Evidence map›Paper›PMID 39797882›Full record

ArticleJACC. Cardiovascular imaging2025

Detecting Hemorrhagic Myocardial Infarction With 3.0-T CMR: Insights Into Spatial Manifestation, Time-Dependence, and Optimal Acquisitions.

Yinyin Chen, Hang Jin, Xingming Guan, Hsin-Jung Yang, Xinheng Zhang, Zhenhui Chen, Shing Fai Chan, Dhirendra Singh, Nithya Jambunathan, Khalid Youssef and 7 more

Abstract read
In one paragraph

Article in JACC. Cardiovascular imaging, 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
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1 · What the graph read from it

What it found

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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

17 authors.

Yinyin ChenDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Medical Imaging, Shanghai Medical School, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China.
Hang JinDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Medical Imaging, Shanghai Medical School, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China.
Xingming GuanDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Hsin-Jung YangBiomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Xinheng ZhangDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA; Department of Bioengineering, University of California, Los Angeles, California, USA.
Zhenhui ChenDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Shing Fai ChanDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Dhirendra SinghDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Nithya JambunathanDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Khalid YoussefDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Keyur P VoraDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Gabriel GruionuDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Sanjana K DharmakumarDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Glen SchmeisserDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Richard TangDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA.
Mengsu ZengDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Medical Imaging, Shanghai Medical School, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China.
Rohan DharmakumarDepartment of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA. Electronic address: rdkumar@iu.edu.

Funding

Developing a MRI-guided Disease-Modifying Therapy for Post Infarction Chronic Heart FailureR01HL133407 · NHLBI · INDIANA UNIVERSITY INDIANAPOLIS · PI Rohan Dharmakumar · 2017 to 2026
$4.9M
An Accurate Non-Contrast-Enhanced Cardiac MRI Method for Imaging Chronic Myocardial Infarctions: Technical Developments to Rapid Clinical ValidationR01HL136578 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI LI, DEBIAO · 2017 to 2020
$3.4M
Mechanistic Insights to A Translatable Therapy for Acute Reperfused Hemorrhagic Myocardial InfarctionsR01HL147133 · NHLBI · INDIANA UNIVERSITY INDIANAPOLIS · PI DHARMAKUMAR, ROHAN, FRANCIS, JOSEPH · 2020 to 2023
$3.2M
Critical Sorting Steps and Pathways in the Trafficking of Cardiac Sarcoplasmic Reticulum ProteinsR01HL169877 · NHLBI · INDIANA UNIVERSITY INDIANAPOLIS · PI Zhenhui Chen · 2023 to 2026
$2.6M
NHLBI NIH HHS R01 HL133407NHLBI NIH HHS R01 HL136578NHLBI NIH HHS R01 HL147133NHLBI NIH HHS R01 HL169877
6 · The paper itself

Abstract

backgroundHemorrhagic myocardial infarction (hMI) can rapidly diminish the benefits of reperfusion therapy and direct the heart toward chronic heart failure. T2∗ cardiac magnetic resonance (CMR) is the reference standard for detecting hMI. However, the lack of clarity around the earliest time point for detection, time-dependent changes in hemorrhage volume, and the optimal methods for detection can limit the development of strategies to manage hMI.

objectivesThe authors investigated CMR signal characteristics of hMI through time-lapse multiparametric mapping using a clinically relevant animal model and evaluated the translatability in ST-segment elevation MI patients when possible.

methodsCanines (N = 20) underwent 3.0-T CMR at baseline and various time points over the first week of reperfused MI. Time-dependent relationships between T1, T2, and T2∗ mapping of hMI, non-hMI, and remote territories were determined. Reperfused ST-segment elevation MI patients (N = 50) were studied to establish clinically feasibility.

resultsAlthough hMI was evident <1 hour after reperfusion on histopathology, it was not reliably detected with T1, T2, or T2∗ CMR. However, 24 hours to 7 days postreperfusion, hMI was detectable on T2∗ (27.0 ± 2.4 ms [baseline] vs 11.7 ± 2.8 ms [hMI]; P < 0.001), with stable volume and transmurality. In T2 maps, hMI was most visible 5 to 7 days postreperfusion with an area under the curve of 0.98 (sensitivity and specificity ≥0.95) relative to T2∗. However, this was not the case with T1 (sensitivity <0.8, across all time points).

conclusionsHMI cannot be reliably detected with T1, T2, or T2∗ on 3.0-T CMR immediately after reperfusion. However, T2∗ CMR can be used to diagnose hMI between 24 hours and 7 days postreperfusion. T2 maps at 3.0-T are a strong alternative to T2∗ maps for diagnosing hMI, provided CMR is performed 5 to 7 days postreperfusion. However, diagnosing hMI with T1 is significantly more challenging at 3.0-T compared with both T2∗ and T2.

Indexed as

HemorrhageMagnetic Resonance ImagingMagnetic Resonance Imaging, CineMyocardiumST Elevation Myocardial InfarctionAgedAnimalsDisease Models, AnimalDogsFeasibility StudiesFemaleHumansMaleMiddle AgedPredictive Value of TestsReproducibility of Results3.0-Tacute myocardial infarctionCMRintramyocardial hemorrhage

Identifiers

PMID39797882
PMCPMC12959644

What Socratic holds

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Registered trials

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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 Socratic graph.