X-ray Angiography
Digital subtraction angiography (DSA) visualizes blood vessels by subtracting a pre-contrast mask image from post-contrast images acquired after injecting iodinated contrast agent. The subtraction eliminates static anatomy, isolating vascular structures. The forward model is y_post - y_pre = Delta_mu * t_vessel + n where Delta_mu is the attenuation increase from iodine. Primary challenges include patient motion between mask and contrast frames, breathing artifacts, and superposition of overlapping vessels.
Subtraction Projection
Poisson
dsa subtraction
FLAT_PANEL_DETECTOR
Forward-Model Signal Chain
Each primitive represents a physical operation in the measurement process. Arrows show signal flow left to right.
Π(proj) → D(g, η₁)
Benchmark Variants & Leaderboards
X-ray Angiography
X-ray Angiography
Π(proj) → D(g, η₁)
Standard Leaderboard (Top 10)
| # | Method | Score | PSNR (dB) | SSIM | Trust | Source |
|---|---|---|---|---|---|---|
| 🥇 | DiffusionAngio | 0.847 | 36.8 | 0.967 | ✓ Certified | Shen et al., Med. Image Anal. 2024 |
| 🥈 | AngioFormer | 0.833 | 36.2 | 0.960 | ✓ Certified | Geometry-aware transformer 3DRA, 2024 |
| 🥉 | NeRF-Angio | 0.824 | 35.8 | 0.955 | ✓ Certified | Wang et al., IEEE TMI 43:1401, 2024 |
| 4 | VesselNet | 0.811 | 35.2 | 0.948 | ✓ Certified | Zhang et al., Radiology AI 2024 |
| 5 | Learned Primal-Dual | 0.792 | 34.5 | 0.935 | ✓ Certified | Adler & Oktem, IEEE TMI 2018 |
| 6 | FBPConvNet | 0.768 | 33.5 | 0.920 | ✓ Certified | Jin et al., IEEE TIP 2017 |
| 7 | PnP-ADMM | 0.730 | 32.0 | 0.893 | ✓ Certified | Venkatakrishnan et al., 2013 |
| 8 | TV-CS | 0.688 | 30.5 | 0.860 | ✓ Certified | Sidky et al., Phys. Med. Biol. 2008 |
| 9 | FDK | 0.590 | 27.0 | 0.780 | ✓ Certified | Feldkamp et al., JOSA A 1984 |
Mismatch Parameters (3) click to expand
| Name | Symbol | Description | Nominal | Perturbed |
|---|---|---|---|---|
| contrast_timing | Δt_c | Contrast bolus timing error (s) | 0 | 0.5 |
| motion | σ_m | Patient motion (mm) | 0 | 2.0 |
| scatter | f_s | Scatter fraction error | 0 | 0.05 |
Reconstruction Triad Diagnostics
The three diagnostic gates (G1, G2, G3) characterize how reconstruction quality degrades under different error sources. Each bar shows the relative attribution.
Model: subtraction projection — Mismatch modes: motion misregistration, breathing artifact, bowel gas motion, contrast timing mismatch
Noise: poisson — Typical SNR: 15.0–35.0 dB
Requires: mask registration, pixel shift correction, contrast timing, flat field
Modality Deep Dive
Principle
X-ray angiography visualizes blood vessels by injecting iodinated contrast agent and acquiring rapid-sequence fluoroscopic images. Digital Subtraction Angiography (DSA) subtracts a pre-contrast mask image from post-contrast frames, removing bone and soft tissue to show only the contrast-filled vasculature with high contrast and spatial resolution.
How to Build the System
Use a biplane or single-plane angiography suite with high-speed flat-panel detectors (30-60 fps capability). The C-arm provides multi-angle positioning. Power injector delivers iodinated contrast (350-370 mgI/mL) at controlled rates. Road-mapping mode overlays vessel map on live fluoro for catheter guidance. 3-D rotational angiography acquires a spin to reconstruct a volume of the vasculature.
Common Reconstruction Algorithms
- Digital subtraction (mask-live image subtraction)
- Pixel shifting for motion compensation in DSA
- 3-D rotational angiography reconstruction (FDK or iterative)
- Time-density curve analysis for perfusion assessment
- Deep-learning vessel segmentation and stenosis quantification
Common Mistakes
- Patient motion between mask and contrast frames causing misregistration artifacts
- Inadequate contrast bolus timing causing suboptimal vessel opacification
- Overexposure or underexposure of the detector outside the linear range
- Bowel gas or cardiac motion causing subtraction artifacts
- Injecting contrast too fast, creating reflux or missing distal vessels
How to Avoid Mistakes
- Instruct patients to remain still; use pixel shifting or elastic registration
- Use test bolus or timing run to determine optimal injection-to-imaging delay
- Use automatic dose rate control; verify detector within calibrated dynamic range
- Use cardiac gating for coronary or thoracic angiography
- Adjust injection rate and volume to vessel size and flow characteristics
Forward-Model Mismatch Cases
- The widefield fallback applies Gaussian blur, but angiography uses X-ray transmission with iodine contrast agent — the exponential attenuation model with contrast-enhanced vessels is not a simple convolution
- Digital subtraction angiography (DSA) requires temporal subtraction between pre- and post-contrast images to isolate vessels — the widefield model has no temporal component and cannot model contrast dynamics
How to Correct the Mismatch
- Use the angiography operator implementing contrast-enhanced X-ray transmission: y = I_0 * exp(-(mu_tissue*t + mu_iodine*c(t))) where c(t) models contrast agent concentration dynamics
- Apply temporal subtraction (post-contrast minus pre-contrast) or parametric mapping of contrast kinetics using the correct time-resolved forward model
Experimental Setup
Siemens Artis Q / Philips Allura Xper FD20
1024x1024
80
30
iodinated (Iopamidol 370 mg I/mL)
4.0
flat-panel (CsI)
cerebral / coronary angiography
Signal Chain Diagram
Key References
- Defined by clinical DSA standards (ACC/AHA guidelines)
Canonical Datasets
- IntrA (intracranial aneurysm 3DRA dataset)