Development of myocardial perfusion imaging from coronary angiography for clinical application.

Yuya Sekikawa, Yusuke Miyazaki, Takuya Sakaguchi

Abstract read

In one paragraph

Article in Radiological physics and technology, 2025. 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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0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Yuya SekikawaDepartment of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, Omuta, Fukuoka, Japan. ysekikawa@fmt.teikyo-u.ac.jp.ORCID http://orcid.org/0009-0002-0459-9420

Yusuke MiyazakiDepartment of Central Radiation, Gifu Prefectural General Medical Center, Gifu, Gifu, Japan.

Takuya SakaguchiSchool of Engineering, Institute of Science Tokyo, Meguro, Tokyo, Japan.

Funding

Teikyo University 2400007

6 · The paper itself

Abstract

This study aimed to generate myocardial perfusion images from coronary angiography (CAG) using Patlak plot analysis and evaluate their effectiveness in detecting ischemia. Data from 29 patients were analyzed. Electrocardiogram-synchronized CAG images of the left coronary artery were registered and processed for pixel-wise Patlak analysis. Image generation succeeded in 18 cases (62%) and failed in 11 due to motion artifacts caused by irregular heartbeats, table panning, or deep breathing. The resulting images clearly distinguished ischemic from normal regions. Perfusion values were significantly lower in ischemic regions compared to normal regions (p < 0.001). Despite technical challenges and variability in patient conditions, this method enabled consistent identification of perfusion deficits. Enhancing image processing increases the success rate. This approach allows ischemia assessment directly from CAG data and supports timely treatment planning, contributing to improved diagnostic precision and clinical decision-making in selected cases.

Indexed as

Coronary AngiographyMyocardial Perfusion ImagingAgedFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedCoronary angiographyIschemia diagnosisMyocardial perfusion imagingPatlak plot analysis

Identifiers

PMID40382514

DOI10.1007/s12194-025-00917-y

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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.