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<p>Radiation exposure is inherent to coronary angiography. Lowering its frame rate significantly reduces radiation exposure but degrades temporal resolution. We introduce Angio-FILM, a novel generative AI model for video frame interpolation dedicated to coronary angiography. Angio-FILM uses a latent flow matching model to synthesize high-temporal-resolution (15 FPS) videos from low-frame-rate (7.5 FPS) inputs. The model was trained on 357,933 videos and validated on both internal and external open-source datasets. While quantitative metrics showed mixed results, qualitative evaluation by human experts showed Angio-FILM outperformed existing state-of-the-art methods. Furthermore, Visual Turing test (30 physicians, 600 videos) showed accuracies of 54% (<i>p</i> = 0.107) in a single-video binary classification task and 49% (<i>p</i> = 0.749) in a two-alternative forced choice task, indicating that the generated videos were hardly distinguishable from real angiograms. Quantitative coronary analysis confirmed high anatomical fidelity, showing minimal deviation in minimal lumen diameter measurements between the original and interpolated frames (MAE, 0.180 mm). Angio-FILM represents a significant step forward in translating a cutting-edge generative AI technology into a clinically helpful tool.</p>

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Video frame interpolation for coronary angiography using latent flow matching

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月23日

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  • Hwi Kwon,
  • Seyeong Park,
  • Do-Hyun Kim,
  • Sang-Kyun Ko,
  • Sun-Hwa Kim,
  • In Tae Moon,
  • Ki-Hyun Jeon,
  • Si-Hyuck Kang,
  • Chang-Hwan Yoon,
  • Tae-Jin Youn,
  • In-Ho Chae

摘要

Radiation exposure is inherent to coronary angiography. Lowering its frame rate significantly reduces radiation exposure but degrades temporal resolution. We introduce Angio-FILM, a novel generative AI model for video frame interpolation dedicated to coronary angiography. Angio-FILM uses a latent flow matching model to synthesize high-temporal-resolution (15 FPS) videos from low-frame-rate (7.5 FPS) inputs. The model was trained on 357,933 videos and validated on both internal and external open-source datasets. While quantitative metrics showed mixed results, qualitative evaluation by human experts showed Angio-FILM outperformed existing state-of-the-art methods. Furthermore, Visual Turing test (30 physicians, 600 videos) showed accuracies of 54% (p = 0.107) in a single-video binary classification task and 49% (p = 0.749) in a two-alternative forced choice task, indicating that the generated videos were hardly distinguishable from real angiograms. Quantitative coronary analysis confirmed high anatomical fidelity, showing minimal deviation in minimal lumen diameter measurements between the original and interpolated frames (MAE, 0.180 mm). Angio-FILM represents a significant step forward in translating a cutting-edge generative AI technology into a clinically helpful tool.