title: Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound.
abstract: Intravascular Ultrasound (IVUS) is a predominant imaging modality in interventional cardiology. It provides real-time cross-sectional images of arteries and assists clinicians to infer about atherosclerotic plaques composition. These plaques are heterogeneous in nature and constitute fibrous tissue, lipid deposits and calcifications. Each of these tissues backscatter ultrasonic pulses and are associated with a characteristic intensity in B-mode IVUS image. However, clinicians are challenged when colocated heterogeneous tissue backscatter mixed signals appearing as non-unique intensity patterns in B-mode IVUS image. Tissue characterization algorithms have been developed to assist clinicians to identify such heterogeneous tissues and assess plaque vulnerability. In this paper, we propose a novel technique coined as Stochastic Driven Histology (SDH) that is able to provide information about co-located heterogeneous tissues. It employs learning of tissue specific ultrasonic backscattering statistical physics and signal confidence primal from labeled data for predicting heterogeneous tissue composition in plaques. We employ a random forest for the purpose of learning such a primal using sparsely labeled and noisy samples. In clinical deployment, the posterior prediction of different lesions constituting the plaque is estimated. Folded cross-validation experiments have been performed with 53 plaques indicating high concurrence with traditional tissue histology. On the wider horizon, this framework enables learning of tissue-energy interaction statistical physics and can be leveraged for promising clinical applications requiring tissue characterization beyond the application demonstrated in this paper.
authors.affiliation: Computer Aided Medical Procedures, Technische Universität München, Germany; School of Medical Science and Technology, Indian Institute of Technology Kharagpur, India. Electronic address: debdoot@smst.iitkgp.ernet.in.
authors.forename: Debdoot
authors.initials: D
authors.lastname: Sheet
authors.forename: Athanasios
authors.initials: A
authors.lastname: Karamalis
authors.forename: Abouzar
authors.initials: A
authors.lastname: Eslami
authors.forename: Peter
authors.initials: P
authors.lastname: Noël
authors.forename: Jyotirmoy
authors.initials: J
authors.lastname: Chatterjee
authors.forename: Ajoy K
authors.initials: AK
authors.lastname: Ray
authors.forename: Andrew F
authors.initials: AF
authors.lastname: Laine
authors.forename: Stephane G
authors.initials: SG
authors.lastname: Carlier
authors.forename: Nassir
authors.initials: N
authors.lastname: Navab
authors.forename: Amin
authors.initials: A
authors.lastname: Katouzian
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging*; D003627:Data Interpretation, Statistical; D004452:Echocardiography / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012542:Scattering, Radiation; D012680:Sensitivity and Specificity; D018084:Ultrasonography, Interventional / Q000379:methods*
keywords: Intravascular ultrasound; Machine learning; Nakagami distribution; Tissue characterization; Ultrasound signal confidence