net + gradient 0.730 29.33 0.906
6 DnCNN-DOT + gradient 0.707 27.39 0.867
7 FEM-DOT + gradient 0.607 23.11 0.735
8 TV-DOT + gradient 0.559 21.59 0.672
9 Born-Approx + gradient 0.502 19.08 0.554
Spec Ranges (3 parameters)
Parameter Min Max Unit
mu_a -10.0 20.0 %
mu_s -8.0 16.0 %
source_pos -1.0 2.0 mm
View Details →
Dev 3 scenes

Blind evaluation tier — no ground truth available.

What you get & how to use

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.

Dev Leaderboard
# Method Score PSNR SSIM
1 DiffusionDOT + gradient 0.788 33.47 0.957
2 PhysDOT + gradient 0.739 29.65 0.911
3 TransDOT + gradient 0.733 29.49 0.909
4 SwinDOT + gradient 0.727 29.87 0.915
5 FEM-DOT + gradient 0.595 23.42 0.747
6 DOT-Net + gradient 0.517 20.68 0.631
7 DnCNN-DOT + gradient 0.498 19.76 0.587
8 Born-Approx + gradient 0.423 17.35 0.468
9 TV-DOT + gradient 0.358 14.99 0.354
Spec Ranges (3 parameters)
Parameter Min Max Unit
mu_a -12.0 18.0 %
mu_s -9.6 14.4 %
source_pos -1.2 1.8 mm
Submit Reconstruction View Details →
Hidden 3 scenes

Fully blind server-side evaluation — no data download.

What you get & how to use

What you get: No data downloadable. Algorithm runs server-side on hidden measurements.

How to use: Package algorithm as Docker container / Python script. Submit via link.

What to submit: Containerized algorithm accepting y + H, outputting x_hat + corrected spec.

Hidden Leaderboard
# Method Score PSNR SSIM
1 DiffusionDOT + gradient 0.739 29.98 0.917
2 PhysDOT + gradient 0.701 28.25 0.886
3 TransDOT + gradient 0.697 27.8 0.877
4 SwinDOT + gradient 0.666 26.85 0.854
5 FEM-DOT + gradient 0.595 23.25 0.741
6 DOT-Net + gradient 0.433 17.55 0.478
7 Born-Approx + gradient 0.396 16.38 0.42
8 DnCNN-DOT + gradient 0.390 15.97 0.4
9 TV-DOT + gradient 0.251 11.42 0.211
Spec Ranges (3 parameters)
Parameter Min Max Unit
mu_a -7.0 23.0 %
mu_s -5.6 18.4 %
source_pos -0.7 2.3 mm
Submit Algorithm View Details →

Blind Reconstruction Challenge

Challenge

Given measurements with unknown mismatch and spec ranges (not exact params), reconstruct the original signal. A method must be evaluated on all three tiers for a complete score. Scored on a composite metric: 0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖).

Input

Measurements y, ideal forward model H, spec ranges

Output

Reconstructed signal x̂

About the Imaging Modality

Diffuse optical tomography (DOT) reconstructs 3D maps of tissue optical properties (absorption mu_a and reduced scattering mu_s') by measuring near-infrared light transport through highly scattering tissue. Multiple source-detector pairs on the tissue surface sample the diffuse photon field. The forward model is the diffusion equation: light propagation is modelled as a diffusive process with the photon fluence depending on the spatial distribution of mu_a and mu_s'. Reconstruction linearizes around a homogeneous background (Born/Rytov approximation) or uses nonlinear iterative methods. Applications include breast imaging and functional brain imaging (fNIRS-DOT).

Principle

Diffuse Optical Tomography reconstructs 3-D maps of tissue optical properties (absorption μₐ and reduced scattering μ'ₛ) from measurements of multiply scattered near-infrared light transmitted through tissue. Multiple source-detector pairs on the tissue surface provide overlapping sensitivity profiles. The diffusion equation models light propagation in the multiple-scattering regime.

How to Build the System

Place fiber-coupled NIR sources (670-850 nm laser diodes, CW or frequency-domain modulated at 100-300 MHz, or time-domain pulsed) and detector fibers (avalanche photodiodes or PMTs) on the tissue surface in an array. A multiplexer switches between source positions. For breast DOT, 32-128 optode positions on a cup or ring geometry. Calibrate with known optical phantoms (Intralipid + ink solutions).

Common Reconstruction Algorithms

  • Normalized Born approximation (linearized diffuse optical tomography)
  • Nonlinear Newton-type iterative reconstruction (Gauss-Newton, Levenberg-Marquardt)
  • Finite-element method (FEM) based forward solver + Tikhonov regularization
  • TOAST++ (Time-resolved Optical Absorption and Scattering Tomography)
  • Deep-learning DOT (learned regularization, direct inversion networks)

Common Mistakes

  • Poor optode-tissue coupling due to hair, uneven surfaces, or insufficient pressure
  • Inadequate source-detector pair coverage causing reconstruction blind spots
  • Cross-talk between source channels if multiplexing is not properly timed
  • Using the diffusion approximation too close to sources or in low-scattering regions
  • Ignoring tissue heterogeneity in the background optical property estimate

How to Avoid Mistakes

  • Use spring-loaded optodes with coupling checks; shave hair in the measurement area
  • Design source-detector geometry with overlapping sensitivity to cover the volume of interest
  • Ensure clean channel switching with adequate settling time between multiplexed measurements
  • Use higher-order transport models (radiative transfer) near sources if needed
  • Initialize reconstruction with patient-specific anatomical prior (from MRI or CT)

Forward-Model Mismatch Cases

  • The widefield fallback produces a 2D (64,64) image, but Diffuse Optical Tomography acquires boundary measurements (source-detector pairs) — output shape (64,) is a 1D vector of photon counts at detector positions
  • DOT measurement physics involves diffuse light propagation through scattering tissue (modeled by the diffusion equation), which is fundamentally different from surface-level Gaussian blur — the fallback cannot model subsurface absorption and scattering

How to Correct the Mismatch

  • Use the DOT operator that models photon transport via the diffusion equation: Jacobian maps from interior optical properties (absorption, scattering) to boundary measurements at each source-detector pair
  • Reconstruct interior absorption/scattering maps using Tikhonov-regularized inversion or iterative methods (conjugate gradient) with the correct diffusion-equation-based forward model

Experimental Setup — Signal Chain

Experimental setup diagram for Diffuse Optical Tomography

Experimental Setup

Instrument: ISS Imagent / NIRx NIRScout
Wavelengths Nm: [685, 785, 830]
Source Positions: 20
Detector Positions: 32
Total Sd Pairs: 640
Geometry: circular array (breast) / cap (brain)
Reconstruction Grid Mm: 1x1x1 voxels, 50x50x30 mm volume
Modulation: continuous-wave / frequency-domain
Reconstruction: Born approximation / diffusion model

Key References

  • Arridge, 'Optical tomography in medical imaging', Inverse Problems 15, R41-R93 (1999)
  • Boas et al., 'Imaging the body with diffuse optical tomography', IEEE Signal Processing Magazine 18, 57-75 (2001)

Canonical Datasets

  • UCL DOT phantom datasets
  • BU fNIRS-DOT brain imaging benchmarks

Spec DAG — Forward Model Pipeline

P(diffuse) → Σ → D(g, η₃)

P Diffuse Propagation (diffuse)
Σ Boundary Integration
D Photodetector (g, η₃)

Mismatch Parameters

Symbol Parameter Description Nominal Perturbed
Δμ_a mu_a Absorption coefficient error (%) 0 10.0
Δμ_s' mu_s Reduced scattering error (%) 0 8.0
Δr_s source_pos Source position error (mm) 0 1.0

Credits System

40%
Platform Profit Pool
Revenue allocated to benchmark rewards
30%
Winner Share
Top algorithm receives from pool
$100
Min Withdrawal
Minimum payout threshold
Spec Primitives Reference (11 primitives)
P Propagation

Free-space or medium propagation kernel (Fresnel, Rayleigh-Sommerfeld).

M Mask / Modulation

Spatial or spatio-temporal amplitude modulation (coded aperture, SLM pattern).

Π Projection

Geometric projection operator (Radon transform, fan-beam, cone-beam).

F Fourier Sampling

Sampling in the Fourier / k-space domain (MRI, ptychography).

C Convolution

Shift-invariant convolution with a point-spread function (PSF).

Σ Summation / Integration

Summation along a physical dimension (spectral, temporal, angular).

D Detector

Sensor readout with gain g and noise model η (Gaussian, Poisson, mixed).

S Structured Illumination

Patterned illumination (block, Hadamard, random) applied to the scene.

W Wavelength Dispersion

Spectral dispersion element (prism, grating) with shift α and aperture a.

R Rotation / Motion

Sample or gantry rotation (CT, electron tomography).

Λ Wavelength Selection

Spectral filter or monochromator selecting a wavelength band.