End‐diastolic segmentation of intravascular ultrasound images enables more reproducible volumetric analysis of atheroma burden

Erdoğan, Emrah; Huang, Xingru; Jain, Ajay; Ramasamy, Anantharaman; Bajaj, Retesh; Torii, Ryo; Moon, James; Deaner, Andrew; Costa, Christos; García‐García, Héctor M.; Tufaro, Vincenzo; Serruys, Patrick W.; Pugliese, Francesca; Mathur, Anthony; Dijkstra, Jouke; Baumbach, Andreas; Zhang, Qianni; Bourantas, Christos V.

doi:10.1002/ccd.29917

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…Despite this limitation, the proposed ED frame detection constitutes a key advance in IVUS image analysis and is expected to positively influence subsequent research. We have previously demonstrated that ED-frame-based volumetric IVUS analysis is more reproducible than conventional IVUS segmentation (44). These findings are important for longitudinal intravascular imaging-based studies assessing the implications of pharmacotherapies on plaque volume, as a more reproducible IVUS analysis is expected to reduce the number of vessels that should be included in these studies to demonstrate statistically significant changes in plaque burden (45,46).…”

Section: Discussionmentioning

confidence: 91%

CARDIAN: a novel computational approach for real-time end-diastolic frame detection in intravascular ultrasound using bidirectional attention networks

Huang,

Bajaj,

Cui

et al. 2023

Front. Cardiovasc. Med.

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IntroductionChanges in coronary artery luminal dimensions during the cardiac cycle can impact the accurate quantification of volumetric analyses in intravascular ultrasound (IVUS) image studies. Accurate ED-frame detection is pivotal for guiding interventional decisions, optimizing therapeutic interventions, and ensuring standardized volumetric analysis in research studies. Images acquired at different phases of the cardiac cycle may also lead to inaccurate quantification of atheroma volume due to the longitudinal motion of the catheter in relation to the vessel. As IVUS images are acquired throughout the cardiac cycle, end-diastolic frames are typically identified retrospectively by human analysts to minimize motion artefacts and enable more accurate and reproducible volumetric analysis.MethodsIn this paper, a novel neural network-based approach for accurate end-diastolic frame detection in IVUS sequences is proposed, trained using electrocardiogram (ECG) signals acquired synchronously during IVUS acquisition. The framework integrates dedicated motion encoders and a bidirectional attention recurrent network (BARNet) with a temporal difference encoder to extract frame-by-frame motion features corresponding to the phases of the cardiac cycle. In addition, a spatiotemporal rotation encoder is included to capture the IVUS catheter's rotational movement with respect to the coronary artery.ResultsWith a prediction tolerance range of 66.7 ms, the proposed approach was able to find 71.9%, 67.8%, and 69.9% of end-diastolic frames in the left anterior descending, left circumflex and right coronary arteries, respectively, when tested against ECG estimations. When the result was compared with two expert analysts’ estimation, the approach achieved a superior performance.DiscussionThese findings indicate that the developed methodology is accurate and fully reproducible and therefore it should be preferred over experts for end-diastolic frame detection in IVUS sequences.

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Section: Discussionmentioning

confidence: 91%

CARDIAN: a novel computational approach for real-time end-diastolic frame detection in intravascular ultrasound using bidirectional attention networks

Huang,

Bajaj,

Cui

et al. 2023

Front. Cardiovasc. Med.

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“…Confusion matrix and classifier performance in region and area-level analysis with 3 tissue types (F-PIT and ENC were combined in a single class) based on ECHO-classification and ECHO-NIRS classification in the test set. of large datasets and quantification of plaque components in a reproducible manner [49,50]. These advances are anticipated to promote the broad use of the combined ECHO-NIRS approach for plaque characterization in serial NIRS-IVUS studies evaluating the efficacy of novel pharmacotherapies on plaque evolution.…”

Section: Tablementioning

confidence: 99%

Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against histology

et al. 2022

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Implications of coronary calcification on the assessment of plaque pathology: a comparison of computed tomography and multimodality intravascular imaging

Yap,

Ramasamy,

Tanboga

et al. 2024

Eur Radiol

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Objectives This study aimed to investigate the impact of calcific (Ca) on the efficacy of coronary computed coronary angiography (CTA) in evaluating plaque burden (PB) and composition with near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) serving as the reference standard. Materials and methods Sixty-four patients (186 vessels) were recruited and underwent CTA and 3-vessel NIRS-IVUS imaging (NCT03556644). Expert analysts matched and annotated NIRS-IVUS and CTA frames, identifying lumen and vessel wall borders. Tissue distribution was estimated using NIRS chemograms and the arc of Ca on IVUS, while in CTA Hounsfield unit cut-offs were utilized to establish plaque composition. Plaque distribution plots were compared at segment-, lesion-, and cross-sectional-levels. Results Segment- and lesion-level analysis showed no effect of Ca on the correlation of NIRS-IVUS and CTA estimations. However, at the cross-sectional level, Ca influenced the agreement between NIRS-IVUS and CTA for the lipid and Ca components (p-heterogeneity < 0.001). Proportional odds model analysis revealed that Ca had an impact on the per cent atheroma volume quantification on CTA compared to NIRS-IVUS at the segment level (p-interaction < 0.001). At lesion level, Ca affected differences between the modalities for maximum PB, remodelling index, and Ca burden (p-interaction < 0.001, 0.029, and 0.002, respectively). Cross-sectional-level modelling demonstrated Ca’s effect on differences between modalities for all studied variables (p-interaction ≤ 0.002). Conclusion Ca burden influences agreement between NIRS-IVUS and CTA at the cross-sectional level and causes discrepancies between the predictions for per cent atheroma volume at the segment level and maximum PB, remodelling index, and Ca burden at lesion-level analysis. Clinical relevance statement Coronary calcification affects the quantification of lumen and plaque dimensions and the characterization of plaque composition coronary CTA. This should be considered in the analysis and interpretation of CTAs performed in patients with extensive Ca burden. Key Points Coronary CT Angiography is limited in assessing coronary plaques by resolution and blooming artefacts. Agreement between dual-source CT angiography and NIRS-IVUS is affected by a Ca burden for the per cent atheroma volume. Advanced CT imaging systems that eliminate blooming artefacts enable more accurate quantification of coronary artery disease and characterisation of plaque morphology.

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End‐diastolic segmentation of intravascular ultrasound images enables more reproducible volumetric analysis of atheroma burden

Cited by 6 publications

References 31 publications

CARDIAN: a novel computational approach for real-time end-diastolic frame detection in intravascular ultrasound using bidirectional attention networks

CARDIAN: a novel computational approach for real-time end-diastolic frame detection in intravascular ultrasound using bidirectional attention networks

Machine learning for atherosclerotic tissue component classification in combined near-infrared spectroscopy intravascular ultrasound imaging: Validation against histology

Implications of coronary calcification on the assessment of plaque pathology: a comparison of computed tomography and multimodality intravascular imaging

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