title: Characterisation of atherosclerotic plaque by spectral similarity of radiofrequency intravascular ultrasound signals.
abstract: OBJECTIVE
  The aim of this paper is to report a method of atherosclerotic plaque tissue characterisation based on pattern recognition and assess its accuracy under conditions of potential clinical relevance.
  
  
  RESULTS
  Excised saline infused human arteries were imaged using IVUS with RF acquisition. 40% of the vessels were re-imaged with human blood infusion. A database of approximately 12000 image regions-of-interest (ROIs) of histologically established types was used to design a pattern recognition algorithm to predict the tissue type of a given ROI by comparing its RF-spectrum against the database, and also to estimate the confidence of prediction. Ex vivo validation demonstrated accuracies at the highest level of confidence as: 97%, 98%, 95%, and 98% for necrotic, lipidic, fibrotic and calcified regions respectively. Good agreement with histology was shown in an in vivo swine animal model.
  
  
  CONCLUSIONS
  Ex vivo validation demonstrated the ability to characterise plaque tissue using an IVUS+RF system and a method incorporating (1) full spectral information (2) spectral similarity (3) estimating confidence of characterisation and, (4) ability to characterise plaque imaged through blood. Promising results were demonstrated in a live animal model. This approach may have potential for accurate and reproducible plaque characterisation in vivo.
authors: Boston Scientific Corporation, Fremont, CA, USA. sathyans@bcsi.com
authors: Shashidhar
authors: S
authors: Sathyanarayana
authors: Stéphane
authors: S
authors: Carlier
authors: Wenguang
authors: W
authors: Li
authors: Lewis
authors: L
authors: Thomas
mesh_terms: D000465:Algorithms; D000818:Animals; D001158:Arteries / Q000737:chemistry / Q000000981:diagnostic imaging*; D050197:Atherosclerosis / Q000000981:diagnostic imaging* / Q000378:metabolism; D001344:Autopsy; D002114:Calcinosis / Q000000981:diagnostic imaging* / Q000378:metabolism; D004195:Disease Models, Animal; D005355:Fibrosis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008055:Lipids / Q000032:analysis; D009336:Necrosis; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D013552:Swine; D018084:Ultrasonography, Interventional
chemical_list: D008055:Lipids