Iterative Self-Organizing Atherosclerotic Tissue Labeling in Intravascular Ultrasound Images and Comparison With Virtual Histology

Abstract

Intravascular ultrasound (IVUS) is the predominant imaging modality in the field of interventional cardiology that provides real-time cross-sectional images of coronary arteries and the extent of atherosclerosis. Due to heterogeneity of lesions and stringent spatial/spectral behavior of tissues, atherosclerotic plaque characterization has always been a challenge and still is an open problem. In this paper, we present a systematic framework from in vitro data collection, histology preparation, IVUS-histology registration along with matching procedure, and finally a robust texture-derived unsupervised atherosclerotic plaque labeling. We have performed our algorithm on in vitro and in vivo images acquired with single-element 40 MHz and 64-elements phased array 20 MHz transducers, respectively. In former case, we have quantified results by local contrasting of constructed tissue colormaps with corresponding histology images employing an independent expert and in the latter case, virtual histology images have been utilized for comparison. We tackle one of the main challenges in the field that is the reliability of tissues behind arc of calcified plaques and validate the results through a novel random walks framework by incorporating underlying physics of ultrasound imaging. We conclude that proposed framework is a formidable approach for retrieving imperative information regarding tissues and building a reliable training dataset for supervised classification and its extension for in vivo applications.

A hybrid plaque characterization method using intravascular ultrasound images

“…As it is shown in Table 2 our results compare favorably to those reported in the literature [10][11][12][13] and only the methodology presented by Zhang et al [12] achieved higher overall classification accuracy. However, it should be stressed that their method was tested in 12 IVUS frames only and thus it is likely the reported results to overestimate the performance of their approach in clinical setting.…”

“…However, this process is time-consuming and limited by high inter-and intra-observer variability [7]. In order to overcome these pitfalls several plaque characterization [8][9][10][11][12][13][14][15] and plaque segmentation [16] methodologies have been introduced.…”

“…In contrast to the grayscale IVUS images that are constructed based on the amplitude of the backscatter signal these data entail also information about the signal which also depends on the imaged tissue. Several methodologies have been proposed to process these data and numerous histology based studies have demonstrated that these techniques permit reliable identification of different plaque components [8][9][10][11]17]. However, the software designed for the RF analysis of the IVUS backscatter data is not always available and thus quite often the plaque characterization is based on grayscale IVUS images.…”

Virtual Inflation of the Cerebral Artery Wall for the Integrated Exploration of OCT and Histology Data

“…Katouzian et al. [KKSea12] correlated IVUS with histological imaging. They created a cage fixture setup for an in vitro experiment.…”

“…Future work should include automatically segmented vessel walls of the OCT data [TSDS*11]. We are also particularly interested in a setup similar to the cage fixture [KKSea12] for an in vitro setup. However, their work is based on IVUS, whereas OCT would require different adaptions.…”