Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the... Show moreIntravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS1 to automatically segment IVUS images in a user-friendly environment. Show less
Vascular inflammation, lipid metabolism, and thrombogenicity play a key role not only in atherogenesis but also in the development of acute coronary syndromes. Biomarkers associated with coronary... Show moreVascular inflammation, lipid metabolism, and thrombogenicity play a key role not only in atherogenesis but also in the development of acute coronary syndromes. Biomarkers associated with coronary high-risk plaques defined according to intravascular imaging have not been systematically studied. A total of 69 patients with coronary artery disease who underwent both optical coherence tomography and intravascular ultrasound imaging, and who provided blood specimens were included. Comprehensive biomarkers for inflammation, lipid, and coagulation were analyzed. Composite models sought biomarker patterns associated with thin-cap fibroatheroma (TCFA) and "high-risk plaques" (TCFA and large plaque burden). Two different composite models were developed for TCFA, based on the finding that high sensitivity C-reactive protein (hsCRP), plasminogen activator inhibitor-1, fibrinogen, IL-6, homocysteine and amyloid A levels were elevated, and high-density lipoprotein cholesterol (HDL) and bile acid levels were decreased in these patients. Both composite models were highly accurate for detecting patients with TCFA (area under curve [AUC]: 0.883 in model-A and 0.875 in model-B, both p < 0.001). In addition, creatinine, hsCRP, fibrinogen, tumor necrosis factor-a, IL-6, homocysteine, amyloid A, HDL, prothrombin, and bile acid were useful for detecting patients with "high-risk plaques". Two composite models were highly accurate for detection of patients with "high-risk plaques" (AUC: 0.925 in model-A and 0.947 in model-B, both p <0.001). Biomarkers useful for detection of patients with high-risk coronary plaques defined according to intravascular imaging have been identified. These biomarkers may be useful to risk stratify patients and to develop targeted therapy. Show less
Background and aims: Accurate classification of plaque composition is essential for treatment planning. Intravascular ultrasound (IVUS) has limited efficacy in assessing tissue types, while near... Show moreBackground and aims: Accurate classification of plaque composition is essential for treatment planning. Intravascular ultrasound (IVUS) has limited efficacy in assessing tissue types, while near-infrared spectroscopy (NIRS) provides complementary information to IVUS but lacks depth information. The aim of this study is to train and assess the efficacy of a machine learning classifier for plaque component classification that relies on IVUS echogenicity and NIRS-signal, using histology as reference standard. Methods: Matched NIRS-IVUS and histology images from 15 cadaveric human coronary arteries were analyzed (10 vessels were used for training and 5 for testing). Fibrous/pathological intimal thickening (F-PIT), early necrotic core (ENC), late necrotic core (LNC), and calcific tissue regions-of-interest were detected on histology and superimposed onto IVUS frames. The pixel intensities of these tissue types from the training set were used to train a J48 classifier for plaque characterization (ECHO-classification). To aid differentiation of F-PIT from necrotic cores, the NIRS-signal was used to classify non-calcific pixels outside yellow-spot regions as F-PIT (ECHO-NIRS classification). The performance of ECHO and ECHO-NIRS classifications were validated against histology. Results: 262 matched frames were included in the analysis (162 constituted the training set and 100 the test set). The pixel intensities of F-PIT and ENC were similar and thus these two tissues could not be differentiated by echogenicity. With ENC and LNC as a single class, ECHO-classification showed good agreement with histology for detecting calcific and F-PIT tissues but had poor efficacy for necrotic cores (recall 0.59 and precision 0.29). Similar results were found when F-PIT and ENC were treated as a single class (recall and precision for LNC 0.78 and 0.33, respectively). ECHO-NIRS classification improved necrotic core and LNC detection, resulting in an increase of the overall accuracy of both models, from 81.4% to 91.8%, and from 87.9% to 94.7%, respectively. Comparable performance of the two models was seen in the test set where the overall accuracy of ECHO-NIRS classification was 95.0% and 95.5%, respectively. Conclusions: The combination of echogenicity with NIRS-signal appears capable of overcoming limitations of echogenicity, enabling more accurate characterization of plaque components. Show less
Aims: The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real... Show moreAims: The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time. Methods and results: IVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard. The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was <0.23mm2 (standard deviation <0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was <0.19 mm (standard deviation<0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754-1.061) with similar results in frames portraying calcific plaques or side branches. Conclusions: The developed DL-methodology appears accurate and capable of segmenting high-resolution realworld IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research. Show less
Ueki, Y.; Yamaji, K.; Losdat, S.; Karagiannis, A.; Taniwaki, M.; Roffi, M.; ... ; Raber, L. 2021
We aimed to evaluate the diagnostic agreement between radiofrequency (RF) intravascular ultrasound (IVUS) and optical coherence tomography (OCT) for thin-cap fibroatheroma (TCFA) in non-infarct... Show moreWe aimed to evaluate the diagnostic agreement between radiofrequency (RF) intravascular ultrasound (IVUS) and optical coherence tomography (OCT) for thin-cap fibroatheroma (TCFA) in non-infarct-related coronary arteries (non-IRA) in patients with ST-segment elevation myocardial infarction (STEMI). In the Integrated Biomarker Imaging Study (IBIS-4), 103 STEMI patients underwent OCT and RF-IVUS imaging of non-IRA after successful primary percutaneous coronary intervention and at 13-month follow-up. A coronary lesion was defined as a segment with >= 3 consecutive frames (approximate to 1.2 mm) with plaque burden >= 40% as assessed by grayscale IVUS. RF-IVUS-derived TCFA was defined as a lesion with > 10% confluent necrotic core abutting to the lumen in > 10% of the circumference. OCT-TCFA was defined by a minimum cap thickness < 65 mu m. The two modalities were matched based on anatomical landmarks using a dedicated matching software. Using grayscale IVUS, we identified 276 lesions at baseline (N = 146) and follow-up (N = 130). Using RF-IVUS, 208 lesions (75.4%) were classified as TCFA. Among them, OCT identified 14 (6.7%) TCFA, 60 (28.8%) thick-cap fibroatheroma (ThCFA), and 134 (64.4%) non-fibroatheroma. All OCT-TCFA (n = 14) were confirmed as RF-TCFA. The concordance rate between RF-IVUS and OCT for TCFA diagnosis was 29.7%. The reasons for discordance were: OCT-ThCFA (25.8%); OCT-fibrous plaque (34.0%); attenuation due to calcium (23.2%); attenuation due to macrophage (10.3%); no significant attenuation (6.7%). There was a notable discordance in the diagnostic assessment of TCFA between RF-IVUS and OCT. The majority of RF-derived TCFA were not categorized as fibroatheroma using OCT, while all OCT-TCFA were classified as TCFA by RF-IVUS. ClinicalTrials.gov Identifier NCT00962416. Show less
Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to... Show moreCoronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intravascular ultrasound (IVUS) analysis is performed in non-gated images as existing methods to acquire gated or to retrospectively gate IVUS images have failed to dominate in research. We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic (ECG)-estimations as reference standard. Near-infrared spectroscopy-IVUS (NIRS-IVUS) data were prospectively acquired from 20 coronary arteries and co-registered with the concurrent ECG-signal to identify end-diastolic frames. A DL-methodology which takes advantage of changes in intensity of corresponding pixels in consecutive NIRS-IVUS frames and consists of a network model designed in a bidirectional gated-recurrent-unit (Bi-GRU) structure was trained to detect end-diastolic frames. The efficacy of the DL-methodology in identifying end-diastolic frames was compared with two expert analysts and a conventional image-based (CIB)-methodology that relies on detecting vessel movement to estimate phases of the cardiac cycle. A window of +/- 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames. The mean difference between DL and ECG estimations was 3 +/- 112 ms while the mean differences between the 1st-analyst and ECG, 2nd-analyst and ECG and CIB-methodology and ECG were 86 +/- 192 ms, 78 +/- 183 ms and 59 +/- 207 ms, respectively. The DL-methodology was able to accurately detect 80.4%, while the two analysts and the CIB-methodology detected 39.0%, 43.4% and 42.8% of end-diastolic frames, respectively (P < 0.05). The DL-methodology can identify NIRS-IVUS end-diastolic frames accurately and should be preferred over expert analysts and CIB-methodologies, which have limited efficacy. Show less
Introduction Coronary angiography (CAG) is the standard modality for assessment of coronary stenoses and intraprocedural guidance of percutaneous coronary interventions (PCI). However, the... Show moreIntroduction Coronary angiography (CAG) is the standard modality for assessment of coronary stenoses and intraprocedural guidance of percutaneous coronary interventions (PCI). However, the limitations of CAG are well recognized. Intracoronary imaging (ICI) can potentially overcome these limitations. Intravascular ultrasound (IVUS) and optical coherence tomography (OCT) are the main ICI techniques utilized in clinical practice. Aim This narrative literature review addresses the current clinical applications of OCT in relation to IVUS and CAG in patients with coronary artery disease (CAD). Items reviewed are: technical implications of OCT and IVUS, lesion characterization and decision-making, stent optimization criteria, post-stenting results, safety in terms of procedural complications, clinical outcomes, and indications. Main Findings OCT is able to reveal more detail than IVUS due to its higher resolution. However, this higher resolution comes at the cost of a lower penetration depth. Pre-stenting OCT results in procedural change in more than 50% of the cases in terms of stent length and diameter. Post-stenting OCT resulting in stent optimization is reported in at least 27% of the cases. Malapposition and under-expansion are treated with post-dilatations, while edge dissections are treated with additional stent placement. Stent expansion, stent apposition, distal stent edge dissections, and reference lumen areas seem to be the most important stent optimization criteria for both decision-making and for reducing the risk of adverse events during follow-up. Both OCT and IVUS are superior in terms of post-stenting results compared with CAG alone. However, there is no consensus about whether OCT guidance results in better stent expansion than IVUS guidance. OCT, IVUS, and CAG are safe procedures with few reported procedural complications. In general, OCT guidance seems to contribute to favorable clinical outcomes compared with CAG guidance only. However, OCT guidance results in similar clinical outcomes as with IVUS guidance. OCT could be considered for lumen assessment and stent-related morphology in more complex cases in which CAG interpretation remains uncertain. Since OCT and IVUS have distinct characteristics, these techniques are complementary and should be considered carefully for each patient case based on the benefits and limitations of both techniques. Show less
In contemporary clinical practice, multimodality invasive assessment of coronary lesions offers an invaluable insight on clinical decision-making and PCI-guidance. Intracoronary imaging and... Show moreIn contemporary clinical practice, multimodality invasive assessment of coronary lesions offers an invaluable insight on clinical decision-making and PCI-guidance. Intracoronary imaging and functional assessment are two different diagnostic entities that offer complementary, rather than competitive, information. The role of this integrative understanding during a heart catheterization procedure is of outmost importance to achieve optimal procedural results, particularly regarding complicated coronary anatomies. Furthermore, this holistic approach can potentially lead to outcome improvement in patients with CAD treated percutaneously. Show less
Park, H.B.; Lee, B.K.; Shin, S.; Heo, R.; Arsanjani, R.; Kitslaar, P.H.; ... ; Chung, N. 2015
This thesis proposes several new algorithms including X-ray angiographic image enhancement, three-dimensional (3D) angiographic reconstruction, angiographic overlap prediction, and the co... Show moreThis thesis proposes several new algorithms including X-ray angiographic image enhancement, three-dimensional (3D) angiographic reconstruction, angiographic overlap prediction, and the co-registration of X-ray angiography with intracoronary imaging devices, such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). The algorithms were integrated into prototype software packages that were validated at a number of clinical centers. The feasibility of using such software packages in typical clinical population was verified, while the advantages and accuracy of the proposed algorithms were demonstrated by phantoms and in-vivo clinical studies. In addition, based on the proposed approaches and the conducted studies, this thesis reports a number of findings including the impact of acquisition angle difference on 3D quantitative coronary angiography (QCA), the clinical characteristics of bifurcation optimal viewing angles and bifurcation angles, and the discrepancy of lumen dimensions as assessed by 3D QCA and by IVUS or OCT. Show less
