Development of an approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

Zhao, Chen; Tang, Haipeng; McGonigle, Daniel; He, Zhuo; Zhang, Chaoyang; Wang, Yuping; Deng, Hong‐Wen; Bober, Robert; Zhou, Weihua

doi:10.1117/1.jmi.9.4.044002

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…Several advanced visualization techniques relying on coronary artery centerlines have been shown to facilitate clinical workflows [7][8][9][10][11]. The categories of coronary artery centerline extraction algorithms can be divided into: (1) automatic extraction methods e.g., [12][13][14][15], which find the centerlines without any user-interaction; (2) extraction with minimal user-interaction e.g., [16][17][18], where only one point per vessel is allowed to be used as an input for the algorithm; and (3) interactive extraction e.g., [19][20][21], which includes any other method that requires more than one point per vessel as input [6].…”

Section: Introductionmentioning

confidence: 99%

Automated Coronary Artery Tracking with a Voronoi-Based 3D Centerline Extraction Algorithm

Dalvit Carvalho da Silva,

Soltanzadeh,

Figley

2023

J. Imaging

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Coronary artery disease is one of the leading causes of death worldwide, and medical imaging methods such as coronary artery computed tomography are vitally important in its detection. More recently, various computational approaches have been proposed to automatically extract important artery coronary features (e.g., vessel centerlines, cross-sectional areas along vessel branches, etc.) that may ultimately be able to assist with more accurate and timely diagnoses. The current study therefore validated and benchmarked a recently developed automated 3D centerline extraction method for coronary artery centerline tracking using synthetically segmented coronary artery models based on the widely used Rotterdam Coronary Artery Algorithm Evaluation Framework (RCAAEF) training dataset. Based on standard accuracy metrics and the ground truth centerlines of all 32 coronary vessel branches in the RCAAEF training dataset, this 3D divide and conquer Voronoi diagram method performed exceptionally well, achieving an average overlap accuracy (OV) of 99.97%, overlap until first error (OF) of 100%, overlap of the clinically relevant portion of the vessel (OT) of 99.98%, and an average error distance inside the vessels (AI) of only 0.13 mm. Accuracy was also found to be exceptionally for all four coronary artery sub-types, with average OV values of 99.99% for right coronary arteries, 100% for left anterior descending arteries, 99.96% for left circumflex arteries, and 100% for large side-branch vessels. These results validate that the proposed method can be employed to quickly, accurately, and automatically extract 3D centerlines from segmented coronary arteries, and indicate that it is likely worthy of further exploration given the importance of this topic.

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Section: Introductionmentioning

confidence: 99%

Automated Coronary Artery Tracking with a Voronoi-Based 3D Centerline Extraction Algorithm

Dalvit Carvalho da Silva,

Soltanzadeh,

Figley

2023

J. Imaging

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FeDETR: A Federated Approach for Stenosis Detection in Coronary Angiography

Mineo,

Sorrenti,

Proietto Salanitri

2024

Lecture Notes in Computer Science

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Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories

Lashgari,

Choudhury,

Banerjee

2024

Front. Cardiovasc. Med.

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Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.

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Development of an approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms

Cited by 4 publications

References 42 publications

Automated Coronary Artery Tracking with a Voronoi-Based 3D Centerline Extraction Algorithm

Automated Coronary Artery Tracking with a Voronoi-Based 3D Centerline Extraction Algorithm

FeDETR: A Federated Approach for Stenosis Detection in Coronary Angiography

Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories

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