Classification of Plaque Composition in Peripheral Arterial Disease with Multi-Contrast Histology using 7 Tesla and a Variational AutoEncoder

To differentiate soft versus hard peripheral arterial disease (PAD) plaques, a dedicated ultra-high field magnetic resonance imaging (MRI) histology protocol using three different image contrast (ultra-short TE, T1-weighted and T2-weighted) was used to scan on a clinical 7 Tesla human MRI scanner six amputated legs immediately after surgery that were harboring PAD lesions. A 2D convolutional network (CNN) variational autoencoder (VAE) was created. For each lesion, average classification values along the long axis of the artery were determined from latent space and ranged from partially occluded to patent (three lesions), soft-plaque occluded (two) and hard-plaque occluded (one).