Figure 1
(A,B) Conventional greyscale and color-coded ultrashort echo time magnetic resonance imaging (UTE MRI) images of stenotic lesion; yellow outline: patent lumen; purple outline: thickened vessel wall with lipid rich and collagenous deposits typical in atherosclerosis; red outline: concentric and central calcium deposits. (C,D) Conventional greyscale and color-coded UTE MRI images of occlusive lesion; orange outline: soft collagenous component; red outline: eccentric calcium deposit.
Table 1
Summary of technical advantage of novel ultrashort echo time magnetic resonance imaging (UTE MRI) protocol. CTA: computed tomography angiography; NPV: negative predictive value
| FEATURE | UTE MRI | CONVENTIONAL MRI | CTA |
|---|---|---|---|
| Calcium and collagenous tissue | Clearly visualized | Signal void or artifacts | Blooming artifacts (especially calcium) |
| Soft/hard plaque distinction | Yes | Poor or none | Poor or none |
| Predictive value for PVI | High (sensitivity 83%, NPV 96%)21,22 | Low | Moderate |
| Radiation exposure | None | None | Present |
| Contrast requirement | Not needed | Often required | Required |
Table 2
Comparison of clinical 7T and preclinical 9.4T and clinical 3T magnetic resonance imaging (MRI)-based AI models for PAD lesion classification. AI: artificial intelligence; PAD: peripheral artery disease; VAE: variational autoencoder; MPR: multiplanar reconstructions; UTE: ultra-short echo time; CNN: convolutional neural network; RGB: red, green, blue
| FEATURE | VAE & 7T MRI STUDY | VAE & 9.4T MRI STUDY | VAE & 3T MRI STUDY |
|---|---|---|---|
| MRI field strength | 7 Tesla (clinical ultra-high field) | 9.4 Tesla (preclinical ultra-high field) | 3 Tesla (clinical standard field) |
| Sample size | 5 amputated limbs | 6 amputated limbs | 5 amputated limbs |
| Image dataset | 2,390 MPR pseudo-color images | 4,014 MPR pseudo-color images | 797 convention greyscale images |
| Input imaging sequences | T1w, T2w, UTE | T2w, UTE (RGB composite) | UTE only |
| AI model used | VAE with 2D CNN | VAE + Gaussian mixture model (GMM) in latent space | 2D convolutional VAE |
| Classification approach | Semi-supervised; manual quadrant division in latent space | Fully unsupervised; clustering via GMM in latent space | Unsupervised; tissue score via Euclidean distance in latent space |
| Output classes | (1) Patent lumen, (2) partially patent, (3) soft occlusion, (4) hard occlusion | (1) Concentric calcified, (2) eccentric calcified, (3) hard occlusion, (4) soft occlusion | Patent to occlusive continuum, differentiated by soft vs hard tissue features |
| Tissue scoring system | Numeric tissue score (TS: 0-5) for each lesion | Latent space clustering; classification probabilities | Tissue score based on latent space separation (0-1.26) |
| Key strength | Clinically feasible, interpretable results, real-world applicability | High resolution, high diagnostic precision, excellent inter-rater reliability | Short acquisition time, no contrast or multi-sequence required, high scalability |
| Crossability prediction | Inferred through TS and plaque type | Achieved 100% inter-rater reliability in predicting lesion crossability | Aligned with expert visual assessment; scalable clinical proxy for lesion severity |
| Clinical integration potential | High–faster scans, easier implementation | Lower–limited by cost, availability, and scan time | Very high–compatible with clinical MRI workflows, < 15 min scan time |
| Best use case | Real-time clinical support and triage | Advanced preprocedural planning and research use | Point-of-care decision-making; standard clinical PAD assessment |
| Main contribution | Introduced AI-based semi-automated TS classification for PAD | Demonstrated unsupervised deep learning for lesion phenotyping with guidewire relevance | Validation of feasibility of using only UTE sequence on 3T clinical scanner for lesion assessment |
Figure 2
(A) Peripheral artery disease tissue type groups in the latent space separated by a custom-made variational autoencoder from magnetic resonance imaging-histology images.43 (B) Data in latent space generated by the variational encoder showing crossable and non-crossable lesions. Hard tissues are represented by dark green/black colors, whereas soft tissues are represented by lighter red/green colors.44 (C) Depiction 2D latent space illustrating the separation of the patent lumen (upper right) to occlusion (lower left). Red dots indicate the location of cross-sections in (D) calculated by the decoder of the convolutional variational autoencoder. Colored arrows point to the approximate location of lesions in (D); yellow: occlusive; green: partially patent