Algorithm optimization for quantitative analysis of intravascular optical coherence tomography data

2009 ◽  
Author(s):  
Gijs van Soest ◽  
Thadé P. M. Goderie ◽  
Sander van Noorden ◽  
Anton F. W. van der Steen
Keyword(s):  
Optical Coherence Tomography ◽  
Quantitative Analysis ◽  
Optical Coherence ◽  
Algorithm Optimization ◽  
Intravascular Optical Coherence Tomography ◽  
Tomography Data

Optical coherence tomography for elucidation of flow-diversion phenomena: The concept of endothelized mural thrombus behind reversible in-stent stenosis in flow-diverters

2021 ◽  
pp. 159101992110034
Author(s):  
Andre Monteiro ◽  
Demetrius K Lopes ◽  
Amin Aghaebrahim ◽  
Ricardo Hanel
Keyword(s):  
Optical Coherence Tomography ◽  
Intracranial Aneurysms ◽  
Flow Diversion ◽  
Optical Coherence ◽  
Recurrence Rates ◽  
Oct Imaging ◽  
Intravascular Optical Coherence Tomography ◽  
Stenotic Area ◽  
Flow Diverters ◽  
Stent Stenosis

Purpose Flow-diverters have revolutionized the endovascular treatment of intracranial aneurysms, offering a durable solution to aneurysms with high recurrence rates after conventional stent-assisted coiling. Events that occur after treatment with flow-diversion, such as in-stent stenosis (ISS) are not well understood and require further assessment. After assessing an animal model with Optical Coherence Tomography (OCT), we propose a concept that could explain the mechanism causing reversible ISS after treatment of intracranial aneurysms with flow-diverters. Methods Six Pipeline Flex embolization devices (PED-Flex), six PED with Shield technology (PED-Shield), and four Solitaire AB devices were implanted in the carotid arteries (two stents per vessel) of four pigs. Intravascular optical coherence tomography (OCT) and digital subtraction angiography (DSA) images obtained on day 21 were compared to histological specimens. Results A case of ISS in a PED-Flex device was assessed with OCT imaging. Neointima with asymmetrical topography completely covering the PED struts was observed. Histological preparations of the stenotic area demonstrated thrombus on the surface of device struts, covered by neointima. Conclusion This study provides a plausible concept for reversible ISS in flow-diverters. Based on an observation of a previous experiment, we propose that similar cases of ISS are related to thrombus presence underneath endothelization, but further experiments focused on this phenomenon are needed. Optical Coherence Tomography will be useful tool when available for clinical use.

Coronary Stent Reconstruction in Intravascular Optical Coherence Tomography

2021 ◽  
Vol 41 (4) ◽  
pp. 0417001
Author(s):  
刘铁根 Liu Tiegen ◽  
陶魁园 Tao Kuiyuan ◽  
丁振扬 Ding Zhenyang ◽  
刘琨 Liu Kun ◽  
江俊峰 Jiang Junfeng ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Coronary Stent ◽  
Optical Coherence ◽  
Intravascular Optical Coherence Tomography

Abstract 15219: Fully Automated Calcification Segmentation in Intravascular Optical Coherence Tomography Images Using a Two-step Deep Learning Approach

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Juhwan Lee ◽  
Yazan Gharaibeh ◽  
Vladislav N Zimin ◽  
Luis A Dallan ◽  
Hiram G Bezerra ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Learning Approach ◽  
Optical Coherence ◽  
Stent Deployment ◽  
Automated Method ◽  
Increased Risk ◽  
Maximum Angle ◽  
Intravascular Optical Coherence Tomography ◽  
Sensitivity Specificity

Introduction: Major calcifications are of great concern when performing percutaneous coronary intervention as they hinder stent deployment. Calcifications can lead to under-expansion and strut malapposition, with increased risk of thrombosis and in-stent restenosis. Therefore, accurate identification, visualization, and quantification of calcifications are important. Objective: In this study, we developed a 2-step deep learning approach to enable segmentation of major calcifications in a typical 500+ frame intravascular optical coherence tomography (IVOCT) images. Methods: The dataset consisted of a total of 12,551 IVOCT frames across 68 patients with 68 pullbacks. We applied a series of pre-processing steps including guidewire/shadow removal, lumen detection, pixel shifting, and Gaussian filtering. To detect the major calcifications in step 1, we implemented the 3D convolutional neural network consisting of 5 convolutional, 5 max-pooling, and 2 fully-connected layers. In step-2, SegNet deep learning model was used to segment calcified plaques. In both steps, classification errors were reduced using conditional random field. Results: Step-1 reliably identified major calcifications (sensitivity/specificity: 97.7%/87.7%). Semantic segmentation of calcifications following step-2 was typically visually quite good (Fig. 1) with (sensitivity/specificity: 86.2%/96.7%). Our method was superior to a single step approach and showed excellent reproducibility on repetitive IVOCT pullbacks, with very small differences of clinically relevant attributes (maximum angle, maximum thickness, and length) and the exact same IVOCT calcium scores for assessment of stent deployment. Conclusions: We developed the fully-automated method for identifying calcifications in IVOCT images based on a 2-step deep learning approach. Extensive analyses indicate that our method is very informative for both live-time treatment planning and research purposes.

Abstract 18844: Diagnosis of Thin-capped Fibroatheromas in Intravascular Optical Coherence Tomography Images: Effects of Light Scattering

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Taylor Hoyt ◽  
Jennifer Phipps ◽  
Deborah Vela ◽  
Tianyi Wang ◽  
Maximillian Buja ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Light Scattering ◽  
Coronary Arteries ◽  
Foam Cell ◽  
Imaging Modality ◽  
Fibrous Tissue ◽  
Image Feature ◽  
Optical Coherence ◽  
Fibrous Cap ◽  
Intravascular Optical Coherence Tomography

Objectives: Intravascular optical coherence tomography (IVOCT) images are recorded by detecting light backscattered within coronary arteries. We hypothesize that non- thin-cap fibroatheroma (TCFA) etiologies may scatter light to create the false appearance of IVOCT TCFA. Background: Conflicting reports are recognized about the accuracy of IVOCT for TCFA detection. Methods: Ten human cadaver hearts were imaged with IVOCT (N=14 arteries). Coronary arteries were sectioned at 120 μm intervals. IVOCT and histologic TCFA were co-registered and compared. Results: Of 21 IVOCT TCFAs identified by two independent IVOCT core labs (fibrous cap <65 μm, lipid arc >90°), only 8 were true histologic TCFA. Foam cell infiltration was responsible for 62% of cases in which either thick-capped fibroatheromas (ThKFAs) appeared like TCFAs or arterial tissue appeared like TCFAs when no lipid core was present. Other false IVOCT TCFA etiologies included SMC-rich fibrous tissue (15%) and loose connective tissue (8%). If the lipid arc >90° criterion was disregarded, 45 IVOCT TCFAs were identified, and sensitivity of IVOCT TCFA detection increased from 53% to 88%; specificity remained high at 93%, and the presence of a new IVOCT image feature called “bright streaks” increased positive predictive value (PPV) to 53%. New mechanisms for light scattering are proposed to explain the low PPV of IVOCT to identify true TCFA (44%), and explain why other plaque components can masquerade as IVOCT TCFA. Conclusions: IVOCT can exhibit up to 88% sensitivity and 98% specificity to detect TCFA, but PPV is limited due to multiple etiologies that cause light scattering similar to true TCFA. Disregarding the lipid arc >90° IVOCT TCFA requirement, and the identification of a new feature, bright steaks, can enhance the ability of IVOCT to detect TCFA. Combining IVOCT with another imaging modality that more specifically recognizes lipid will be important for increasing PPV in the future.

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