Format

Send to

Choose Destination
J Cardiovasc Magn Reson. 2016 May 4;18(1):27. doi: 10.1186/s12968-016-0242-5.

A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data.

Author information

1
Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden.
2
Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden.
3
Laboratory of Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
4
Section for Interventional Cardiology, Department of Cardiology, Division of Cardiovascular and Pulmonary Diseases, Oslo University Hospital, Ullevaal, Oslo, Norway.
5
Aix-Marseille University, UMR 7339 CRMBM, Marseille, France.
6
Department of Radiology, La Timone University Hospital, Marseille, France.
7
Department of Cardiology, Medical University of Innsbruck, Innsbruck, Austria.
8
Department of Cardiology, Lund University, Lund, Sweden.
9
Department of Cardiology B, Oslo University Hospital Ullevål and Faculty of Medicine, University of Oslo, Oslo, Norway.
10
Department of Clinical Sciences Lund, Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden. einar.heiberg@med.lu.se.
11
Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden. einar.heiberg@med.lu.se.
12
Department of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, SE-221 85, Lund, Sweden. einar.heiberg@med.lu.se.

Abstract

BACKGROUND:

Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data.

METHODS:

The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n = 7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n = 23 IR and n = 13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n = 124 IR and n = 49 PSIR images).

RESULTS:

Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1 ± 4%LVM (R = 0.84) in IR and -2 ± 3%LVM (R = 0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0 ± 4%LVM (R = 0.94) in IR and 0 ± 5%LVM (R = 0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2 ± 6 %LVM (R = 0.81) in IR images (n = 124) and 0 ± 5%LVM (R = 0.89) in PSIR images (n = 49).

CONCLUSIONS:

The EWA algorithm was validated experimentally and in patient data with a low bias in both IR and PSIR LGE images. Thus, the use of EM and a weighted intensity as in the EWA algorithm, may serve as a clinical standard for the quantification of myocardial infarction in LGE CMR images.

CLINICAL TRIAL REGISTRATION:

CHILL-MI: NCT01379261 .

MITOCARE:

NCT01374321 .

KEYWORDS:

Automatic quantification algorithm; Expectation maximization; Experimental validation; LGE CMR; Multi-center patient data

PMID:
27145749
PMCID:
PMC4855857
DOI:
10.1186/s12968-016-0242-5
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center