title: An inverse scattering algorithm for the segmentation of the luminal border on intravascular ultrasound data.
abstract: Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a novel method for segmentation of the luminal border on IVUS images using the radio frequency (RF) raw signal based on a scattering model and an inversion scheme. The scattering model is based on a random distribution of point scatterers in the vessel. The per-scatterer signal uses a differential backscatter cross-section coefficient (DBC) that depends on the tissue type. Segmentation requires two inversions: a calibration inversion and a reconstruction inversion. In the calibration step, we use a single manually segmented frame and then solve an inverse problem to recover the DBC for the lumen and vessel wall (kappa(l) and kappa(w), respectively) and the width of the impulse signal theta. In the reconstruction step, we use the parameters from the calibration step to solve a new inverse problem: for each angle theta(i) of the IVUS data, we reconstruct the lumen-vessel wall interface. We evaluated our method using three 40MHz IVUS sequences by comparing with manual segmentations. Our preliminary results indicate that it is possible to segment the luminal border by solving an inverse problem using the IVUS RF raw signal with the scatterer model.
authors.affiliation: Computational Biomedicine Lab, Department of Computer Science, University of Houston, Houston, TX, USA.
authors.forename: E Gerardo
authors.initials: EG
authors.lastname: Mendizabal-Ruiz
authors.forename: George
authors.initials: G
authors.lastname: Biros
authors.forename: Ioannis A
authors.initials: IA
authors.lastname: Kakadiaris
mesh_terms: D000465:Algorithms; D000818:Animals; D001011:Aorta / Q000000981:diagnostic imaging*; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011817:Rabbits; D015203:Reproducibility of Results; D012542:Scattering, Radiation; D012680:Sensitivity and Specificity; D018084:Ultrasonography, Interventional / Q000379:methods*