A prospective multicenter validation study for a novel angiography-derived physiological assessment software: Rationale and design of the radiographic imaging validation and evaluation for Angio-iFR (ReVEAL iFR) study

Consensus document on the clinical application of invasive functional coronary angiography from the Japanese Association of Cardiovascular Intervention and Therapeutics

Invasive functional coronary angiography (FCA), an angiography-derived physiological index of the functional significance of coronary obstruction, is a novel physiological assessment tool for coronary obstruction that does not require the utilization of a pressure wire. This technology enables operators to rapidly evaluate the functional relevance of coronary stenoses during and even after angiography while reducing the burden of cost and complication risks related to the pressure wire. FCA can be used for treatment decision-making for revascularization, strategy planning for percutaneous coronary intervention, and procedure optimization. Currently, various software-computing FCAs are available worldwide, with unique features in their computation algorithms and functions. With the emerging application of this novel technology in various clinical scenarios, the Japanese Association of Cardiovascular Intervention and Therapeutics task force was created to outline expert consensus on the clinical use of FCA. This consensus document advocates optimal clinical applications of FCA according to currently available evidence while summarizing the concept, history, limitations, and future perspectives of FCA along with globally available software.

Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography

BACKGROUND

Invasive coronary angiography (ICA) is a primary imaging modality that visualizes the lumen area of coronary arteries for diagnosis and interventional guidance. In the current practice of quantitative coronary analysis (QCA), semi-automatic segmentation tools require labor-intensive and time-consuming manual correction, limiting their application in the catheterization room.

PURPOSE

This study aims to propose rank-based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep-learning segmentation of ICA.

METHODS

Two selective ensemble methods proposed in this work integrated the weighted ensemble approach with per-image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo-ground truth generated from a meta-learner (ESEN). Five-fold cross-validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients.

RESULTS

The selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one-sixth of a second.

CONCLUSION

Proposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real-time QCA-based diagnostic methods in routine clinical settings.

Angiography-based estimation of coronary physiology: A frame is worth a thousand words

Cumulative evidence has shown that coronary revascularization should be guided by functional significance of coronary lesions. Fractional flow reserve (FFR) is the gold standard for assessment of hemodynamic significance of coronary stenosis and FFR-guided percutaneous coronary intervention has improved clinical outcomes in patients with coronary artery disease. However, limitations of FFR such as increased operational time and cost, requirement of pressure wire and adenosine and technical difficulties have led to significant underutilization of the method in clinical practice. In the last few years, several methods of FFR estimation based on coronary angiography images have emerged to overcome invasive FFR limitations. The common elements of the novel indices include a 3D anatomical reconstruction of coronary vessels by angiographic projections and various approaches to fluid dynamics computation. Angiography-derived FFR methods have shown high diagnostic accuracy compared to invasive FFR. Although there are promising results regarding their prognostic role, large randomized trials evaluating clinical outcomes are lacking. The aim of this review is to present currently available angiography-derived FFR indices and highlight their differences, advantages, disadvantages and potential clinical implications.