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Purpose <p>Digital Subtraction Angiography (DSA) is an X-ray-based imaging modality intimately related to minimally invasive procedures in interventional radiology, cardiology, vascular and neurologic surgery. Emulating tomographic methods like 3D vessel reconstruction and flat-panel detector CT perfusion imaging increases its diagnostic utility. This study demonstrates a hardware and software setup expanding DSA capability to functional perfusion imaging by assessing planar cerebral perfusion at runtime.</p> Methods <p>The setup uses an HDMI video splitter and frame-grabber for duplicating the video output of the intervention suite to an arbitrary machine at the time of image acquisition. A selection of methods, including numerical approximation and neural network inference of fitted curve features, is applied to create perfusion parameter maps and compare their respective completion times over a set of angiographic runs.</p> Results <p>We identified two distinct perfusion estimation methods able to yield results within 1–2&#xa0;s, signal deconvolution using single-value decomposition (SVD) and numerical curve feature estimation, as well as a variation on conventional curve fitting that massively shortened calculation times from five minutes to clinically feasible 30&#xa0;s.</p> Conclusion <p>Directly accessing image data outside of the angiography suite enables real-time angiographic cerebral perfusion evaluation in the clinical workflow. This way, on-the-fly analysis of angiograms in clinical settings, e.g., angiographic perfusion, is made possible, facilitating future clinical studies.</p>

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Real-time planar angiographic cerebral perfusion imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

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  • Suam Kim,
  • Andrea Kronfeld,
  • Sebastian R. Reder,
  • Henry Krumb,
  • Anirban Mukhopadhyay,
  • Marianne Hahn,
  • Timo Uphaus,
  • Marc A. Brockmann,
  • Ahmed Othman

摘要

Purpose

Digital Subtraction Angiography (DSA) is an X-ray-based imaging modality intimately related to minimally invasive procedures in interventional radiology, cardiology, vascular and neurologic surgery. Emulating tomographic methods like 3D vessel reconstruction and flat-panel detector CT perfusion imaging increases its diagnostic utility. This study demonstrates a hardware and software setup expanding DSA capability to functional perfusion imaging by assessing planar cerebral perfusion at runtime.

Methods

The setup uses an HDMI video splitter and frame-grabber for duplicating the video output of the intervention suite to an arbitrary machine at the time of image acquisition. A selection of methods, including numerical approximation and neural network inference of fitted curve features, is applied to create perfusion parameter maps and compare their respective completion times over a set of angiographic runs.

Results

We identified two distinct perfusion estimation methods able to yield results within 1–2 s, signal deconvolution using single-value decomposition (SVD) and numerical curve feature estimation, as well as a variation on conventional curve fitting that massively shortened calculation times from five minutes to clinically feasible 30 s.

Conclusion

Directly accessing image data outside of the angiography suite enables real-time angiographic cerebral perfusion evaluation in the clinical workflow. This way, on-the-fly analysis of angiograms in clinical settings, e.g., angiographic perfusion, is made possible, facilitating future clinical studies.