Abstract 139: Semi-automated Classification Of Peripheral Artery Disease Lesion Composition From Multi-Contrast Magnetic Resonance Histology At 9.4 Tesla With A 2 Dimensional Convolutional Neural Network Variational AutoEncoder Algorithm

• Published in: Arteriosclerosis, thrombosis, and vascular biology Vol. 43; no. Suppl_1
• Main Authors: Csore, Judit, Karmonik, Christof, Lumsden, Alan B, Roy, Trisha L
• Format: Journal Article
• Language: English
• Published: 01.05.2023
• ISSN: 1079-5642; 1524-4636
• DOI: 10.1161/atvb.43.suppl_1.139

Abstract only Introduction: Using artificial intelligence (AI)-assisted methods on MRI histology may help endovascular peripheral artery disease (PAD) treatment planning. Objectives: To evaluate soft vs. hard PAD plaque components and crossability using multi-contrast high-resolution MRI, AI algorithm, and histologic samples. Methods: PAD lesions were harvested from 6 patients (one of each) after amputation and imaged using a 9.4T ultra-high resolution MRI scanner with 3 contrasts: T1-weighted (T1w), T2-weighted (T2w) and Ultrashort Echo Time (UTE). 3D imaging volumes were spatially registered and pseudo-color composites (T1w: red, T2w: green, UTE:blue) were created. Custom 2D variational autoencoder (VAE) classification AI algorithm using convolutional neural networks for image recognition classified and scored cross-sections as lumen occluded with soft tissue (tissue score (TS)=10), with soft and hard tissue (TS=30) and with hard tissue (TS= 50). 2 readers performed visual assessment of crossability (crossable/non-crossable) on preselected MRI slices compared to histologic samples (Fig1a), percent agreement was calculated for inter-rater reliability. Results: MRI multi-contrast registration succeeded for all section (pseudo-colors: smooth muscle cells- pink/red, fatty lesions-green, collagen-dark blue, calcification-black). VAE separated axial sections based on plaque composition (Fig1b), average TS per tissue segments ranged from 10 to 48.1 and corresponded well to the composition of tissues inspected visually.20 slices showed appropriate alignment, for the evaluation of crossability 11 were excluded due to lack of sufficient histological sample, inter-rater reliability was 100% on 9 slices (7 crossable, 2 non-crossable). Conclusion: AI algorithm for image recognition successfully distinguished lesion composition. AI-aided analysis may help rapid evaluation of lesion crossability in planning endovascular interventions.