Evidence map›Paper›PMID 42029843›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

Real-time planar angiographic cerebral perfusion imaging.

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

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Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Suam KimDepartment of Neuroradiology, University Medical Centre Mainz, Johannes Gutenberg University, Mainz, Germany. suam.kim@unimedizin-mainz.de.ORCID http://orcid.org/0000-0001-8940-4076
Andrea KronfeldDepartment of Neuroradiology, University Medical Centre Mainz, Johannes Gutenberg University, Mainz, Germany.
Sebastian R RederDepartment of Neuroradiology, University Medical Centre Mainz, Johannes Gutenberg University, Mainz, Germany.ORCID http://orcid.org/0000-0002-4193-9976
Henry KrumbDepartment of Informatics, Technical University Darmstadt, Darmstadt, Germany.
Anirban MukhopadhyayDepartment of Informatics, Technical University Darmstadt, Darmstadt, Germany.ORCID http://orcid.org/0000-0003-0669-4018
Marianne HahnDepartment of Neurology, University Medical Centre Mainz, Johannes Gutenberg University, Mainz, Germany.ORCID http://orcid.org/0000-0002-9462-3844
Timo UphausDepartment of Neurology, University Medical Centre Mainz, Johannes Gutenberg University, Mainz, Germany.ORCID http://orcid.org/0000-0001-5526-0510
Marc A BrockmannDepartment of Neuroradiology, University Medical Centre Mainz, Johannes Gutenberg University, Mainz, Germany.
Ahmed OthmanDepartment of Neuroradiology, University Medical Centre Mainz, Johannes Gutenberg University, Mainz, Germany.ORCID http://orcid.org/0000-0002-3827-8695

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDigital 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.

methodsThe 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.

resultsWe 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.

conclusionDirectly 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.

Indexed as

Artificial intelligenceCerebral perfusionDigital subtraction angiographyStroke

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.