Heartbeat OCT: in vivo intravascular megahertz-optical coherence tomography.
Ontology highlight
ABSTRACT:
SUBMITTER: Wang T
PROVIDER: S-EPMC4679274 | biostudies-other | 2015 Dec
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Intravascular optical coherence tomography (IVOCT) is rapidly becoming the method of choice for the in vivo investigation of coronary artery disease. While IVOCT visualizes atherosclerotic plaques with a resolution <20µm, image analysis in terms of tissue composition is currently performed by a time-consuming manual procedure based on the qualitative interpretation of image features. We illustrate an algorithm for the automated and systematic characterization of IVOCT atherosclerotic tissue. The proposed method consists in a supervised classification of image pixels according to textural features combined with the estimated value of the optical attenuation coefficient. IVOCT images of 64 plaques, from 49 in vivo IVOCT data sets, constituted the algorithm's training and testing data sets. Validation was obtained by comparing automated analysis results to the manual assessment of atherosclerotic plaques. An overall pixel-wise accuracy of 81.5% with a classification feasibility of 76.5% and per-class accuracy of 89.5%, 72.1% and 79.5% for fibrotic, calcified and lipid-rich tissue respectively, was found. Moreover, measured optical properties were in agreement with previous results reported in literature. As such, an algorithm for automated tissue characterization was developed and validated using in vivo human data, suggesting that it can be applied to clinical IVOCT data. This might be an important step towards the integration of IVOCT in cardiovascular research and routine clinical practice.
| S-EPMC3704084 | biostudies-literature
Project description:Optical coherence tomography (OCT) is a non-invasive imaging technique routinely used in ophthalmology to visualize and quantify the layers of the retina. It also provides information on optic nerve head topography, peripapillary retinal nerve fiber layer thickness, and macular volume, which correlate with axonal loss. These measurements are of particular interest in optic neuropathies and in multiple sclerosis, and OCT parameters are now used as endpoints in neurologic clinical trials.
| S-EPMC3005938 | biostudies-literature
Project description:Comprehensive imaging of both the structural and biochemical characteristics of atherosclerotic plaque is essential for the diagnosis and study of coronary artery disease because both a plaque's morphology and its biochemical composition affect the level of risk it poses. Optical coherence tomography (OCT) and fluorescence lifetime imaging (FLIm) are promising optical imaging methods for characterizing coronary artery plaques morphologically and biochemically, respectively. In this study, we present a hybrid intravascular imaging device, including a custom-built OCT/FLIm system, a hybrid optical rotary joint, and an imaging catheter, to visualize the structure and biochemical composition of the plaque in an atherosclerotic rabbit artery in vivo. Especially, the autofluorescence lifetime of the endogenous tissue molecules can be used to characterize the biochemical composition; thus no exogenous contrast agent is required. Also, the physical properties of the imaging catheter and the imaging procedures are similar to those already used clinically, facilitating rapid translation into clinical use. This new intravascular imaging catheter can open up new opportunities for clinicians and researchers to investigate and diagnose coronary artery disease by simultaneously providing tissue microstructure and biochemical composition data in vivo without the use of exogenous contrast agent.
| S-EPMC6162321 | biostudies-literature
Project description:Intravascular optical coherence tomography (IV-OCT) is an imaging modality that can be used for the assessment of intracoronary stents. Recent publications pointed to the fact that 3D visualizations have potential advantages compared to conventional 2D representations. However, 3D imaging still requires a time consuming manual procedure not suitable for on-line application during coronary interventions. We propose an algorithm for a rapid and fully automatic 3D visualization of IV-OCT pullbacks. IV-OCT images are first processed for the segmentation of the different structures. This also allows for automatic pullback calibration. Then, according to the segmentation results, different structures are depicted with different colors to visualize the vessel wall, the stent and the guide-wire in details. Final 3D rendering results are obtained through the use of a commercial 3D DICOM viewer. Manual analysis was used as ground-truth for the validation of the segmentation algorithms. A correlation value of 0.99 and good limits of agreement (Bland Altman statistics) were found over 250 images randomly extracted from 25 in vivo pullbacks. Moreover, 3D rendering was compared to angiography, pictures of deployed stents made available by the manufacturers and to conventional 2D imaging corroborating visualization results. Computational time for the visualization of an entire data sets resulted to be ~74 sec. The proposed method allows for the on-line use of 3D IV-OCT during percutaneous coronary interventions, potentially allowing treatments optimization.
| S-EPMC3521298 | biostudies-literature
Project description:AimsFibrin deposition and absent endothelium characterize unhealed stents that are at heightened risk of stent thrombosis. Optical coherence tomography (OCT) is increasingly used for assessing stent tissue coverage as a measure of healed stents, but cannot precisely identify whether overlying tissue represents physiological neointima. Here we assessed and compared fibrin deposition and persistence on bare metal stent (BMS) and drug-eluting stent (DES) using near-infrared fluorescence (NIRF) molecular imaging in vivo, in combination with simultaneous OCT stent coverage.Methods and resultsRabbits underwent implantation of one BMS and one DES without overlap in the infrarenal aorta (N = 20 3.5 × 12 mm). At Days 7 and/or 28, intravascular NIRF-OCT was performed following the injection of fibrin-targeted NIRF molecular imaging agent FTP11-CyAm7. Intravascular NIRF-OCT enabled high-resolution imaging of fibrin overlying stent struts in vivo, as validated by histopathology. Compared with BMS, DES showed greater fibrin deposition and fibrin persistence at Days 7 and 28 (P < 0.01 vs. BMS). Notably, for edge stent struts identified as covered by OCT on Day 7, 92.8 ± 9.5% of DES and 55.8 ± 23.6% of BMS struts were NIRF fibrin positive (P < 0.001). At Day 28, 18.6 ± 10.6% (DES) and 5.1 ± 8.7% (BMS) of OCT-covered struts remained fibrin positive (P < 0.001).ConclusionIntravascular NIRF fibrin molecular imaging improves the detection of unhealed stents, using clinically translatable technology that complements OCT. A sizeable percentage of struts deemed covered by OCT are actually covered by fibrin, particularly in DES, and therefore such stents might remain prothrombotic. These findings have implications for the specificity of standalone clinical OCT assessments of stent healing.
| S-EPMC5837565 | biostudies-literature
Project description:Thin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.
| S-EPMC10884035 | biostudies-literature
Project description:ObjectiveThe goal of this study was to evaluate the feasibility of imaging the aorta of apolipoprotein E-deficient (ApoE(-/-)) mice for the detection of atherosclerosis and macrophages using optical coherence tomography (OCT) compared with histology.Methods and resultsAtherosclerosis was induced by high-fat diet in 7-week-old ApoE(-/-) mice for 10 (n=7) and 22 (n=7) weeks. Nine-week-old ApoE(-/-) mice (n=7) fed a standard chow diet were used as controls. OCT images of a 10-mm descending aorta in situ were performed in 4 mice for each, and plaque and macrophages were determined at 0.5-mm intervals. Automated detection and quantification of macrophages were performed independently using a customized algorithm. Coregistered histological cross-sections were stained with hematoxylin-eosin, Mac-3, and von Kossa. Three mice in each group had en face OCT imaging to detect macrophages, which were compared with lipid-positive area with Sudan IV. OCT images were successfully acquired in all mice. OCT and histology were able to discriminate macrophages and plaque among the 3 groups and showed excellent correlation for (1) visual detection of plaque (r=0.98) and macrophages (r=0.93), (2) automated detection and quantification of macrophages by OCT versus Mac-3-positive area (r=0.92), and (3) en face OCT detection of macrophages versus Sudan IV-positive area (r=0.92).ConclusionsMurine intra-aortic OCT is feasible and shows excellent correlation with histology for detection of atherosclerotic plaque and macrophages.
| S-EPMC4091044 | biostudies-literature
Project description:Intravascular ultrasound (IVUS) and optical coherence tomography (OCT) are intravascular imaging technologies widely used in the cardiac catheterization laboratory. The impact of these modalities for optimizing the acute and longer-term clinical impact following percutaneous coronary intervention (PCI) is supported by a wealth of clinical evidence. Intravascular imaging provides unique information for enhanced lesion preparation, optimal stent sizing, recognizing post PCI complications, and the etiology of stent failure. This review compares and contrasts the key aspects of these imaging modalities during PCI.
| S-EPMC7666918 | biostudies-literature
Project description:Axonal and neuronal degeneration are important features of multiple sclerosis (MS) and other neurologic disorders that affect the anterior visual pathway. Optical coherence tomography (OCT) is a non-invasive technique that allows imaging of the retinal nerve fiber layer (RNFL), a structure which is principally composed of ganglion cell axons that form the optic nerves, chiasm, and optic tracts. Since retinal axons are nonmyelinated until they penetrate the lamina cribrosa, the RNFL is an ideal structure (no other central nervous system tract has this unique arrangement) for visualizing the processes of neurodegeneration, neuroprotection and, potentially, even neuro-repair. OCT is capable of providing high-resolution reconstructions of retinal anatomy in a rapid and reproducible fashion and permits objective analysis of the RNFL (axons) as well as ganglion cells and other neurons in the macula. In a systematic OCT examination of multiple sclerosis (MS) patients, RNFL thickness and macular volumes are reduced when compared to disease-free controls. Conspicuously, these changes, which signify disorganization of retinal structural architecture, occur over time even in the absence of a history of acute demyelinating optic neuritis. RNFL axonal loss in MS is most severe in those eyes with a corresponding reduction in low-contrast letter acuity (a sensitive vision test involving the perception of gray letters on a white background) and in those patients who exhibit the greatest magnitude of brain atrophy, as measured by validated magnetic resonance imaging techniques. These unique structure-function correlations make the anterior visual pathway an ideal model for investigating the effects of standard and novel therapies that target axonal and neuronal degeneration. We provide an overview of the physics of OCT, its unique properties as a non-invasive imaging technique, and its potential applications toward understanding mechanisms of brain tissue injury in MS, other optic neuropathies, and neurologic disorders.
| S-EPMC3075740 | biostudies-literature
Project description:Histological hematoxylin and eosin-stained (H&E) tissue sections are used as the gold standard for pathologic detection of cancer, tumor margin detection, and disease diagnosis. Producing H&E sections, however, is invasive and time-consuming. While deep learning has shown promise in virtual staining of unstained tissue slides, true virtual biopsy requires staining of images taken from intact tissue. In this work, we developed a micron-accuracy coregistration method [micro-registered optical coherence tomography (OCT)] that can take a two-dimensional (2D) H&E slide and find the exact corresponding section in a 3D OCT image taken from the original fresh tissue. We trained a conditional generative adversarial network using the paired dataset and showed high-fidelity conversion of noninvasive OCT images to virtually stained H&E slices in both 2D and 3D. Applying these trained neural networks to in vivo OCT images should enable physicians to readily incorporate OCT imaging into their clinical practice, reducing the number of unnecessary biopsy procedures.
| S-EPMC11006228 | biostudies-literature