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  • Oral presentation
  • Open Access

Left ventricular function, aortic velocity, and late gadolinium enhancement assessed by real-time and single shot CMR is comparable to breath-held segmented imaging: a prospective study

  • 1,
  • 1,
  • 5,
  • 2,
  • 4,
  • 4,
  • 1 and
  • 3
Journal of Cardiovascular Magnetic Resonance201315 (Suppl 1) :O51

https://doi.org/10.1186/1532-429X-15-S1-O51

  • Published:

Keywords

  • Late Gadolinium Enhancement
  • Velocity Mapping
  • Concordance Correlation
  • Maximum Peak Velocity
  • Aortic Velocity

Background

The typical CMR exam utilizes segmented k-space acquisitions that require repeated breath-holds, a regular cardiac rhythm, and long exam times. Widespread utilization of CMR has been hampered by prolonged exam times that limit cost-effectiveness, and limited reliability in patients with irregular rhythm and/or inability to breath-hold.

Objectives

The purpose of this study was to prospectively compare assessments of left ventricular (LV) ejection fraction (EF), aortic velocity, and late gadolinium enhancement by single-shot and real time (RT) techniques vs. traditional breath-hold segmented k-space (BH) acquisitions.

Methods

Thirteen patients referred to the CMR lab for evaluation of undiagnosed cardiomyopathy were prospectively enrolled. All patients were in regular cardiac rhythm and able to breath-hold. Standard segmented k-space images (including cine, velocity mapping (VM), and late-gadolinium enhancement (LGE)) were acquired during breath-hold; real-time images (including cine, VM, and LGE) were acquired during free-breathing. Relevant imaging parameters are listed in Table 1. Breath-held and real-time studies for LGE were blinded and scored on a segmental basis for presence or absence of enhancement by two reviewers. LVEF was calculated using Simpson's rule on short-axis BH cines, and biplane area-length methodology on long-axis RT cines. EF results were averaged across cardiac cycles when RT cine spanned more than one heartbeat. Peak velocity through the aortic valve was assessed in BH and RT images and compared. Maximum peak velocity was used when RT images spanned more than one heartbeat.
Table 1

Imaging parameters for real time and breath held imaging

 

Typical imaging parameters

 

Cine

Velocity mapping

Late gadolinium enhancement

 

BH

RT

BH

RT

BH

RT

Sequence Type

SSFP

SSFP

GRE

GRE-EPI

IR-GRE

IR-SSFP

TR/TE ms

2.9/1.2

2.2/1.0

5.3/2.1

10.0/7.0

8.0/4.2

2.8/1.1

Matrix

256X256

108X160

128X192

84X128

160X192

100X192

Slice Thickness (mm)

8

10

6

10

8

8

Temporal Resolution (ms)

40

60

53

40

200

180

Acceleration Rate

R2

R4

R2

R3

R1

R2

Bandwidth (Hz/pixel)

930

1488

401

2790

130

1184

Breath-hold Time (RR)

11

-

16

-

12

-

Results

Example images from one patient are shown in Figure 1. There was good to excellent agreement between real-time imaging and breath-held imaging for all assessed parameters: the kappa statistic for agreement for LGE was 0.73 for reviewer 1 (n=204) and 0.64 for reviewer 2 (n=202). Quantitative assessment of ejection fraction and peak aortic velocity revealed excellent concordance correlations of 0.89 and 0.91 respectively between breath-held and real-time acquisitions.
Figure 1
Figure 1

Representative example of cine, LGE and velocity breath-held and real-time imaging : arrows show areas of post-infract sub-endocardial scarring seen on real-time and breath-held images that had a corresponding lateral wall motion abnormality on both real-time and breath-held cine images.

Conclusions

Prospective, real-time data acquisitions of left ventricular ejection fraction, late-Gadolinium enhancement, and quantitative velocity mapping reveal comparable, reliable, interpretable data compared to traditional breath-held CMR methodologies. These results augur well for improved CMR throughput and reliability in the future.

Funding

Grants from the National Institutes of Health (R01 HL102450) and Siemens Healthcare.

Authors’ Affiliations

(1)
Department of Internal Medicine, Department of Cardiovascular Medicine, The Ohio State University Medical Center, Columbus, OH, USA
(2)
Siemens Healthcare, Columbus, OH, USA
(3)
Internal Medicine and Radiology, The Ohio State University, Columbus, OH, USA
(4)
Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
(5)
Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, USA

Copyright

© Aneja et al; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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