Doppler Ultrasound

Doppler Ultrasound

Join the Competition Contribute to Benchmark

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
🥇 DiffDoppler 0.882 39.3 0.954 ✓ Certified Gao et al. 2024
🥈 PhysDoppler 0.853 37.9 0.942 ✓ Certified Perdios et al. 2024
🥉 SwinDoppler 0.829 36.8 0.932 ✓ Certified Li et al. 2023
4 TransFlow 0.792 35.1 0.914 ✓ Certified Wang et al. 2022
5 FlowNet-US 0.726 32.4 0.872 ✓ Certified Nair et al. 2020
6 DnCNN-Doppler 0.658 29.5 0.832 ✓ Certified Perdios et al. 2018
7 MV-Doppler 0.586 26.8 0.778 ✓ Certified Langeland et al. 2003
8 VENC-Flow 0.521 24.1 0.738 ✓ Certified Moran 1982
9 CF-Doppler 0.481 22.5 0.712 ✓ Certified Evans & McDicken 2000

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
🥇 SwinDoppler + gradient 0.775
0.808
34.94 dB / 0.967
0.790
33.65 dB / 0.958
0.727
29.39 dB / 0.907
✓ Certified Li et al., Ultrasound Med. Biol. 2023
🥈 PhysDoppler + gradient 0.773
0.841
36.17 dB / 0.974
0.759
31.61 dB / 0.938
0.719
28.59 dB / 0.893
✓ Certified Perdios et al., Sci. Rep. 2024
🥉 DiffDoppler + gradient 0.762
0.838
36.74 dB / 0.977
0.731
30.41 dB / 0.923
0.716
29.1 dB / 0.902
✓ Certified Gao et al., MICCAI 2024
4 TransFlow + gradient 0.757
0.784
32.63 dB / 0.949
0.753
31.72 dB / 0.940
0.733
30.26 dB / 0.921
✓ Certified Wang et al., IEEE TUFFC 2022
5 FlowNet-US + gradient 0.600
0.742
29.56 dB / 0.910
0.573
22.66 dB / 0.717
0.486
19.26 dB / 0.563
✓ Certified Nair et al., IEEE TMI 2020
6 DnCNN-Doppler + gradient 0.551
0.692
27.05 dB / 0.859
0.503
19.78 dB / 0.588
0.457
18.85 dB / 0.542
✓ Certified Perdios et al., IEEE TUFFC 2018
7 VENC-Flow + gradient 0.523
0.562
21.57 dB / 0.671
0.536
21.0 dB / 0.646
0.472
18.68 dB / 0.534
✓ Certified Moran, Magn. Reson. Imaging 1982
8 MV-Doppler + gradient 0.498
0.666
25.4 dB / 0.815
0.451
18.38 dB / 0.519
0.377
15.6 dB / 0.382
✓ Certified Langeland et al., IEEE TUFFC 2003
9 CF-Doppler + gradient 0.474
0.523
20.17 dB / 0.607
0.472
19.15 dB / 0.557
0.427
17.16 dB / 0.458
✓ Certified Evans & McDicken, Doppler Ultrasound 2000

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 PhysDoppler + gradient 0.841 36.17 0.974
2 DiffDoppler + gradient 0.838 36.74 0.977
3 SwinDoppler + gradient 0.808 34.94 0.967
4 TransFlow + gradient 0.784 32.63 0.949
5 FlowNet-US + gradient 0.742 29.56 0.91
6 DnCNN-Doppler + gradient 0.692 27.05 0.859
7 MV-Doppler + gradient 0.666 25.4 0.815
8 VENC-Flow + gradient 0.562 21.57 0.671
9 CF-Doppler + gradient 0.523 20.17 0.607
Spec Ranges (4 parameters)
Parameter Min Max Unit
sos 1525.0 1570.0 m/s
doppler_angle -5.0 10.0 deg
wall_filter 20.0 110.0 Hz
prf -1.0 2.0 %
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 SwinDoppler + gradient 0.790 33.65 0.958
2 PhysDoppler + gradient 0.759 31.61 0.938
3 TransFlow + gradient 0.753 31.72 0.94
4 DiffDoppler + gradient 0.731 30.41 0.923
5 FlowNet-US + gradient 0.573 22.66 0.717
6 VENC-Flow + gradient 0.536 21.0 0.646
7 DnCNN-Doppler + gradient 0.503 19.78 0.588
8 CF-Doppler + gradient 0.472 19.15 0.557
9 MV-Doppler + gradient 0.451 18.38 0.519
Spec Ranges (4 parameters)
Parameter Min Max Unit
sos 1522.0 1567.0 m/s
doppler_angle -6.0 9.0 deg
wall_filter 14.0 104.0 Hz
prf -1.2 1.8 %
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 TransFlow + gradient 0.733 30.26 0.921
2 SwinDoppler + gradient 0.727 29.39 0.907
3 PhysDoppler + gradient 0.719 28.59 0.893
4 DiffDoppler + gradient 0.716 29.1 0.902
5 FlowNet-US + gradient 0.486 19.26 0.563
6 VENC-Flow + gradient 0.472 18.68 0.534
7 DnCNN-Doppler + gradient 0.457 18.85 0.542
8 CF-Doppler + gradient 0.427 17.16 0.458
9 MV-Doppler + gradient 0.377 15.6 0.382
Spec Ranges (4 parameters)
Parameter Min Max Unit
sos 1529.5 1574.5 m/s
doppler_angle -3.5 11.5 deg
wall_filter 29.0 119.0 Hz
prf -0.7 2.3 %
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

Doppler ultrasound measures blood flow velocity by detecting the frequency shift of ultrasound echoes reflected from moving red blood cells. The Doppler shift f_d = 2*f_0*v*cos(theta)/c relates velocity v to the observed frequency shift. Color Doppler maps 2D velocity fields by applying autocorrelation estimators to ensembles of pulse-echo data at each spatial location. A wall filter (high-pass) separates slow tissue clutter from blood flow signals. Challenges include aliasing when velocity exceeds the Nyquist limit (PRF/2) and angle-dependence of the velocity estimate.

Principle

Doppler ultrasound measures blood flow velocity by detecting the frequency shift of echoes reflected from moving red blood cells. The Doppler equation relates the frequency shift to velocity: Δf = 2f₀·v·cos(θ)/c, where θ is the beam-flow angle. Color Doppler maps velocity spatially, spectral Doppler provides velocity-time waveforms at a sample volume, and power Doppler shows flow amplitude regardless of direction.

How to Build the System

Use a clinical ultrasound system with Doppler capability. For vascular studies, use a linear array transducer (5-12 MHz). Steer the beam to achieve a Doppler angle <60° to the vessel axis. Set the velocity scale (PRF) to match expected flow speeds (avoid aliasing). For spectral Doppler, place the sample volume within the vessel lumen and adjust the gate size. Angle correction must be applied for accurate velocity measurements.

Common Reconstruction Algorithms

  • Autocorrelation-based color flow estimation (Kasai algorithm)
  • FFT spectral analysis for pulsed-wave Doppler
  • Clutter filtering (wall filtering) to remove tissue motion
  • Power Doppler (amplitude mode) for slow flow detection
  • Ultrafast Doppler (plane-wave compounding) for functional ultrasound

Common Mistakes

  • Doppler angle >60° causing large velocity measurement errors
  • Aliasing in color or spectral Doppler from PRF set too low for flow velocity
  • Wall filter too aggressive, eliminating slow venous flow signals
  • Blooming artifact in color Doppler from excessive gain
  • Not correcting for angle in spectral Doppler velocity measurements

How to Avoid Mistakes

  • Maintain Doppler angle <60°; ideally 30-60° for best accuracy
  • Increase PRF (velocity scale) until aliasing resolves; or use CW Doppler
  • Reduce wall filter setting when looking for slow flow (venous, microvascular)
  • Reduce color Doppler gain until color just fills the vessel without overflow
  • Always apply angle correction cursor parallel to the vessel wall for spectral Doppler

Forward-Model Mismatch Cases

  • The widefield fallback produces a 2D (64,64) image, but Doppler ultrasound acquires velocity-encoded data — output includes blood flow velocity maps estimated from phase shifts between consecutive pulses
  • Doppler measurement relies on the frequency shift of backscattered ultrasound from moving blood cells (f_d = 2*v*cos(theta)*f_0/c) — the widefield spatial blur has no velocity or frequency-shift information

How to Correct the Mismatch

  • Use the Doppler ultrasound operator that models pulsed-wave Doppler: multiple pulses along each line, with phase differences between returns encoding blood flow velocity
  • Estimate velocity using autocorrelation (Kasai estimator) or spectral Doppler analysis on the correctly modeled multi-pulse RF data, then map to color flow images

Experimental Setup — Signal Chain

Experimental setup diagram for Doppler Ultrasound

Experimental Setup

Instrument: GE LOGIQ E10 / Philips EPIQ Elite
Probe Frequency Mhz: 5.0
Prf Khz: 10.0
Ensemble Length: 16
Wall Filter: polynomial regression / SVD clutter filter
Velocity Range Cm S: 0-200
Application: carotid / renal flow imaging

Key References

  • Kasai et al., 'Real-time two-dimensional blood flow imaging using an autocorrelation technique', IEEE Trans. Sonics Ultrasonics 32, 458-464 (1985)

Canonical Datasets

  • Clinical Doppler benchmark collections

Spec DAG — Forward Model Pipeline

P(acoustic) → Σ_t → D(g, η₂)

P Acoustic Propagation (acoustic)
Σ Temporal Integration (t)
D Piezo Array (g, η₂)

Mismatch Parameters

Symbol Parameter Description Nominal Perturbed
Δc sos Speed-of-sound error (m/s) 1540 1555
Δθ doppler_angle Doppler angle error (deg) 0 5.0
Δf_w wall_filter Wall filter cutoff error (Hz) 50 80
ΔPRF prf PRF jitter (%) 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.