Evidence map›Paper›PMID 40893489›Full record

ArticleQuantitative imaging in medicine and surgery2025

Fractional flow reserve calculation optimized by myocardial computed tomography perfusion information.

Peiming Qin, Yan Yi, Cheng Xu, Limiao Zou, Fenggang Jia, Jian Guo, Ming Wang, Yan Zhang, Ziquan Wang, Pei Dong and 3 more

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

Corrections and comments

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

13 authors.

Peiming Qin *Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China.
Yan Yi *Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Cheng XuDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Limiao ZouDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Fenggang JiaShanghai United Imaging Healthcare Co., Ltd., Shanghai, China.
Jian GuoShanghai United Imaging Healthcare Co., Ltd., Shanghai, China.
Ming WangDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yan ZhangBeijing United Imaging Research Institute of Intelligent Imaging, Beijing, China.
Ziquan WangBeijing United Imaging Research Institute of Intelligent Imaging, Beijing, China.
Pei DongBeijing United Imaging Intelligence, Beijing, China.
Dijia WuShanghai United Imaging Intelligence, Shanghai, China.
Xiaodong Wang *Shanghai United Imaging Healthcare Co., Ltd., Shanghai, China.
Yining Wang *Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Current computed tomography angiography-derived fractional flow reserve (CT-FFR) diagnosis leaves room for improvement in diagnosing coronary heart disease. In this study, the computed fluid dynamics boundary condition optimization method was used to calculate CT-FFR aiming to improve the diagnostic accuracy of CT-FFR for coronary heart disease. Methods: The two enhancement approaches are as follows: (A) inlet flow optimization, which involves determining the total coronary inlet flow rate by summing the myocardial blood flow (MBF) across the entire left ventricular myocardium; and (B) inlet & outlet flow optimization: building upon method A, where the outlet flow of coronary artery branches is calculated through blood supply area analysis. Results: A total of 100 fractional flow reserve pressure guide wire measurement sites from 47 cases were used to evaluate the above two methods comparing with the traditional computed fluid dynamics method without computed tomography perfusion (CTP) images. In traditional method, the accuracy was 88%, the sensitivity was 91.4% (95% confidence interval: 75.8-97.7%), and the specificity was 86.2% (95% confidence interval: 74.8-93.1%). In Method A, the accuracy improved by 5% (93%), the sensitivity remained unchanged (91.4%, 95% confidence interval: 75.8-97.7%), and the specificity increased by 7.6% (93.8%, 95% confidence interval: 84.2-98%). In Method B, the accuracy increased by 6% (94%), the sensitivity increased to 100% (95% confidence interval: 87.7-100%), and the specificity increased by 4.8% (94%, 95% confidence interval: 80.3-96.2%). Conclusions: The computed fluid dynamics calculation, guided by MBF values from stress CTP imaging, helps enhance the consistency between CT-FFR calculation and invasive fractional flow reserve measurements.

Indexed as

blood flowcomputational fluid dynamics (CFD)computed tomography perfusion (CTP)Coronary arteryfractional flow reserve (FFR)

Identifiers

PMID40893489
PMCPMC12397671

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.