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Video frame interpolation for coronary angiography using latent flow matching.

Hwi Kwon¹, Seyeong Park¹, Do-Hyun Kim^1,2

¹Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam-si, Korea.

NPJ Digital Medicine

|June 23, 2026

Summary

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Angio-FILM, a generative AI model, synthesizes high-resolution coronary angiography videos from low-frame-rate inputs. This AI tool enhances temporal resolution while maintaining anatomical accuracy, aiding clinical applications.

Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Medicine

Background:

Coronary angiography involves inherent radiation exposure.
Reducing frame rates lowers radiation but impairs temporal resolution.

Purpose of the Study:

Introduce Angio-FILM, a generative AI model for coronary angiography video frame interpolation.
Synthesize high-temporal-resolution videos from low-frame-rate inputs to reduce radiation exposure.

Main Methods:

Developed Angio-FILM using a latent flow matching model.
Trained the model on over 350,000 coronary angiography videos.
Validated using internal, external, and open-source datasets.

Main Results:

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Qualitative expert evaluation showed Angio-FILM outperformed existing methods.

Visual Turing tests indicated generated videos were indistinguishable from real angiograms.

Quantitative analysis confirmed high anatomical fidelity with minimal deviation in measurements.

Conclusions:

Angio-FILM effectively interpolates frames to create high-temporal-resolution coronary angiography videos.
The AI model shows potential for reducing radiation exposure without significant loss of diagnostic information.
Angio-FILM represents a significant advancement in applying generative AI to clinical practice.