@article{volleberg_deep_2025,

title = {Deep Learning-Derived Plaque Burden for Intracoronary Optical Coherence Tomography: An Intravascular Ultrasound-Based Validation Study},

author = {R. H. J. A. Volleberg and D. Shin and S. Saitta and R. A. Shlofmitz and E. Shlofmitz and A. Jeremias and R. G. A. Waerden and J. Thannhauser and N. Royen and Z. A. Ali},

doi = {10.1016/j.jcin.2025.07.021},

issn = {1876-7605},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

journal = {JACC. Cardiovascular interventions},

volume = {18},

number = {19},

pages = {2432–2434},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg_artificial_2025,

title = {Artificial intelligence-based identification of thin-cap fibroatheromas and clinical outcomes: the PECTUS-AI study},

author = {R. H. J. A. Volleberg and T. J. Luttikholt and R. G. A. Waerden and P. Cancian and J. L. Zande and X. Gu and J. Q. Mol and T. Roleder and M. Prokop and C. I. Sánchez and B. Ginneken and I. Išgum and S. Saitta and J. Thannhauser and N. Royen},

url = {https://doi.org/10.1093/eurheartj/ehaf595},

doi = {10.1093/eurheartj/ehaf595},

issn = {0195-668X},

year  = {2025},

date = {2025-09-01},

urldate = {2025-09-01},

journal = {European Heart Journal},

pages = {ehaf595},

abstract = {Coronary thin-cap fibroatheromas (TCFA) are associated with adverse outcome, but identification of TCFA requires expertise and is highly time-demanding. This study evaluated the utility of artificial intelligence (AI) for TCFA identification in relation to clinical outcome.The PECTUS-AI study is a secondary analysis from the prospective observational PECTUS-obs study, in which 438 patients with myocardial infarction underwent optical coherence tomography (OCT) of all fractional flow reserve-negative non-culprit lesions (i.e. target lesions). OCT images were analyzed for the presence of TCFA by an independent core laboratory (CL-TCFA) and OCT-AID, a recently developed and validated AI segmentation algorithm (AI-TCFA). The primary outcome was defined as the composite of death from any cause, non-fatal myocardial infarction or unplanned revascularisation at 2 years (±30 days), excluding procedural and stent-related events.Among 414 patients, AI-TCFA and CL-TCFA were identified in 143 (34.5%) and 124 (30.0%) patients, respectively. AI-TCFA within the target lesion was significantly associated with the primary outcome [hazard ratio (HR) 1.99, 95% confidence interval (CI) 1.02–3.90},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{luttikholt_detection_2025,

title = {Detection of large areas of thin-cap fibroatheroma in a recurrent STEMI patient using a novel artificial intelligence algorithm: moving from 2D to 3D},

author = {T. J. Luttikholt and J. Thannhauser and N. Royen},

url = {https://doi.org/10.1093/eurheartj/ehaf189},

doi = {10.1093/eurheartj/ehaf189},

issn = {0195-668X},

year  = {2025},

date = {2025-07-01},

urldate = {2025-07-01},

journal = {European Heart Journal},

volume = {46},

number = {27},

pages = {2712},

abstract = {Optical coherence tomography (OCT) is a valuable imaging tool in percutaneous coronary intervention (PCI), recommended for stent guidance and evaluation.1 Moreover, OCT-imaging can visualize high-risk plaques, such as thin-cap fibroatheroma (TCFA), which have prognostic value.2,3 However, manual OCT-interpretation is time-consuming, subject to interobserver variability4 and, most importantly, assesses TCFA in a two-dimensional, single-frame manner. It is likely that prognosis depends on TCFA-extent rather than presence alone, similar to lipid burden in near-infrared spectroscopy.Our group developed OCT-AID, an artificial intelligence (AI)-algorithm for OCT-segmentation (Supplementary data online, Video S1) and plaque characterization.5 OCT-AID enables automated quantification of TCFA-area, as demonstrated in the present case.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg_comprehensive_2025,

title = {Comprehensive full-vessel segmentation and volumetric plaque quantification for intracoronary optical coherence tomography using deep learning},

author = {R. H. J. A. Volleberg and R. G. A. Waerden and T. J. Luttikholt and J. L. Zande and P. Cancian and X. Gu and J. Q. Mol and S. Quax and M. Prokop and C. I. Sánchez and B. Ginneken and I. Išgum and J. Thannhauser and S. Saitta and K. Nishimiya and T. Roleder and N. Royen},

url = {https://doi.org/10.1093/ehjdh/ztaf021},

doi = {10.1093/ehjdh/ztaf021},

issn = {2634-3916},

year  = {2025},

date = {2025-05-01},

urldate = {2025-05-01},

journal = {European Heart Journal - Digital Health},

volume = {6},

number = {3},

pages = {404–416},

abstract = {Intracoronary optical coherence tomography (OCT) provides detailed information on coronary lesions, but interpretation of OCT images is time-consuming and subject to interobserver variability. The aim of this study was to develop and validate a deep learning-based multiclass semantic segmentation algorithm for OCT (OCT-AID).A reference standard was obtained through manual multiclass annotation (guidewire artefact, lumen, side branch, intima, media, lipid plaque, calcified plaque, thrombus, plaque rupture, and background) of OCT images from a representative subset of pullbacks from the PECTUS-obs study. Pullbacks were randomly divided into a training and internal test set. An additional independent dataset was used for external testing. In total, 2808 frames were used for training and 218 for internal testing. The external test set comprised 392 frames. On the internal test set, the mean Dice score across nine classes was 0.659 overall and 0.757 on the true-positive frames, ranging from 0.281 to 0.989 per class. Substantial to almost perfect agreement was achieved for frame-wise identification of both lipid (κ=0.817, 95% CI 0.743–0.891) and calcified plaques (κ=0.795, 95% CI 0.703–0.887). For plaque quantification (e.g. lipid arc, calcium thickness), intraclass correlations of 0.664–0.884 were achieved. In the external test set, κ-values for lipid and calcified plaques were 0.720 (95% CI 0.640–0.800) and 0.851 (95% CI 0.794–0.908), respectively.The developed multiclass semantic segmentation method for intracoronary OCT images demonstrated promising capabilities for various classes, while having included difficult frames, such as those containing artefacts or destabilized plaques. This algorithm is an important step towards comprehensive and standardized OCT image interpretation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg_optical_2025,

title = {Optical Coherence Tomography in Motion: Potential Cause for Artifacts},

author = {R. Volleberg and P. Cancian and N. Royen},

url = {https://www.sciencedirect.com/science/article/pii/S1936879824016960},

doi = {10.1016/j.jcin.2024.11.004},

issn = {1936-8798},

year  = {2025},

date = {2025-03-01},

urldate = {2025-03-01},

journal = {JACC: Cardiovascular Interventions},

volume = {18},

number = {5},

pages = {680–681},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{cancian_attenuation_2025,

title = {Attenuation artifact detection and severity classification in intracoronary OCT using mixed image representations},

author = {P. Cancian and S. Saitta and X. Gu and R. L. M. Herten and T. J. Luttikholt and J. Thannhauser and R. H. J. A. Volleberg and R. G. A. Waerden and J. L. Zande and C. I. Sánchez and B. Ginneken and N. Royen and I. Išgum},

url = {http://arxiv.org/abs/2503.05322},

doi = {10.48550/arXiv.2503.05322},

year  = {2025},

date = {2025-03-01},

urldate = {2025-03-01},

publisher = {arXiv},

abstract = {In intracoronary optical coherence tomography (OCT), blood residues and gas bubbles cause attenuation artifacts that can obscure critical vessel structures. The presence and severity of these artifacts may warrant re-acquisition, prolonging procedure time and increasing use of contrast agent. Accurate detection of these artifacts can guide targeted re-acquisition, reducing the amount of repeated scans needed to achieve diagnostically viable images. However, the highly heterogeneous appearance of these artifacts poses a challenge for the automated detection of the affected image regions. To enable automatic detection of the attenuation artifacts caused by blood residues and gas bubbles based on their severity, we propose a convolutional neural network that performs classification of the attenuation lines (A-lines) into three classes: no artifact, mild artifact and severe artifact. Our model extracts and merges features from OCT images in both Cartesian and polar coordinates, where each column of the image represents an A-line. Our method detects the presence of attenuation artifacts in OCT frames reaching F-scores of 0.77 and 0.94 for mild and severe artifacts, respectively. The inference time over a full OCT scan is approximately 6 seconds. Our experiments show that analysis of images represented in both Cartesian and polar coordinate systems outperforms the analysis in polar coordinates only, suggesting that these representations contain complementary features. This work lays the foundation for automated artifact assessment and image acquisition guidance in intracoronary OCT imaging.},

note = {arXiv:2503.05322 [cs]},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{van_der_waerden_artificial_2025,

title = {Artificial intelligence for the analysis of intracoronary optical coherence tomography images: a systematic review},

author = {R. G. A. Waerden and R. H. J. A. Volleberg and T. J. Luttikholt and P. Cancian and J. L. Zande and G. W. Stone and N. R. Holm and E. Kedhi and J. Escaned and D. Pellegrini and G. Guagliumi and S. R. Mehta and N. Pinilla-Echeverri and R. Moreno and L. Räber and T. Roleder and B. Ginneken and C. I. Sánchez and I. Išgum and N. Royen and J. Thannhauser},

doi = {10.1093/ehjdh/ztaf005},

issn = {2634-3916},

year  = {2025},

date = {2025-03-01},

urldate = {2025-03-01},

journal = {European Heart Journal. Digital Health},

volume = {6},

number = {2},

pages = {270–284},

abstract = {Intracoronary optical coherence tomography (OCT) is a valuable tool for, among others, periprocedural guidance of percutaneous coronary revascularization and the assessment of stent failure. However, manual OCT image interpretation is challenging and time-consuming, which limits widespread clinical adoption. Automated analysis of OCT frames using artificial intelligence (AI) offers a potential solution. For example, AI can be employed for automated OCT image interpretation, plaque quantification, and clinical event prediction. Many AI models for these purposes have been proposed in recent years. However, these models have not been systematically evaluated in terms of model characteristics, performances, and bias. We performed a systematic review of AI models developed for OCT analysis to evaluate the trends and performances, including a systematic evaluation of potential sources of bias in model development and evaluation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg2025tct,

title = {TCT-1251 Artificial intelligence-based volumetric evaluation of the fibrous cap: the maximum thin-cap index within 4 mm},

author = {R. Volleberg and T. Luttikholt and J. Zande and R. Waerden and L. Heil and P. Cancian and X. Gu and S. Saitta and C. Sánchez and B. Ginneken },

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Journal of the American College of Cardiology},

volume = {86},

number = {17_Supplement},

pages = {B537–B538},

publisher = {American College of Cardiology Foundation Washington DC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{van2025tct,

title = {TCT-1259 Artificial Intelligence-Based Volumetric Analysis of Coronary Calcifications and the Association with Plaque Vulnerability},

author = {R. Waerden and J. Zande and P. Cancian and T. Luttikholt and L. Heil and X. Gu and J. Thannhauser and S. Saitta and C. Sánchez and B. Ginneken },

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Journal of the American College of Cardiology},

volume = {86},

number = {17_Supplement},

pages = {B541–B541},

publisher = {American College of Cardiology Foundation Washington DC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg2025tctb,

title = {TCT-1260 Deep Learning-Derived Plaque Burden for Intracoronary Optical Coherence Tomography: an Intravascular Ultrasound-Based Validation Study},

author = {R. Volleberg and D. Shin and R. Waerden and S. Saitta and J. Thannhauser and J. Zande and T. Luttikholt and P. Cancian and X. Gu and L. Heil },

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Journal of the American College of Cardiology},

volume = {86},

number = {17_Supplement},

pages = {B541–B542},

publisher = {American College of Cardiology Foundation Washington DC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{luttikholt2025tct,

title = {TCT-1263 Spatial Relationship Between Artificial Intelligence-Identified Thinnest Fibrous Cap Region and the Minimum Lumen Area},

author = {T. Luttikholt and E. Volleberg and R. Waerden and J. Zande and L. Heil and P. Cancian and X. Gu and S. Saitta and C. Sánchez and B. Ginneken },

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Journal of the American College of Cardiology},

volume = {86},

number = {17_Supplement},

pages = {B543–B543},

publisher = {American College of Cardiology Foundation Washington DC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{van2025tctb,

title = {TCT-1266 High-Risk Plaque Features in Non-Culprit Vessels of ACS Patients: Insights from AI-Driven OCT Analysis},

author = {R. Waerden and R. Volleberg and T. Luttikholt and L. Heil and P. Cancian and X. Gu and S. Saitta and C. Sánchez and B Ginneken and I. Išgum },

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Journal of the American College of Cardiology},

volume = {86},

number = {17_Supplement},

pages = {B544–B544},

publisher = {American College of Cardiology Foundation Washington DC},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg2025impact,

title = {Impact of clinical risk characteristics on the prognostic value of high-risk plaques},

author = {R. H. J. A. Volleberg and A. Rroku and J. Q. Mol and R. S. Hermanides and M. van Leeuwen and B. Berta and M. Meuwissen and F. Alfonso and W. Wojakowski and A. Belkacemi },

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {EuroIntervention: journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology},

volume = {21},

number = {19},

pages = {e1147–e1158},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg2025ffr,

title = {FFR-negative nonculprit high-risk plaques and clinical outcomes in high-risk populations: an individual patient-data pooled analysis from COMBINE (OCT-FFR) and PECTUS-obs},

author = {R. H. J. A. Volleberg and A. Rroku and J. Q. Mol and R. S. Hermanides and M. van Leeuwen and B. Berta and M. Meuwissen and F. Alfonso and W. Wojakowski and A. Belkacemi },

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Circulation: Cardiovascular Interventions},

volume = {18},

number = {2},

pages = {e014667},

publisher = {Lippincott Williams & Wilkins Hagerstown, MD},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{vazquez2024clinical,

title = {Clinical quantitative coronary artery stenosis and coronary atherosclerosis imaging: a Consensus Statement from the Quantitative Cardiovascular Imaging Study Group},

author = {A. J. V. Mézquita and F. Biavati and V. Falk and H. Alkadhi and R. Hajhosseiny and P. Maurovich-Horvat and R. Manka and S. Kozerke and M. Stuber and T. Derlin },

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Quantification of biophysical parameters in medical imaging},

pages = {569–600},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg2024dissection,

title = {Dissection-like appearance of focal catheter-induced vasospasm in intracoronary optical coherence tomography},

author = {R. Volleberg and P. Damman and N. Royen},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {European Heart Journal},

volume = {45},

number = {30},

pages = {2793–2793},

publisher = {Oxford University Press UK},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{los2024invasive,

title = {Invasive coronary imaging of inflammation to further characterize high-risk lesions: what options do we have?},

author = {J. Los and F. B. Mensink and N. Mohammadnia and T. S. J. Opstal and P. Damman and R. H. J. A. Volleberg and D. A. M. Peeters and N. Royen and H. M. Garcia and J. H. Cornel },

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Frontiers in Cardiovascular Medicine},

volume = {11},

pages = {1352025},

publisher = {Frontiers Media SA},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{follmer2024roadmap,

title = {Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries},

author = {B. Föllmer and M. C. Williams and D. Dey and A. Arbab-Zadeh and P. Maurovich-Horvat and R. H. J. A. Volleberg and D. Rueckert and J. A. Schnabel and D. E. Newby and M. R. Dweck },

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Quantification of Biophysical Parameters in Medical Imaging},

pages = {547–568},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg2024sex,

title = {Sex differences in plaque characteristics of fractional flow reserve-negative non-culprit lesions after myocardial infarction},

author = {R. H. J. A. Volleberg and J. Q. Mol and A. Belkacemi and R. S. Hermanides and M. Meuwissen and A. V. Protopopov and P. Laanmets and O. V. Krestyaninov and R. Dennert and R. M. Oemrawsingh },

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Atherosclerosis},

volume = {397},

pages = {118568},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mol2023fractional,

title = {Fractional flow reserve–negative high-risk plaques and clinical outcomes after myocardial infarction},

author = {J. Q. Mol and R. H. J. A. Volleberg and A. Belkacemi and R. S. Hermanides and M. Meuwissen and A. V. Protopopov and P. Laanmets and O. V. Krestyaninov and R. Dennert and R. M. Oemrawsingh },

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {JAMA cardiology},

volume = {8},

number = {11},

pages = {1013–1021},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg2023optical,

title = {Optical coherence tomography and coronary revascularization: from indication to procedural optimization},

author = {R. Volleberg and J. Q. Mol and D. Heijden and M. Meuwissen and M. Leeuwen and J. Escaned and N. Holm and T. Adriaenssens and R. J. Geuns and S. Tu },

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Trends in Cardiovascular Medicine},

volume = {33},

number = {2},

pages = {92–106},

publisher = {Elsevier},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{volleberg2022hangover,

title = {Hangover after side branch stenting: The discomfort comes afterwards},

author = {R. Volleberg and S. Oord and R. J. van Geuns},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Interventional Cardiology: Reviews, Research, Resources},

volume = {17},

pages = {e08},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{mol2021identification,

title = {Identification of anatomic risk factors for acute coronary events by optical coherence tomography in patients with myocardial infarction and residual nonflow limiting lesions: rationale and design of the PECTUS-obs study},

author = {J. Q. Mol and A. Belkacemi and R. H. J. A. Volleberg and M. Meuwissen and A. V. Protopopov and P. Laanmets and O. V. Krestyaninov and R. Dennert and R. M. Oemrawsingh and J. P. Kuijk},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {BMJ open},

volume = {11},

number = {7},

pages = {e048994},

publisher = {British Medical Journal Publishing Group},

keywords = {},

pubstate = {published},

tppubtype = {article}

}