Elastography

Shear-Wave Elastography

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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
🥇 DiffElasto 0.880 39.2 0.953 ✓ Certified Gao et al. 2024
🥈 PhysElasto 0.851 37.8 0.942 ✓ Certified Chen et al. 2024
🥉 SwinElasto 0.826 36.6 0.932 ✓ Certified Wang et al. 2023
4 TransElasto 0.791 35.0 0.915 ✓ Certified Li et al. 2022
5 ElastoNet 0.730 32.5 0.876 ✓ Certified Tzschatzsch et al. 2021
6 DnCNN-Elasto 0.664 29.7 0.838 ✓ Certified Guo et al. 2019
7 AIDE 0.592 26.9 0.787 ✓ Certified Oliphant et al. 2001
8 DI-Elasto 0.539 24.8 0.752 ✓ Certified Van Houten et al. 2001
9 LFE-Elasto 0.477 22.3 0.710 ✓ Certified Manduca et al. 2001

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
🥇 DiffElasto + gradient 0.782
0.857
38.02 dB / 0.982
0.760
31.26 dB / 0.934
0.729
30.49 dB / 0.924
✓ Certified Gao et al., MICCAI 2024
🥈 SwinElasto + gradient 0.773
0.828
35.51 dB / 0.971
0.760
31.91 dB / 0.942
0.730
29.97 dB / 0.916
✓ Certified Wang et al., IEEE TMI 2023
🥉 PhysElasto + gradient 0.762
0.841
36.43 dB / 0.976
0.750
30.47 dB / 0.924
0.694
28.68 dB / 0.894
✓ Certified Chen et al., Magn. Reson. Med. 2024
4 TransElasto + gradient 0.742
0.782
32.22 dB / 0.945
0.754
31.09 dB / 0.932
0.689
28.03 dB / 0.881
✓ Certified Li et al., Magn. Reson. Med. 2022
5 ElastoNet + gradient 0.670
0.745
29.99 dB / 0.917
0.655
25.36 dB / 0.813
0.611
24.5 dB / 0.786
✓ Certified Tzschatzsch et al., IEEE TMI 2021
6 DnCNN-Elasto + gradient 0.587
0.693
26.97 dB / 0.857
0.546
20.98 dB / 0.645
0.522
20.57 dB / 0.626
✓ Certified Guo et al., Med. Phys. 2019
7 LFE-Elasto + gradient 0.498
0.516
20.02 dB / 0.600
0.490
19.27 dB / 0.563
0.488
19.83 dB / 0.590
✓ Certified Manduca et al., Magn. Reson. Imaging 2001
8 AIDE + gradient 0.457
0.638
24.6 dB / 0.789
0.408
16.26 dB / 0.414
0.326
14.28 dB / 0.322
✓ Certified Oliphant et al., Magn. Reson. Med. 2001
9 DI-Elasto + gradient 0.444
0.623
23.56 dB / 0.753
0.405
16.49 dB / 0.425
0.305
13.3 dB / 0.281
✓ Certified Van Houten et al., Magn. Reson. Med. 2001

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 DiffElasto + gradient 0.857 38.02 0.982
2 PhysElasto + gradient 0.841 36.43 0.976
3 SwinElasto + gradient 0.828 35.51 0.971
4 TransElasto + gradient 0.782 32.22 0.945
5 ElastoNet + gradient 0.745 29.99 0.917
6 DnCNN-Elasto + gradient 0.693 26.97 0.857
7 AIDE + gradient 0.638 24.6 0.789
8 DI-Elasto + gradient 0.623 23.56 0.753
9 LFE-Elasto + gradient 0.516 20.02 0.6
Spec Ranges (3 parameters)
Parameter Min Max Unit
shear_speed -0.3 0.6 m/s
push_duration -10.0 20.0 μs
tissue_viscosity -15.0 30.0 %
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 DiffElasto + gradient 0.760 31.26 0.934
2 SwinElasto + gradient 0.760 31.91 0.942
3 TransElasto + gradient 0.754 31.09 0.932
4 PhysElasto + gradient 0.750 30.47 0.924
5 ElastoNet + gradient 0.655 25.36 0.813
6 DnCNN-Elasto + gradient 0.546 20.98 0.645
7 LFE-Elasto + gradient 0.490 19.27 0.563
8 AIDE + gradient 0.408 16.26 0.414
9 DI-Elasto + gradient 0.405 16.49 0.425
Spec Ranges (3 parameters)
Parameter Min Max Unit
shear_speed -0.36 0.54 m/s
push_duration -12.0 18.0 μs
tissue_viscosity -18.0 27.0 %
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 SwinElasto + gradient 0.730 29.97 0.916
2 DiffElasto + gradient 0.729 30.49 0.924
3 PhysElasto + gradient 0.694 28.68 0.894
4 TransElasto + gradient 0.689 28.03 0.881
5 ElastoNet + gradient 0.611 24.5 0.786
6 DnCNN-Elasto + gradient 0.522 20.57 0.626
7 LFE-Elasto + gradient 0.488 19.83 0.59
8 AIDE + gradient 0.326 14.28 0.322
9 DI-Elasto + gradient 0.305 13.3 0.281
Spec Ranges (3 parameters)
Parameter Min Max Unit
shear_speed -0.21 0.69 m/s
push_duration -7.0 23.0 μs
tissue_viscosity -10.5 34.5 %
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

Shear-wave elastography (SWE) quantifies tissue stiffness by generating shear waves using an acoustic radiation force impulse (ARFI) push and tracking their propagation with ultrafast ultrasound imaging (10,000+ fps). The shear wave speed c_s is related to the shear modulus by mu = rho * c_s^2, enabling quantitative mapping of Young's modulus E = 3*mu (assuming incompressibility). The technique is clinically validated for liver fibrosis staging (F0-F4) and breast lesion characterization. Challenges include shear wave attenuation in deep tissue and reflections from boundaries.

Principle

Shear-wave elastography measures tissue stiffness by tracking the propagation speed of shear waves generated by an acoustic radiation force impulse (ARFI) or external vibration. Shear-wave speed is proportional to the square root of the shear modulus: cₛ = √(μ/ρ). Stiffer tissues (fibrosis, tumors) have faster shear-wave propagation. Results are displayed as quantitative elasticity maps (in kPa or m/s).

How to Build the System

Use a clinical ultrasound system with shear-wave elastography mode (Supersonic Imagine Aixplorer, Siemens ARFI/VTQ, or GE 2D-SWE). The transducer generates a focused push pulse to create shear waves, then tracks their propagation with ultrafast plane-wave imaging (up to 10,000 fps). Place the ROI in a region free of large vessels and interfaces. Patient should hold breath for liver measurements. Calibrate with an elasticity phantom.

Common Reconstruction Algorithms

  • Time-to-peak shear-wave arrival estimation
  • Phase-gradient shear-wave speed inversion
  • 2-D shear-wave elastography mapping (real-time SWE)
  • Transient elastography (FibroScan 1-D measurement)
  • Deep-learning elasticity estimation from B-mode + SWE data

Common Mistakes

  • Pre-compression by pressing transducer too hard, artifactually increasing stiffness
  • Measuring in the near-field where push pulse is unreliable
  • Not having patient hold breath for liver measurements (respiratory motion invalidates SWE)
  • Placing ROI near large vessels or liver capsule causing boundary artifacts
  • Not waiting for the measurement to stabilize (IQR/median >30 % indicates unreliable data)

How to Avoid Mistakes

  • Apply light transducer pressure with coupling gel; avoid compressing tissue
  • Place measurement ROI at 1.5-2 cm depth in liver; avoid the near-field zone
  • Instruct patient to suspend breathing calmly during each SWE measurement
  • Avoid ROI placement near vessels, liver edges, or ribs
  • Acquire ≥10 valid measurements and check IQR/median <30 % per EFSUMB guidelines

Forward-Model Mismatch Cases

  • The widefield fallback produces a 2D (64,64) image, but elastography measures tissue displacement/strain from mechanical wave propagation — output includes displacement maps at multiple time points
  • Elastography estimates tissue stiffness (Young's modulus) from shear wave speed, which requires tracking mechanical wave propagation through tissue — the widefield Gaussian blur has no connection to mechanical wave physics

How to Correct the Mismatch

  • Use the elastography operator that models mechanical excitation (acoustic radiation force or external vibration) and tracks the resulting tissue displacement using ultrasound or MRI phase encoding
  • Estimate shear wave speed from displacement propagation, then compute tissue stiffness: E = 3*rho*c_s^2, using the correct wave propagation and displacement tracking forward model

Experimental Setup — Signal Chain

Experimental setup diagram for Shear-Wave Elastography

Experimental Setup

Instrument: Supersonic Imagine Aixplorer MACH 30 / Siemens ACUSON Sequoia
Probe Frequency Mhz: 4.0
Push Frequency Hz: 50
Shear Wave Speed Range M S: 1-5
Method: ARFI / supersonic shear imaging (SSI)
Stiffness Range Kpa: 1-75
Ultrafast Frame Rate Fps: 10000
Application: liver fibrosis staging

Key References

  • Bercoff et al., 'Supersonic shear imaging: a new technique for soft tissue elasticity mapping', IEEE TUFFC 51, 396-409 (2004)
  • Barr et al., 'Elastography assessment of liver fibrosis', Radiology 276, 845-861 (2015)

Canonical Datasets

  • Clinical SWE liver fibrosis benchmark data

Spec DAG — Forward Model Pipeline

P(shear) → Σ_t → D(g, η₂)

P Shear-Wave Propagation (shear)
Σ Temporal Tracking (t)
D Ultrafast Array (g, η₂)

Mismatch Parameters

Symbol Parameter Description Nominal Perturbed
Δc_s shear_speed Shear speed error (m/s) 0 0.3
Δτ push_duration Push-pulse duration error (μs) 0 10
Δη tissue_viscosity Viscosity model error (%) 0 15.0

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.