An All-in-One Tool for 2D Atherosclerotic Disease Assessment and 3D Coronary Artery Reconstruction

Diagnosis of coronary artery disease is mainly based on invasive imaging modalities such as X-ray angiography, intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Computed tomography coronary angiography (CTCA) is also used as a non-invasive imaging alternative. In this work, we present a novel and unique tool for 3D coronary artery reconstruction and plaque characterization using the abovementioned imaging modalities or their combination. In particular, image processing and deep learning algorithms were employed and validated for the lumen and adventitia borders and plaque characterization at the IVUS and OCT frames. Strut detection is also achieved from the OCT images. Quantitative analysis of the X-ray angiography enables the 3D reconstruction of the lumen geometry and arterial centerline extraction. The fusion of the generated centerline with the results of the OCT or IVUS analysis enables hybrid coronary artery 3D reconstruction, including the plaques and the stent geometry. CTCA image processing using a 3D level set approach allows the reconstruction of the coronary arterial tree, the calcified and non-calcified plaques as well as the detection of the stent location. The modules of the tool were evaluated for efficiency with over 90% agreement of the 3D models with the manual annotations, while a usability assessment using external evaluators demonstrated high usability resulting in a mean System Usability Scale (SUS) score equal to 0.89, classifying the tool as excellent.

Automated summary

Diagnosis of coronary artery disease relies mainly on invasive imaging modalities such as X-ray angiography, intravascular ultrasound (IVUS), and optical coherence tomography (OCT), with computed tomography coronary angiography (CTCA) as a non-invasive alternative. This study presents a novel tool for reconstructing 3D coronary arteries and characterizing plaques using these imaging modalities. Image processing and deep learning algorithms were used to determine lumen and adventitia borders, plaque properties, and strut detection. The tool combines X-ray angiography with IVUS/OCT analysis for hybrid 3D reconstruction, and uses a 3D level set approach for CTCA image processing. The tool was evaluated for efficiency and usability, achieving high agreement with manual annotations and a high System Usability Scale (SUS) score of 0.89, indicating excellent usability.